diff --git a/.gcloudignore b/.gcloudignore
deleted file mode 100644
index 95151e34f..000000000
--- a/.gcloudignore
+++ /dev/null
@@ -1,34 +0,0 @@
-# This file specifies files that are _not_ uploaded to Google Cloud
-
-# using gcloud. It follows the same syntax as .gitignore, with the addition of
-
-# "#!include" directives (which insert the entries of the given .gitignore-style
-
-# file at that point).
-
-#
-
-# For more information, run:
-
-# $ gcloud topic gcloudignore
-
-#
-
-.gcloudignore
-
-# If you would like to upload your .git directory, .gitignore file or files
-
-# from your .gitignore file, remove the corresponding line
-
-# below:
-
-.git
-.gitignore
-
-# Node.js dependencies:
-
-node_modules/
-
-# Docusaurus generated files:
-
-.docusaurus/
diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml
index 2fcf074fb..fd049c30b 100644
--- a/.github/workflows/build_docs.yml
+++ b/.github/workflows/build_docs.yml
@@ -16,7 +16,7 @@ jobs:
- name: Set up Node.js
uses: actions/setup-node@v3
with:
- node-version: 18.0.0
+ node-version: 22.9.0
- name: Install yarn
run: npm install --global yarn
@@ -25,4 +25,4 @@ jobs:
run: yarn install
- name: Build the docs
- run: yarn docusaurus build
\ No newline at end of file
+ run: yarn build:prod
\ No newline at end of file
diff --git a/.github/workflows/dependabot.yml b/.github/workflows/dependabot.yml
new file mode 100644
index 000000000..59770b55f
--- /dev/null
+++ b/.github/workflows/dependabot.yml
@@ -0,0 +1,7 @@
+version: 2
+updates:
+ - package-ecosystem: "github-actions"
+ directory: "/"
+ schedule:
+ # Check for updates to GitHub Actions every weekday
+ interval: "daily"
\ No newline at end of file
diff --git a/.github/workflows/linkcheck.yml b/.github/workflows/linkcheck.yml
new file mode 100644
index 000000000..488e9d3a8
--- /dev/null
+++ b/.github/workflows/linkcheck.yml
@@ -0,0 +1,33 @@
+name: Lychee check
+on:
+ pull_request:
+ paths:
+ - '**.md'
+
+jobs:
+ lychee:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Get changed files
+ id: changed-files
+ uses: tj-actions/changed-files@v45
+ with:
+ files: "**.md"
+
+ - name: Link Checker
+ id: lychee
+ uses: lycheeverse/lychee-action@v1.10.0
+ with:
+ token: ${{ secrets.GITHUB_TOKEN }}
+ args: --scheme https --scheme http --verbose ${{ steps.changed-files.outputs.all_changed_files }}
+ env:
+ GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}}
+
+ - name: Create PR comment with link reults
+ uses: marocchino/sticky-pull-request-comment@v2
+ with:
+ header: lychee
+ recreate: true
+ path: ./lychee/out.md
\ No newline at end of file
diff --git a/.github/workflows/reviewdog.yml b/.github/workflows/reviewdog.yml
index c24173073..ebe909130 100644
--- a/.github/workflows/reviewdog.yml
+++ b/.github/workflows/reviewdog.yml
@@ -1,10 +1,19 @@
-name: reviewdog
-on: [pull_request]
+name: Vale check
+on:
+ pull_request:
+ paths:
+ - '**.md'
jobs:
vale:
- name: runner / vale
+ name: Vale check
runs-on: ubuntu-latest
steps:
- - uses: actions/checkout@v3
- - uses: errata-ai/vale-action@reviewdog
\ No newline at end of file
+ - uses: actions/checkout@v4
+ - uses: errata-ai/vale-action@reviewdog
+ env:
+ REVIEWDOG_GITHUB_API_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+ with:
+ fail_on_error: false
+ files: docs
+ reporter: github-pr-review
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 93bcb2617..d9eeb4f77 100644
--- a/.gitignore
+++ b/.gitignore
@@ -29,4 +29,6 @@ yarn-error.log*
#wandb
wandb/
translate_dev/
-chatgpt*
\ No newline at end of file
+chatgpt*yar
+package-lock.json
+.vale.ini.swp
diff --git a/.vale.ini b/.vale.ini
index 7097a5625..fade15c66 100644
--- a/.vale.ini
+++ b/.vale.ini
@@ -21,4 +21,7 @@ Packages = Google, Readability
[*.md]
BasedOnStyles = Vale, Google
-Google.Contractions = No
\ No newline at end of file
+Google.Contractions = No
+Google.Semicolons = No
+Google.Parens = No
+Google.Acronyms = No
\ No newline at end of file
diff --git a/.vale.ini.swp b/.vale.ini.swp
deleted file mode 100644
index 518c3f9d0..000000000
Binary files a/.vale.ini.swp and /dev/null differ
diff --git a/app-beta.yaml b/app-beta.yaml
deleted file mode 100644
index 7003896b2..000000000
--- a/app-beta.yaml
+++ /dev/null
@@ -1,3 +0,0 @@
-service: beta
-runtime: nodejs18
-entrypoint: yarn serve
\ No newline at end of file
diff --git a/app.yaml b/app.yaml
deleted file mode 100644
index 9186f7533..000000000
--- a/app.yaml
+++ /dev/null
@@ -1,3 +0,0 @@
-service: default
-runtime: nodejs18
-entrypoint: yarn serve
\ No newline at end of file
diff --git a/data.json b/data.json
index 7946979dc..f75e2d1ba 100644
--- a/data.json
+++ b/data.json
@@ -393,6 +393,30 @@
"from": "/guides/hosting/intro",
"to": "/guides/hosting"
},
+ {
+ "from": "/guides/self-hosted",
+ "to": "/guides/hosting/hosting-options/self-managed/"
+ },
+ {
+ "from": "/guides/self-hosted/local",
+ "to": "/guides/hosting/hosting-options/self-managed/"
+ },
+ {
+ "from": "/guides/self-hosted/local-common-questions",
+ "to": "/guides/hosting"
+ },
+ {
+ "from": "/guides/self-hosted/setup/private-cloud",
+ "to": "/guides/hosting/hosting-options/dedicated_cloud",
+ },
+ {
+ "from": "/self-hosted/setup/on-premise-baremetal",
+ "to": "/guides/hosting/hosting-options/self-managed",
+ },
+ {
+ "from": "/guides/self-hosted/setup/configuration",
+ "to": "/guides/hosting",
+ },
{
"from": "/guides/hosting/secure-storage-connector",
"to": "/guides/hosting/data-security/secure-storage-connector"
diff --git a/dispatch.yaml b/dispatch.yaml
deleted file mode 100644
index 0a1aae2ff..000000000
--- a/dispatch.yaml
+++ /dev/null
@@ -1,25 +0,0 @@
-dispatch:
- # # Route requests to
- - url: "docs.wandb.ai/ko*"
- service: ko
-
-# Route requests to
- - url: "docs.wandb.ai/ja*"
- service: ja
-
- # Route requests to docs.wandb.ai to the default service
- - url: "docs.wandb.ai/*"
- service: default
-
- # Route requests to docs-beta.wandb.ai to the ko beta service
- - url: "docs-beta.wandb.ai/ko*"
- service: beta-ko
-
- # Route requests to docs-beta.wandb.ai to the ko beta service
- - url: "docs-beta.wandb.ai/ja*"
- service: beta-ja
-
- # Route requests to docs-beta.wandb.ai to the beta service
- - url: "docs-beta.wandb.ai/*"
- service: beta
-
diff --git a/docs/guides/app/features/anon.md b/docs/guides/app/features/anon.md
index 08b938ca2..d0e123a10 100644
--- a/docs/guides/app/features/anon.md
+++ b/docs/guides/app/features/anon.md
@@ -1,10 +1,9 @@
---
description: Log and visualize data without a W&B account
displayed_sidebar: default
+title: Anonymous mode
---
-# Anonymous mode
-
Are you publishing code that you want anyone to be able to run easily? Use anonymous mode to let someone run your code, see a W&B dashboard, and visualize results without needing to create a W&B account first.
Allow results to be logged in anonymous mode with:
@@ -54,7 +53,7 @@ If you set `anonymous="allow"` in your script, W&B checks to make sure there's n
* **No persistent data**: Runs are only saved for 7 days in an anonymous account. You can claim anonymous run data by saving it to a real account.
-![](@site/static/images/app_ui/anon_mode_no_data.png)
+![](/images/app_ui/anon_mode_no_data.png)
* **No artifact logging**: Runs print a warning on the command line that you can't log an artifact to an anonymous run:
```bash
diff --git a/docs/guides/app/features/custom-charts/intro.md b/docs/guides/app/features/custom-charts/intro.md
index 0f4583e6f..8eb1ebd67 100644
--- a/docs/guides/app/features/custom-charts/intro.md
+++ b/docs/guides/app/features/custom-charts/intro.md
@@ -1,19 +1,17 @@
---
slug: /guides/app/features/custom-charts
displayed_sidebar: default
+title: Custom charts
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Custom Charts
-
Use **Custom Charts** to create charts that aren't possible right now in the default UI. Log arbitrary tables of data and visualize them exactly how you want. Control details of fonts, colors, and tooltips with the power of [Vega](https://vega.github.io/vega/).
-* **What's possible**: Read the[ launch announcement →](https://wandb.ai/wandb/posts/reports/Announcing-the-W-B-Machine-Learning-Visualization-IDE--VmlldzoyNjk3Nzg)
-* **Code**: Try a live example in a[ hosted notebook →](https://tiny.cc/custom-charts)
-* **Video**: Watch a quick [walkthrough video →](https://www.youtube.com/watch?v=3-N9OV6bkSM)
-* **Example**: Quick Keras and Sklearn [demo notebook →](https://colab.research.google.com/drive/1g-gNGokPWM2Qbc8p1Gofud0\_5AoZdoSD?usp=sharing)
+* **What's possible**: Read the[ launch announcement](https://wandb.ai/wandb/posts/reports/Announcing-the-W-B-Machine-Learning-Visualization-IDE--VmlldzoyNjk3Nzg)
+* **Code**: Try a live example in a[ hosted notebook](https://tiny.cc/custom-charts)
+* **Video**: Watch a quick [walkthrough video](https://www.youtube.com/watch?v=3-N9OV6bkSM)
+* **Example**: Quick Keras and Sklearn [demo notebook](https://colab.research.google.com/drive/1g-gNGokPWM2Qbc8p1Gofud0_5AoZdoSD?usp=sharing)
![Supported charts from vega.github.io/vega](/images/app_ui/supported_charts.png)
@@ -63,9 +61,9 @@ You can use this to log curves on any two dimensions. Note that if you're plotti
![](/images/app_ui/line_plot.png)
-[See in the app →](https://wandb.ai/wandb/plots/reports/Custom-Line-Plots--VmlldzoyNjk5NTA)
+[See in the app](https://wandb.ai/wandb/plots/reports/Custom-Line-Plots--VmlldzoyNjk5NTA)
-[Run the code →](https://tiny.cc/custom-charts)
+[Run the code](https://tiny.cc/custom-charts)
@@ -84,9 +82,9 @@ You can use this to log scatter points on any two dimensions. Note that if you'r
![](/images/app_ui/demo_scatter_plot.png)
-[See in the app →](https://wandb.ai/wandb/plots/reports/Custom-Scatter-Plots--VmlldzoyNjk5NDQ)
+[See in the app](https://wandb.ai/wandb/plots/reports/Custom-Scatter-Plots--VmlldzoyNjk5NDQ)
-[Run the code →](https://tiny.cc/custom-charts)
+[Run the code](https://tiny.cc/custom-charts)
@@ -109,11 +107,11 @@ wandb.log(
You can use this to log arbitrary bar charts. Note that the number of labels and values in the lists must match exactly (i.e. each data point must have both).
-![](@site/static/images/app_ui/line_plot_bar_chart.png)
+![](/images/app_ui/line_plot_bar_chart.png)
-[See in the app →](https://wandb.ai/wandb/plots/reports/Custom-Bar-Charts--VmlldzoyNzExNzk)
+[See in the app](https://wandb.ai/wandb/plots/reports/Custom-Bar-Charts--VmlldzoyNzExNzk)
-[Run the code →](https://tiny.cc/custom-charts)
+[Run the code](https://tiny.cc/custom-charts)
@@ -132,16 +130,16 @@ You can use this to log arbitrary histograms. Note that `data` is a list of list
![](/images/app_ui/demo_custom_chart_histogram.png)
-[See in the app →](https://wandb.ai/wandb/plots/reports/Custom-Histograms--VmlldzoyNzE0NzM)
+[See in the app](https://wandb.ai/wandb/plots/reports/Custom-Histograms--VmlldzoyNzE0NzM)
-[Run the code →](https://tiny.cc/custom-charts)
+[Run the code](https://tiny.cc/custom-charts)
`wandb.plot.pr_curve()`
-Create a [Precision-Recall curve](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision\_recall\_curve.html#sklearn.metrics.precision\_recall\_curve) in one line:
+Create a [Precision-Recall curve](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve) in one line:
```python
plot = wandb.plot.pr_curve(ground_truth, predictions, labels=None, classes_to_plot=None)
@@ -159,16 +157,16 @@ You can log this whenever your code has access to:
![](/images/app_ui/demo_average_precision_lines.png)
-[See in the app →](https://wandb.ai/wandb/plots/reports/Plot-Precision-Recall-Curves--VmlldzoyNjk1ODY)
+[See in the app](https://wandb.ai/wandb/plots/reports/Plot-Precision-Recall-Curves--VmlldzoyNjk1ODY)
-[Run the code →](https://colab.research.google.com/drive/1mS8ogA3LcZWOXchfJoMrboW3opY1A8BY?usp=sharing)
+[Run the code](https://colab.research.google.com/drive/1mS8ogA3LcZWOXchfJoMrboW3opY1A8BY?usp=sharing)
`wandb.plot.roc_curve()`
-Create an [ROC curve](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc\_curve.html#sklearn.metrics.roc\_curve) in one line:
+Create an [ROC curve](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve) in one line:
```python
plot = wandb.plot.roc_curve(
@@ -187,9 +185,9 @@ You can log this whenever your code has access to:
![](/images/app_ui/demo_custom_chart_roc_curve.png)
-[See in the app →](https://wandb.ai/wandb/plots/reports/Plot-ROC-Curves--VmlldzoyNjk3MDE)
+[See in the app](https://wandb.ai/wandb/plots/reports/Plot-ROC-Curves--VmlldzoyNjk3MDE)
-[Run the code →](https://colab.research.google.com/drive/1\_RMppCqsA8XInV\_jhJz32NCZG6Z5t1RO?usp=sharing)
+[Run the code](https://colab.research.google.com/drive/1_RMppCqsA8XInV_jhJz32NCZG6Z5t1RO?usp=sharing)
@@ -214,7 +212,7 @@ my_custom_chart = wandb.plot_table(
)
```
-[Run the code →](https://tiny.cc/custom-charts)
+[Run the code](https://tiny.cc/custom-charts)
![](/images/app_ui/custom_presets.png)
@@ -230,9 +228,9 @@ Here are the data types you can log from your script and use in a custom chart:
### How to log a custom table
-Use `wandb.Table()` to log your data as a 2D array. Typically each row of this table represents one data point, and each column denotes the relevant fields/dimensions for each data point which you'd like to plot. As you configure a custom panel, the whole table will be accessible via the named key passed to `wandb.log()`("custom\_data\_table" below), and the individual fields will be accessible via the column names ("x", "y", and "z"). You can log tables at multiple time steps throughout your experiment. The maximum size of each table is 10,000 rows.
+Use `wandb.Table()` to log your data as a 2D array. Typically each row of this table represents one data point, and each column denotes the relevant fields/dimensions for each data point which you'd like to plot. As you configure a custom panel, the whole table will be accessible via the named key passed to `wandb.log()`("custom_data_table" below), and the individual fields will be accessible via the column names ("x", "y", and "z"). You can log tables at multiple time steps throughout your experiment. The maximum size of each table is 10,000 rows.
-[Try it in a Google Colab →](https://tiny.cc/custom-charts)
+[Try it in a Google Colab](https://tiny.cc/custom-charts)
```python
# Logging a custom table of data
diff --git a/docs/guides/app/features/custom-charts/walkthrough.md b/docs/guides/app/features/custom-charts/walkthrough.md
index f65a01542..bc6f46e93 100644
--- a/docs/guides/app/features/custom-charts/walkthrough.md
+++ b/docs/guides/app/features/custom-charts/walkthrough.md
@@ -1,11 +1,10 @@
---
description: Tutorial of using the custom charts feature in the W&B UI
displayed_sidebar: default
+title: "Tutorial: Use custom charts"
---
-# Custom Charts Walkthrough
-
-To go beyond the built-in charts in W&B, use the new **Custom Charts** feature to control the details of exactly what data you're loading in to a panel and how you visualize that data.
+Use custom charts to control the data you're loading in to a panel and its visualization.
**Overview**
@@ -44,15 +43,15 @@ Now that the query is loading in these columns, they're available as options to
![Pulling in columns from the query results to set Vega fields](/images/app_ui/set_vega_fields.png)
-* **x-axis:** runSets\_historyTable\_r (recall)
-* **y-axis:** runSets\_historyTable\_p (precision)
-* **color:** runSets\_historyTable\_c (class label)
+* **x-axis:** runSets_historyTable_r (recall)
+* **y-axis:** runSets_historyTable_p (precision)
+* **color:** runSets_historyTable_c (class label)
## 3. Customize the chart
Now that looks pretty good, but I'd like to switch from a scatter plot to a line plot. Click **Edit** to change the Vega spec for this built in chart. Follow along in [this workspace](https://app.wandb.ai/demo-team/custom-charts).
-![](https://paper-attachments.dropbox.com/s\_5FCA7E5A968820ADD0CD5402B4B0F71ED90882B3AC586103C1A96BF845A0EAC7\_1597442115525\_Screen+Shot+2020-08-14+at+2.52.24+PM.png)
+![](/images/general/custom-charts-1.png)
I updated the Vega spec to customize the visualization:
@@ -64,7 +63,7 @@ I updated the Vega spec to customize the visualization:
To save this as a preset that you can use elsewhere in this project, click **Save as** at the top of the page. Here's what the result looks like, along with an ROC curve:
-![](https://paper-attachments.dropbox.com/s\_5FCA7E5A968820ADD0CD5402B4B0F71ED90882B3AC586103C1A96BF845A0EAC7\_1597442868347\_Screen+Shot+2020-08-14+at+3.07.30+PM.png)
+![](/images/general/custom-charts-2.png)
## Bonus: Composite Histograms
@@ -76,11 +75,11 @@ To create your own version of the custom composite histogram panel:
1. Create a new Custom Chart panel in your Workspace or Report (by adding a “Custom Chart” visualization). Hit the “Edit” button in the top right to modify the Vega spec starting from any built-in panel type.
2. Replace that built-in Vega spec with my [MVP code for a composite histogram in Vega](https://gist.github.com/staceysv/9bed36a2c0c2a427365991403611ce21). You can modify the main title, axis titles, input domain, and any other details directly in this Vega spec [using Vega syntax](https://vega.github.io/) (you could change the colors or even add a third histogram :)
-3. Modify the query in the right hand side to load the correct data from your wandb logs. Add the field “summaryTable” and set the corresponding “tableKey” to “class\_scores” to fetch the wandb.Table logged by your run. This will let you populate the two histogram bin sets (“red\_bins” and “blue\_bins”) via the dropdown menus with the columns of the wandb.Table logged as “class\_scores”. For my example, I chose the “animal” class prediction scores for the red bins and “plant” for the blue bins.
+3. Modify the query in the right hand side to load the correct data from your wandb logs. Add the field “summaryTable” and set the corresponding “tableKey” to “class_scores” to fetch the wandb.Table logged by your run. This will let you populate the two histogram bin sets (“red_bins” and “blue_bins”) via the dropdown menus with the columns of the wandb.Table logged as “class_scores”. For my example, I chose the “animal” class prediction scores for the red bins and “plant” for the blue bins.
4. You can keep making changes to the Vega spec and query until you’re happy with the plot you see in the preview rendering. Once you’re done, click “Save as” in the top and give your custom plot a name so you can reuse it. Then click “Apply from panel library” to finish your plot.
Here’s what my results look like from a very brief experiment: training on only 1000 examples for one epoch yields a model that’s very confident that most images are not plants and very uncertain about which images might be animals.
-![](https://paper-attachments.dropbox.com/s\_5FCA7E5A968820ADD0CD5402B4B0F71ED90882B3AC586103C1A96BF845A0EAC7\_1598376315319\_Screen+Shot+2020-08-25+at+10.24.49+AM.png)
+![](/images/general/custom-charts-3.png)
-![](https://paper-attachments.dropbox.com/s\_5FCA7E5A968820ADD0CD5402B4B0F71ED90882B3AC586103C1A96BF845A0EAC7\_1598376160845\_Screen+Shot+2020-08-25+at+10.08.11+AM.png)
+![](/images/general/custom-charts-4.png)
diff --git a/docs/guides/app/features/intro.md b/docs/guides/app/features/intro.md
index c28a3d815..b3a0bc996 100644
--- a/docs/guides/app/features/intro.md
+++ b/docs/guides/app/features/intro.md
@@ -2,10 +2,9 @@
slug: /guides/app/features
description: High level overview of the W&B App features
displayed_sidebar: default
+title: Features
---
-# Features
-
This guides provides information about W&B App features. Select on a topic to learn more:
* [Panels](./panels/intro.md) - visualizations to explore your logged data, the relationships between hyperparameters and output metrics, and dataset examples.
diff --git a/docs/guides/app/features/notes.md b/docs/guides/app/features/notes.md
index 613e2db86..23777e81d 100644
--- a/docs/guides/app/features/notes.md
+++ b/docs/guides/app/features/notes.md
@@ -3,10 +3,9 @@ description: >-
Add notes to your runs and projects, and use notes to describe your findings
in reports
displayed_sidebar: default
+title: Add notes to runs and projects
---
-# Notes
-
There are a few ways to take notes about your work in W&B.
1. Add notes to a run. These notes show up on the run page in the overview tab and in the table of runs on the project page.
@@ -22,28 +21,22 @@ You can edit the notes on a run in two places.
On the project page, expand the table. Click "Add notes..." to type notes in-line.
-![Editing notes in the table on the project page](https://downloads.intercomcdn.com/i/o/148296355/34114b47362b0378e233a440/2019-09-13+08.05.17.gif)
-
This field also shows up on the individual run page. Click the name of the run in the table to go to the run page. Click the top tab on the left side to go to the Overview tab. The field has much more room to grow here. You can type as many notes as you'd like in this space, and a preview will be shown in the runs table when you mouse over the notes field.
-![Editing notes in the overview tab on the run page](https://downloads.intercomcdn.com/i/o/148297196/afdb48d2fb59aaa0c90c3aed/2019-09-13+08.06.45.gif)
-
You can also create a report to add plots and markdown side by side. Use different sections to show different runs, and tell a story about what you worked on. These notes can be saved and shared with colleagues.
## Write descriptive notes comparing runs
Use reports to write about your findings when comparing multiple runs. Click "Add visualization" to add a markdown panel. You can arrange these panels beside panels of graphs.
-![](https://downloads.intercomcdn.com/i/o/148297552/64e5baa86a48927158d17456/2019-09-13+08.08.31.gif)
-
## Write Markdown in a new panel
Use markdown and latex equations such as:
-```
+```text
$TPR = Sensitivity = \dfrac{TP}{TP+FN}$
```
Do this by adding a panel, selecting markdown, and then entering your markdown text, tables equations and code block will automatically render when clicking outside the markdown panel.
-![](@site/static/images/app_ui/tables_panel.gif)
+![](/images/app_ui/tables_panel.gif)
diff --git a/docs/guides/app/features/organizations.md b/docs/guides/app/features/organizations.md
index eac55e747..dd56bebf7 100644
--- a/docs/guides/app/features/organizations.md
+++ b/docs/guides/app/features/organizations.md
@@ -1,11 +1,10 @@
---
description: >-
Manage organization members, subscription settings, and billing information
-displayed_sidebar: default
+displayed_sidebar: default
+title: Manage organizations
---
-# Organizations
-
Organizations allow you to manage membership across teams in your organization as well as subscription settings and billing information.
:::note
diff --git a/docs/guides/app/features/panels/bar-plot.md b/docs/guides/app/features/panels/bar-plot.md
index b7a34269e..b1d50a944 100644
--- a/docs/guides/app/features/panels/bar-plot.md
+++ b/docs/guides/app/features/panels/bar-plot.md
@@ -1,10 +1,9 @@
---
description: Visualize metrics, customize axes, and compare categorical data as bars.
displayed_sidebar: default
+title: Bar plots
---
-# Bar Plot
-
A bar plot presents categorical data with rectangular bars which can be plotted vertically or horizontally. Bar plots show up by default with **wandb.log()** when all logged values are of length one.
![Plotting Box and horizontal Bar plots in W&B](/images/app_ui/bar_plot.png)
@@ -22,4 +21,4 @@ You can also create **Box** or **Violin** Plots to combine many summary statisti
3. Add a standard 'Bar Chart' and select the metric to plot.
4. Under the 'Grouping' tab, pick 'box plot' or 'Violin', etc. to plot either of these styles.
-![Customize Bar Plots](@site/static/images/app_ui/bar_plots.gif)
+![Customize Bar Plots](/images/app_ui/bar_plots.gif)
diff --git a/docs/guides/app/features/panels/code.md b/docs/guides/app/features/panels/code.md
index 0b053208d..220329d24 100644
--- a/docs/guides/app/features/panels/code.md
+++ b/docs/guides/app/features/panels/code.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Save and diff code
---
-# Code Saving
-
By default, we only save the latest git commit hash. You can turn on more code features to compare the code between your experiments dynamically in the UI.
Starting with `wandb` version 0.8.28, we can save the code from your main training file where you call `wandb.init()`. This will get sync'd to the dashboard and show up in a tab on the run page, as well as the Code Comparer panel. Go to your [settings page](https://app.wandb.ai/settings) to enable code saving by default.
@@ -15,7 +14,7 @@ Starting with `wandb` version 0.8.28, we can save the code from your main traini
When code saving is enabled, wandb will save the code from the file that called `wandb.init()`. To save additional library code, you have two options:
* Call `wandb.run.log_code(".")` after calling `wandb.init()`
-* Pass a settings object to `wandb.init` with code\_dir set: `wandb.init(settings=wandb.Settings(code_dir="."))`
+* Pass a settings object to `wandb.init` with code_dir set: `wandb.init(settings=wandb.Settings(code_dir="."))`
This will capture all python source code files in the current directory and all subdirectories as an [artifact](../../../../ref/python/artifact.md). For more control over the types and locations of source code files that are saved, please see the [reference docs](../../../../ref/python/run.md#log_code).
diff --git a/docs/guides/app/features/panels/intro.md b/docs/guides/app/features/panels/intro.md
index 2b71ec0b4..52ef35715 100644
--- a/docs/guides/app/features/panels/intro.md
+++ b/docs/guides/app/features/panels/intro.md
@@ -1,26 +1,97 @@
---
slug: /guides/app/features/panels
displayed_sidebar: default
+title: Panels
---
-# Panels
+Use panel visualizations to explore your logged data, the relationships between hyperparameters and output metrics, and more.
-Use visualizations to explore your logged data, the relationships between hyperparameters and output metrics, and dataset examples.
+## Add a single panel
-## Common Questions
+1. Within your workspace, navigate to the section you want to add a panel to
+2. Choose the Add panel button
+3. From the dropdown, select a type of panel to add
+![](/images/app_ui/add_single_panel.gif)
+4. (Optional) If prompted, define parameters for the plot.
-### I select dimensions on a parallel coordinates plot and it disappears
-This is likely because you have dots in your config parameter names. We flatten nested parameters using dots, and we only handle 3 levels of dots in the backend. I recommend using a different character as a separator.
+:::tip Undo changes to your workspace
+Select the undo button (arrow that points left) to undo any unwanted changes.
+:::
-### Visualize max accuracy in groups
+## Add multiple panels
+Add multiple panels to your workspace at the same time. You can add up to 500 panels at a time.
-Turn on the "eye" icon next to the top run in each group to visualize the maximum accuracy on the charts
+1. Within your project workspace, choose the **Add panels** button
+2. Provide a regular expression within the search field
+3. Select the **Add all** button
+![](/images/app_ui/bulk_panels.gif)
-![](/images/app_ui/visualize_max_accuracy.png)
+:::note
+The **Add all** button appears only if a regular expression match occurs.
+:::
-### Download charts
-You can download charts by clicking the down arrow and select a format (.png, .svg, export API or export by CSV)
-![](/images/app_ui/download_charts.png)
+## Activate or deactivate auto generated panels
+
+By default, W&B generates a panel for each unique metric you [`log`](../../../../ref/python/log.md) with the Python SDK.
+
+:::info
+W&B uses the key value you specify with [`log`](../../../../ref/python/log.md) to determine whether or not to create a new panel.
+:::
+
+To activate or deactivate auto generated panels:
+
+1. Navigate to your project's workspace
+2. Select on the gear icon in the upper right hand corner
+3. A modal appears, choose **Sections**
+4. Toggle the **Panel generation** option to desired state
+![](/images/app_ui/panel_generation.png)
+
+### Check auto generated panel settings
+Each workspace indicates whether or not the workspace automatically generates panels. Next to the name of your workspace is a clipboard icon. If the icon is red, panels are not automatically generated. If the panel is green, panels are automatically created each time you log a unique metric.
+
+Example of workspace with panel auto generation off:
+![](/images/app_ui/auto_panel_off.png)
+
+Example of workspace with panel auto generation on:
+![](/images/app_ui/auto_panel_on.png)
+
+
+## Remove a panel
+
+1. Hover your mouse in the upper corner of the panel you want to remove
+2. Select the three horizontal dots (**...**) that appear
+3. From the dropdown, select **Delete**
+
+## Remove all panels
+
+1. Within your project workspace, select the three horizontal dots (**...**) next to the panel search bar
+2. Select **Clear all panels**
+
+:::note
+Clearing panels in your workspace deactivates automatic panel generation.
+:::
+
+
+
+
+
+## Organize workspace sections
+
+Group panels within sections in your organize to help organize your panels. Within a workspace section you can sort panels, rearrange panels, and rename the section name.
+
+### Rename a section
+1. Select the three horizontal dots (**...**) in the upper right corner of a section
+2. From the dropdown, select **Rename section**
+
+### Sort panels
+1. Select the three horizontal dots (**...**) in the upper right corner of a section
+2. From the dropdown, select **Sort panels A-Z**
+
+### Rearrange panels
+
+Click and drag a panel within a section to manually order your panels:
+
+![](/images/app_ui/rearrange_panels.gif)
\ No newline at end of file
diff --git a/docs/guides/app/features/panels/line-plot/intro.md b/docs/guides/app/features/panels/line-plot/intro.md
index 51c10944a..b28b424fc 100644
--- a/docs/guides/app/features/panels/line-plot/intro.md
+++ b/docs/guides/app/features/panels/line-plot/intro.md
@@ -2,10 +2,9 @@
slug: /guides/app/features/panels/line-plot
description: Visualize metrics, customize axes, and compare multiple lines on the same plot
displayed_sidebar: default
+title: Line plots
---
-# Line Plot
-
Line plots show up by default when you plot metrics over time with **wandb.log()**. Customize with chart settings to compare multiple lines on the same plot, calculate custom axes, and rename labels.
![](/images/app_ui/line_plot_example.png)
@@ -55,7 +54,7 @@ Pick **multiple y-axes** in the line plot settings to compare different metrics
**Expressions**
* **Y Axis Expressions**: Add calculated metrics to your graph. You can use any of the logged metrics as well as configuration values like hyperparameters to calculate custom lines.
-* **X Axis Expressions**: Rescale the x-axis to use calculated values using custom expressions. Useful variables include\*\*\_step\*\* for the default x-axis, and the syntax for referencing summary values is `${summary:value}`
+* **X Axis Expressions**: Rescale the x-axis to use calculated values using custom expressions. Useful variables include\*\*_step\*\* for the default x-axis, and the syntax for referencing summary values is `${summary:value}`
## Visualize average values on a plot
@@ -83,17 +82,12 @@ wandb.log({"test": [..., float("nan"), ...]})
Click on a run to go to the run page. Here's an [example run](https://app.wandb.ai/stacey/estuary/runs/9qha4fuu?workspace=user-carey) from Stacey's Estuary project. The auto-generated charts show single metrics.
-
-![](@site/static/images/app_ui/visualization_add.png)
+![](/images/app_ui/visualization_add.png)
Click **the plus sign** at the top right of the page, and select the **Line Plot**.
-![](https://downloads.intercomcdn.com/i/o/142936481/d0648728180887c52ab46549/image.png)
-
In the **Y variables** field, select a few metrics you'd like to compare. They'll show up together on the line graph.
-![](https://downloads.intercomcdn.com/i/o/146033909/899fc05e30795a1d7699dc82/Screen+Shot+2019-09-04+at+9.10.52+AM.png)
-
## Changing the color of the line plots
Sometimes the default color of runs is not helpful for comparison. To help overcome this, wandb provides two instances with which one can manually change the colors.
diff --git a/docs/guides/app/features/panels/line-plot/reference.md b/docs/guides/app/features/panels/line-plot/reference.md
index 9162bcca4..6eaa3e50b 100644
--- a/docs/guides/app/features/panels/line-plot/reference.md
+++ b/docs/guides/app/features/panels/line-plot/reference.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Line plot reference
---
-# Reference
-
## X-Axis
![Selecting X-Axis](/images/app_ui/reference_x_axis.png)
@@ -36,22 +35,22 @@ You can control the legend of your chart to show for any run any config value th
Example:
-${run:displayName} - ${config:dropout} will make the legend name for each run something like "royal-sweep - 0.5" where "royal-sweep" is the run name and 0.5 is the config parameter named "dropout".
+`${run:displayName} - ${config:dropout}` will make the legend name for each run something like `royal-sweep - 0.5` where `royal-sweep` is the run name and `0.5` is the config parameter named `dropout`.
You can set value inside`[[ ]]` to display point specific values in the crosshair when hovering over a chart. For example `\[\[ $x: $y ($original) ]]` would display something like "2: 3 (2.9)"
-Supported values inside \[\[ ]] are as follows:
-
-| Value | Meaning |
-| ----------- | ------------------------------------------ |
-| ${x} | X value |
-| ${y} | Y value (Including smoothing adjustment) |
-| ${original} | Y value not including smoothing adjustment |
-| ${mean} | Mean of grouped runs |
-| ${stddev} | Standard Deviation of grouped runs |
-| ${min} | Min of grouped runs |
-| ${max} | Max of grouped runs |
-| ${percent} | Percent of total (for stacked area charts) |
+Supported values inside `[[ ]]` are as follows:
+
+| Value | Meaning |
+| ------------ | ------------------------------------------ |
+| `${x}` | X value |
+| `${y}` | Y value (Including smoothing adjustment) |
+| `${original}` | Y value not including smoothing adjustment |
+| `${mean}` | Mean of grouped runs |
+| `${stddev}` | Standard Deviation of grouped runs |
+| `${min}` | Min of grouped runs |
+| `${max}` | Max of grouped runs |
+| `${percent}` | Percent of total (for stacked area charts) |
## Grouping
diff --git a/docs/guides/app/features/panels/line-plot/sampling.md b/docs/guides/app/features/panels/line-plot/sampling.md
index 058862894..0468039e4 100644
--- a/docs/guides/app/features/panels/line-plot/sampling.md
+++ b/docs/guides/app/features/panels/line-plot/sampling.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Use point aggregation
---
-# W&B Line Plots Point aggregation
-
Use point aggregation methods within W&B Line Plots to improve data visualization accuracy and performance. There are two types of point aggregation modes: [Random sampling](#random-sampling) and [Full fidelity](#full-fidelity).
:::note
diff --git a/docs/guides/app/features/panels/line-plot/smoothing.md b/docs/guides/app/features/panels/line-plot/smoothing.md
index 190cf0690..bf96cceb0 100644
--- a/docs/guides/app/features/panels/line-plot/smoothing.md
+++ b/docs/guides/app/features/panels/line-plot/smoothing.md
@@ -1,16 +1,15 @@
---
description: In line plots, use smoothing to see trends in noisy data.
displayed_sidebar: default
+title: Smooth line plots
---
-# Smoothing
-
In W&B line plots, we support three types of smoothing:
- [exponential moving average](smoothing.md#exponential-moving-average-default) (default)
- [gaussian smoothing](smoothing.md#gaussian-smoothing)
- [running average](smoothing.md#running-average)
-- [exponential moving average - Tensorboard](smoothing.md#exponential-moving-average-tensorboard) (deprecated)
+- [exponential moving average - Tensorboard](smoothing.md#exponential-moving-average-deprecated) (deprecated)
See these live in an [interactive W&B report](https://wandb.ai/carey/smoothing-example/reports/W-B-Smoothing-Features--Vmlldzo1MzY3OTc).
diff --git a/docs/guides/app/features/panels/parallel-coordinates.md b/docs/guides/app/features/panels/parallel-coordinates.md
index c8cc9ad24..92ac2f5ed 100644
--- a/docs/guides/app/features/panels/parallel-coordinates.md
+++ b/docs/guides/app/features/panels/parallel-coordinates.md
@@ -1,10 +1,9 @@
---
description: Compare results across machine learning experiments
displayed_sidebar: default
+title: Parallel coordinates
---
-# Parallel Coordinates
-
Parallel coordinates charts summarize the relationship between large numbers of hyperparameters and model metrics at a glance.
![](/images/app_ui/parallel_coordinates.gif)
diff --git a/docs/guides/app/features/panels/parameter-importance.md b/docs/guides/app/features/panels/parameter-importance.md
index 1cfdcbfc6..7ceafef9e 100644
--- a/docs/guides/app/features/panels/parameter-importance.md
+++ b/docs/guides/app/features/panels/parameter-importance.md
@@ -3,15 +3,14 @@ description: >-
Visualize the relationships between your model's hyperparameters and output
metrics
displayed_sidebar: default
+title: Parameter importance
---
-# Parameter Importance
-
This panel surfaces which of your hyperparameters were the best predictors of, and highly correlated to desirable values of your metrics.
-![](https://paper-attachments.dropbox.com/s\_B78AACEDFC4B6CE0BF245AA5C54750B01173E5A39173E03BE6F3ACF776A01267\_1578795733856\_image.png)
+![](/images/general/parameter-importance-1.png)
-**Correlation** is the linear correlation between the hyperparameter and the chosen metric (in this case val\_loss). So a high correlation means that when the hyperparameter has a higher value, the metric also has higher values and vice versa. Correlation is a great metric to look at but it can’t capture second order interactions between inputs and it can get messy to compare inputs with wildly different ranges.
+**Correlation** is the linear correlation between the hyperparameter and the chosen metric (in this case val_loss). So a high correlation means that when the hyperparameter has a higher value, the metric also has higher values and vice versa. Correlation is a great metric to look at but it can’t capture second order interactions between inputs and it can get messy to compare inputs with wildly different ranges.
Therefore we also calculate an **importance** metric where we train a random forest with the hyperparameters as inputs and the metric as the target output and report the feature importance values for the random forest.
@@ -25,13 +24,13 @@ Go to your W&B Project. If you don’t have one, you can use [this project](http
From your project page, click **Add Visualization**.
-![](https://paper-attachments.dropbox.com/s\_B78AACEDFC4B6CE0BF245AA5C54750B01173E5A39173E03BE6F3ACF776A01267\_1578795570241\_image.png)
+![](/images/general/parameter-importance-2.png)
Then choose **Parameter Importance**.
-You don’t need to write any new code, other than [integrating W&B](https://docs.wandb.com/quickstart) into your project.
+You don’t need to write any new code, other than [integrating W&B](/quickstart) into your project.
-![](https://paper-attachments.dropbox.com/s\_B78AACEDFC4B6CE0BF245AA5C54750B01173E5A39173E03BE6F3ACF776A01267\_1578795636072\_image.png)
+![](/images/general/parameter-importance-3.png)
:::info
If an empty panel appears, make sure that your runs are ungrouped
@@ -49,9 +48,9 @@ With the parameter manager, we can manually set the visible and hidden parameter
## Interpreting A Hyperparameter Importance Panel
-![](https://paper-attachments.dropbox.com/s\_B78AACEDFC4B6CE0BF245AA5C54750B01173E5A39173E03BE6F3ACF776A01267\_1578798509642\_image.png)
+![](/images/general/parameter-importance-4.png)
-This panel shows you all the parameters passed to the [wandb.config](https://docs.wandb.com/library/python/config) object in your training script. Next, it shows the feature importances and correlations of these config parameters with respect to the model metric you select (`val_loss` in this case).
+This panel shows you all the parameters passed to the [wandb.config](/guides/track/config/) object in your training script. Next, it shows the feature importances and correlations of these config parameters with respect to the model metric you select (`val_loss` in this case).
### Importance
@@ -65,7 +64,7 @@ This brings us to correlations.
### Correlations
-Correlations capture linear relationships between individual hyperparameters and metric values. They answer the question – is there a significant relationship between using a hyperparameter, say the SGD optimizer, and my val\_loss (the answer in this case is yes). Correlation values range from -1 to 1, where positive values represent positive linear correlation, negative values represent negative linear correlation and a value of 0 represents no correlation. Generally a value greater than 0.7 in either direction represents strong correlation.
+Correlations capture linear relationships between individual hyperparameters and metric values. They answer the question – is there a significant relationship between using a hyperparameter, say the SGD optimizer, and my val_loss (the answer in this case is yes). Correlation values range from -1 to 1, where positive values represent positive linear correlation, negative values represent negative linear correlation and a value of 0 represents no correlation. Generally a value greater than 0.7 in either direction represents strong correlation.
We might use this graph to further explore the values that are have a higher correlation to our metric (in this case we might pick stochastic gradient descent or adam over rmsprop or nadam) or train for more epochs.
diff --git a/docs/guides/app/features/panels/query-panel/embedding-projector.md b/docs/guides/app/features/panels/query-panel/embedding-projector.md
index 3d5ed95a4..06ec32453 100644
--- a/docs/guides/app/features/panels/query-panel/embedding-projector.md
+++ b/docs/guides/app/features/panels/query-panel/embedding-projector.md
@@ -4,17 +4,17 @@ description: >-
a 2D plane using common dimension reduction algorithms like PCA, UMAP, and
t-SNE.
displayed_sidebar: default
+title: Embed objects
---
-# Embedding Projector
-
![](/images/weave/embedding_projector.png)
[Embeddings](https://developers.google.com/machine-learning/crash-course/embeddings/video-lecture) are used to represent objects (people, images, posts, words, etc...) with a list of numbers - sometimes referred to as a _vector_. In machine learning and data science use cases, embeddings can be generated using a variety of approaches across a range of applications. This page assumes the reader is familiar with embeddings and is interested in visually analyzing them inside of W&B.
## Embedding Examples
-You can jump right into a [Live Interactive Demo Report](https://wandb.ai/timssweeney/toy\_datasets/reports/Feature-Report-W-B-Embeddings-Projector--VmlldzoxMjg2MjY4?accessToken=bo36zrgl0gref1th5nj59nrft9rc4r71s53zr2qvqlz68jwn8d8yyjdz73cqfyhq) or run the code in this report from the [Example Colab](https://colab.research.google.com/drive/1DaKL4lZVh3ETyYEM1oJ46ffjpGs8glXA#scrollTo=D--9i6-gXBm\_).
+- [Live Interactive Demo Report](https://wandb.ai/timssweeney/toy_datasets/reports/Feature-Report-W-B-Embeddings-Projector--VmlldzoxMjg2MjY4?accessToken=bo36zrgl0gref1th5nj59nrft9rc4r71s53zr2qvqlz68jwn8d8yyjdz73cqfyhq)
+- [Example Colab](https://colab.research.google.com/drive/1DaKL4lZVh3ETyYEM1oJ46ffjpGs8glXA#scrollTo=D--9i6-gXBm_).
### Hello World
@@ -42,7 +42,7 @@ After running the above code, the W&B dashboard will have a new Table containing
### Digits MNIST
-While the above example shows the basic mechanics of logging embeddings, typically you are working with many more dimensions and samples. Let's consider the MNIST Digits dataset ([UCI ML hand-written digits dataset](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits)[s](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits)) made available via [SciKit-Learn](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load\_digits.html). This dataset has 1797 records, each with 64 dimensions. The problem is a 10 class classification use case. We can convert the input data to an image for visualization as well.
+While the above example shows the basic mechanics of logging embeddings, typically you are working with many more dimensions and samples. Let's consider the MNIST Digits dataset ([UCI ML hand-written digits dataset](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits)[s](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits)) made available via [SciKit-Learn](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html). This dataset has 1797 records, each with 64 dimensions. The problem is a 10 class classification use case. We can convert the input data to an image for visualization as well.
```python
import wandb
@@ -70,7 +70,7 @@ wandb.log({"digits": df})
wandb.finish()
```
-After running the above code, again we are presented with a Table in the UI. By selecting `2D Projection` we can configure the definition of the embedding, coloring, algorithm (PCA, UMAP, t-SNE), algorithm parameters, and even overlay (in this case we show the image when hovering over a point). In this particular case, these are all "smart defaults" and you should see something very similar with a single click on `2D Projection`. ([Click here to interact](https://wandb.ai/timssweeney/embedding\_tutorial/runs/k6guxhum?workspace=user-timssweeney) with this example).
+After running the above code, again we are presented with a Table in the UI. By selecting `2D Projection` we can configure the definition of the embedding, coloring, algorithm (PCA, UMAP, t-SNE), algorithm parameters, and even overlay (in this case we show the image when hovering over a point). In this particular case, these are all "smart defaults" and you should see something very similar with a single click on `2D Projection`. ([Click here to interact](https://wandb.ai/timssweeney/embedding_tutorial/runs/k6guxhum?workspace=user-timssweeney) with this example).
![](/images/weave/embedding_projector.png)
diff --git a/docs/guides/app/features/panels/run-colors.md b/docs/guides/app/features/panels/run-colors.md
index a03129e20..a827f34d0 100644
--- a/docs/guides/app/features/panels/run-colors.md
+++ b/docs/guides/app/features/panels/run-colors.md
@@ -1,10 +1,9 @@
---
description: How to change the colors of your runs in visualizations
displayed_sidebar: default
+title: Change run colors
---
-# Run Colors
-
When a new run is created, it is assigned a default color. You can edit the color for a given run by clicking the color preview.
Colors are locally scoped. On the project page, custom colors only apply to your own workspace. In reports, custom colors for runs only apply at the section level. You can visualize the same run in different sections, and it can have a different custom color in each section.
@@ -13,6 +12,4 @@ Colors are locally scoped. On the project page, custom colors only apply to your
1. Select the Run you want to visualize
2. Click the colored dot
-3. Select a color for the graphs of your run
-
-![](https://downloads.intercomcdn.com/i/o/149202442/b430d9b1473e41644a91fae3/run+coloring.gif)
+3. Select a color for the graphs of your run
\ No newline at end of file
diff --git a/docs/guides/app/features/panels/run-comparer.md b/docs/guides/app/features/panels/run-comparer.md
index 44e48fea8..ced5fd2b2 100644
--- a/docs/guides/app/features/panels/run-comparer.md
+++ b/docs/guides/app/features/panels/run-comparer.md
@@ -1,16 +1,15 @@
---
description: Compare metrics across multiple runs
displayed_sidebar: default
+title: Compare run metrics
---
-# Run Comparer
-
Use the Run Comparer to see what metrics are different across your runs. Add a new visualization and click **Run Comparer** to try it.
-![](https://paper-attachments.dropbox.com/s_2BA455B46A7EB5D90BB456BA993340F060AB348F16A4BF63AB4BB2199F3052A2_1574210574429_demo+-+run+comparer+button.png)
+![](/images/general/run-comparer-1.png)
Use the **diff only** option to hide rows where the values are the same across runs.
-![](https://paper-attachments.dropbox.com/s_2BA455B46A7EB5D90BB456BA993340F060AB348F16A4BF63AB4BB2199F3052A2_1574210366243_demo+-+run+comparison+table.gif)
+![](/images/general/run-comparer-2.gif)
[View a live example →](http://bit.ly/wandb-comparison-table)
diff --git a/docs/guides/app/features/panels/scatter-plot.md b/docs/guides/app/features/panels/scatter-plot.md
index 754ae3c2e..00a68a5a3 100644
--- a/docs/guides/app/features/panels/scatter-plot.md
+++ b/docs/guides/app/features/panels/scatter-plot.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Scatter plots
---
-# Scatter Plot
-
Use the scatter plot to compare multiple runs and visualize how your experiments are performing. We've added some customizable features:
1. Plot a line along the min, max, and average
@@ -15,7 +14,7 @@ Use the scatter plot to compare multiple runs and visualize how your experiments
Here’s an example of validation accuracy of different models over a couple of weeks of experimentation. The tooltip is customized to include the batch size and dropout as well as the values on the axes. There’s also a line plotting the running average of validation accuracy.
[See a live example →](https://app.wandb.ai/l2k2/l2k/reports?view=carey%2FScatter%20Plot)
-![](https://paper-attachments.dropbox.com/s_9D642C56E99751C2C061E55EAAB63359266180D2F6A31D97691B25896D2271FC_1579031258748_image.png)
+![](/images/general/scatter-plots-1.png)
## Common Questions
diff --git a/docs/guides/app/features/runs-table.md b/docs/guides/app/features/runs-table.md
index 61f4efd66..2d5a82c1e 100644
--- a/docs/guides/app/features/runs-table.md
+++ b/docs/guides/app/features/runs-table.md
@@ -1,9 +1,10 @@
---
description: How to use the sidebar and table on the project page
displayed_sidebar: default
+title: Use the project page
---
-# Runs Table
+## Runs Table
On the project page, we show runs in a sidebar. Expand the sidebar to view a table of hyperparameters and summary metrics across runs.
@@ -27,7 +28,7 @@ The following steps describe how to sort the run table by a specific metric base
If regex doesn't provide you the desired results, you can make use of [tags](tags.md) to filter out the runs in Runs Table. Tags can be added either on run creation or after they're finished. Once the tags are added to a run, you can add a tag filter as shown in the gif below.
-![If regex doesn't provide you the desired results, you can make use of tags to filter out the runs in Runs Table](@site/static/images/app_ui/tags.gif)
+![If regex doesn't provide you the desired results, you can make use of tags to filter out the runs in Runs Table](/images/app_ui/tags.gif)
## Search End Time for runs
@@ -39,8 +40,6 @@ We provide a column named `End Time` that logs that last heartbeat from the clie
Would you like to make more space for the graphs on the project page? Click and drag the edge of the column header to resize the sidebar. You'll still be able to click on the eye icon to turn on and off runs on the graphs.
-![](https://downloads.intercomcdn.com/i/o/153755378/d54ae70fb8155657a87545b1/howto+-+resize+column.gif)
-
## Add sidebar columns
On the project page, we show runs in a sidebar. To show more columns:
@@ -49,10 +48,6 @@ On the project page, we show runs in a sidebar. To show more columns:
2. On a column header, click the dropdown menu to pin a column.
3. Pinned columns will be available in the sidebar when you collapse the table.
-Here's a screen capture. I expand the table, pin two columns, collapse the table, then resize the sidebar.
-
-![](https://downloads.intercomcdn.com/i/o/152951680/cf8cbc6b35e923be2551ba20/howto+-+pin+rows+in+table.gif)
-
## Bulk select runs
Delete multiple runs at once, or tag a group of runs— bulk selection makes it easier to keep the runs table organized.
diff --git a/docs/guides/app/features/storage.md b/docs/guides/app/features/storage.md
index ae8a59ba5..be3aa1d75 100644
--- a/docs/guides/app/features/storage.md
+++ b/docs/guides/app/features/storage.md
@@ -1,10 +1,9 @@
---
description: Ways to manage W&B data storage.
displayed_sidebar: default
+title: Manage storage
---
-# Storage
-
If you are approaching or exceeding your storage limit, there are multiple paths forward to manage your data. The path that's best for you will depend on your account type and your current project setup.
## Manage storage consumption
diff --git a/docs/guides/app/features/system-metrics.md b/docs/guides/app/features/system-metrics.md
index 572513499..23d4636f5 100644
--- a/docs/guides/app/features/system-metrics.md
+++ b/docs/guides/app/features/system-metrics.md
@@ -1,10 +1,9 @@
---
description: Metrics automatically logged by wandb
displayed_sidebar: default
+title: System metrics
---
-# System Metrics
-
\ No newline at end of file
diff --git a/docs/guides/core.md b/docs/guides/core.md
index eaafe84e1..bf810e92d 100644
--- a/docs/guides/core.md
+++ b/docs/guides/core.md
@@ -1,11 +1,11 @@
---
displayed_sidebar: default
+title: W&B Core
---
-# W&B Core
W&B Core is the foundational framework supporting [W&B Models](./models.md) and [W&B Weave](./weave_platform.md), and is itself supported by the [W&B Platform](./hosting/intro.md).
-![](@site/static/images/general/core.png)
+![](/images/general/core.png)
W&B Core provides capabilities across the entire ML lifecycle. With W&B Core, you can:
diff --git a/docs/guides/hosting/data-security/data-encryption.md b/docs/guides/hosting/data-security/data-encryption.md
index accb47dce..7bf9a7c85 100644
--- a/docs/guides/hosting/data-security/data-encryption.md
+++ b/docs/guides/hosting/data-security/data-encryption.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Manage encryption on Dedicated Cloud instance
---
-# Encryption on Dedicated Cloud
-
W&B uses a W&B-managed cloud-native key to encrypt the W&B-managed database and object storage in every [Dedicated Cloud](../hosting-options/dedicated_cloud.md), by using the customer-managed encryption key (CMEK) capability in each cloud. In this case, W&B acts as a `customer` of the cloud provider, while providing the W&B platform as a service to you. Using a W&B-managed key means that W&B has control over the keys that it uses to encrypt the data in each cloud, thus doubling down on its promise to provide a highly safe and secure platform to all of its customers.
W&B uses a `unique key` to encrypt the data in each customer instance, providing another layer of isolation between Dedicated Cloud tenants. The capability is available on AWS, Azure and GCP.
diff --git a/docs/guides/hosting/data-security/ip-allowlisting.md b/docs/guides/hosting/data-security/ip-allowlisting.md
index a5b7280e7..e43197005 100644
--- a/docs/guides/hosting/data-security/ip-allowlisting.md
+++ b/docs/guides/hosting/data-security/ip-allowlisting.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Manage IP allowlisting for Dedicated Cloud instance
---
-# IP allowlisting for Dedicated Cloud
-
You can restrict access to your [Dedicated Cloud](../hosting-options/dedicated_cloud.md) instance from only an authorized list of IP addresses. This applies to the access from your AI workloads to the W&B APIs and from your user browsers to the W&B app UI as well. Once you configure IP allowlisting for your Dedicated Cloud instance, any requests from other unauthorized locations are denied.
IP allowlisting is available on Dedicated Cloud instances on AWS, GCP and Azure.
diff --git a/docs/guides/hosting/data-security/presigned-urls.md b/docs/guides/hosting/data-security/presigned-urls.md
index 4bf316f05..b91031d3d 100644
--- a/docs/guides/hosting/data-security/presigned-urls.md
+++ b/docs/guides/hosting/data-security/presigned-urls.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Manage storage access
---
-# Pre-signed URLs
-
W&B uses pre-signed URLs to simplify access to blob storage from your AI workloads or user browsers. For basic information on pre-signed URLs, refer to [Pre-signed URLs for AWS S3](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-presigned-url.html), [Signed URLs for Google Cloud Storage](https://cloud.google.com/storage/docs/access-control/signed-urls) and [Shared Access Signature for Azure Blob Storage](https://learn.microsoft.com/en-us/azure/storage/common/storage-sas-overview).
When needed, AI workloads or user browser clients within your network request pre-signed URLs from the W&B Platform. W&B Platform then access the relevant blob storage to generate the pre-signed URL with required permissions, and returns it back to the client. The client then uses the pre-signed URL to access the blob storage for object upload or retrieval operations. URL expiry time for object downloads is 1 hour, and it is 24 hours for object uploads as some large objects may need more time to upload in chunks.
diff --git a/docs/guides/hosting/data-security/private-connectivity.md b/docs/guides/hosting/data-security/private-connectivity.md
index 5b0649a87..0d9dda1cc 100644
--- a/docs/guides/hosting/data-security/private-connectivity.md
+++ b/docs/guides/hosting/data-security/private-connectivity.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Manage private connectivity for Dedicated Cloud instance
---
-# Secure private connectivity for Dedicated Cloud
-
You can connect to your [Dedicated Cloud](../hosting-options/dedicated_cloud.md) instance over the cloud provider's secure private network. This applies to the access from your AI workloads to the W&B APIs and optionally from your user browsers to the W&B app UI as well. When using private connectivity, the relevant requests and responses do not transit through the public network or internet.
:::info
diff --git a/docs/guides/hosting/data-security/secure-storage-connector.md b/docs/guides/hosting/data-security/secure-storage-connector.md
index 501f6b9d6..33ec56f73 100644
--- a/docs/guides/hosting/data-security/secure-storage-connector.md
+++ b/docs/guides/hosting/data-security/secure-storage-connector.md
@@ -1,10 +1,10 @@
---
displayed_sidebar: default
+title: Bring your own bucket (BYOB)
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# BYOB (Secure storage connector)
Bring your own bucket (BYOB) allows you to store W&B artifacts and other related sensitive data in your own cloud or on-prem infrastructure. In case of [Dedicated Cloud](../hosting-options/dedicated_cloud.md) or [SaaS Cloud](../hosting-options/saas_cloud.md), data that you store in your bucket is not copied to the W&B managed infrastructure.
:::info
@@ -101,7 +101,6 @@ W&B recommends that you use a Terraform module managed by W&B to provision a sto
]}>
-
#### Provision the KMS Key
W&B requires you to provision a KMS Key which is needed to encrypt and decrypt the data on the S3 bucket. The key usage type must be `ENCRYPT_DECRYPT`. Assign the following policy to the key:
@@ -211,8 +210,8 @@ If you are using [SaaS Cloud](../hosting-options/saas_cloud.md) or [Dedicated Cl
* For [SaaS Cloud](../hosting-options/saas_cloud.md): `arn:aws:iam::725579432336:role/WandbIntegration`
* For [Dedicated Cloud](../hosting-options/dedicated_cloud.md): `arn:aws:iam::830241207209:root`
-
-
+
+ For more details, see the [AWS self-managed hosting guide](../self-managed/aws-tf.md).
@@ -258,7 +257,6 @@ If you are using [SaaS Cloud](../hosting-options/saas_cloud.md) or [Dedicated Cl
Keep a record of the bucket name. If you are using [Dedicated Cloud](../hosting-options/dedicated_cloud.md), share the bucket name with your W&B team in case of instance level BYOB. In case of team level BYOB on any deployment type, [configure the bucket while creating the team](#configure-byob-in-wb).
-
#### Provision the Azure Blob Storage
@@ -288,7 +286,6 @@ For the team level BYOB, W&B highly recommends using [this Terraform module](htt
* Storage container name
* Managed identity client id
* Azure tenant id
-
@@ -297,8 +294,8 @@ For the team level BYOB, W&B highly recommends using [this Terraform module](htt
diff --git a/docs/guides/hosting/debug.md b/docs/guides/hosting/debug.md
index 33e36ce29..7762ab435 100644
--- a/docs/guides/hosting/debug.md
+++ b/docs/guides/hosting/debug.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Create debugging bundle
---
-# Debug bundle
-
Share logs and other details with the support team to debug any issues in your W&B Server. To download the debug bundle, follow these steps:
1. Click the profile picture in top right corner.
diff --git a/docs/guides/hosting/env-vars.md b/docs/guides/hosting/env-vars.md
index cd46c158b..90ab92a91 100644
--- a/docs/guides/hosting/env-vars.md
+++ b/docs/guides/hosting/env-vars.md
@@ -1,10 +1,9 @@
---
description: How to configure the W&B Server installation
displayed_sidebar: default
+title: Configure environment variables
---
-# Environment variables
-
In addition to configuring instance level settings via the System Settings admin UI, W&B also provides a way to configure these values via code using Environment Variables. Also, refer to [advanced configuration for IAM](./iam/advanced_env_vars.md).
## Configuration as code
diff --git a/docs/guides/hosting/export-data-from-dedicated-cloud.md b/docs/guides/hosting/export-data-from-dedicated-cloud.md
index 6b0b4e9b8..03ab889fa 100644
--- a/docs/guides/hosting/export-data-from-dedicated-cloud.md
+++ b/docs/guides/hosting/export-data-from-dedicated-cloud.md
@@ -1,10 +1,9 @@
---
description: Export data from Dedicated Cloud
displayed_sidebar: default
+title: Export data from Dedicated Cloud
---
-# Export data from Dedicated Cloud
-
If you would like to export all the data managed in your Dedicated Cloud instance, you may use the W&B SDK API to extract the runs, metrics, artifacts etc. and log those to another cloud or on-premises storage using API relevant to that storage.
Refer to data export use cases at [Import and Export Data](../track/public-api-guide#export-data). Another use case would be if you are planning to end your agreement to use Dedicated Cloud, you may want to export the pertinent data before W&B terminates the instance.
@@ -16,7 +15,7 @@ Refer to the table below for data export API and pointers to relevant documentat
| Export project metadata | [Projects API](../../ref/python/public-api/api#projects) |
| Export runs in a project | [Runs API](../../ref/python/public-api/api#runs), [Export run data](../track/public-api-guide#export-run-data), [Querying multiple runs](../track/public-api-guide#querying-multiple-runs) |
| Export reports | [Reports API](../../ref/python/public-api/api#reports) |
-| Export artifacts | [Artifact API](../../ref/python/public-api/api#artifact), [Explore and traverse an artifact graph](../artifacts/explore-and-traverse-an-artifact-graph#traverse-an-artifact-programmatically), [Download and use an artifact](../artifacts/download-and-use-an-artifact#download-and-use-an-artifact-stored-on-wb) |
+| Export artifacts | [Artifact API](../../ref/python/public-api/api#artifact), [Explore and traverse an artifact graph](../artifacts/explore-and-traverse-an-artifact-graph/#use-the-api-to-track-lineage), [Download and use an artifact](../artifacts/download-and-use-an-artifact#download-and-use-an-artifact-stored-on-wb) |
:::info
You manage artifacts stored in the Dedicated Cloud with [Secure Storage Connector](./data-security/secure-storage-connector). In that case, you may not need to export the artifacts using the W&B SDK API.
diff --git a/docs/guides/hosting/hosting-options/dedicated_cloud.md b/docs/guides/hosting/hosting-options/dedicated_cloud.md
index 52509633d..186ed6bf9 100644
--- a/docs/guides/hosting/hosting-options/dedicated_cloud.md
+++ b/docs/guides/hosting/hosting-options/dedicated_cloud.md
@@ -1,5 +1,6 @@
---
displayed_sidebar: default
+title: Use Dedicated Cloud
---
# Dedicated cloud (Single-tenant SaaS)
diff --git a/docs/guides/hosting/hosting-options/dedicated_regions.md b/docs/guides/hosting/hosting-options/dedicated_regions.md
index 7f73f7ccb..42d66f796 100644
--- a/docs/guides/hosting/hosting-options/dedicated_regions.md
+++ b/docs/guides/hosting/hosting-options/dedicated_regions.md
@@ -1,10 +1,8 @@
---
-title: Dedicated Cloud Regions
displayed_sidebar: default
+title: Supported Dedicated Cloud regions
---
-# Dedicated Cloud Regions
-
AWS, GCP, and Azure support cloud computing services in multiple locations worldwide. Global regions help ensure that you satisfy requirements related to data residency & compliance, latency, cost efficiency and more. W&B supports many of the available global regions for Dedicated Cloud.
:::info
diff --git a/docs/guides/hosting/hosting-options/saas_cloud.md b/docs/guides/hosting/hosting-options/saas_cloud.md
index 31738d2b5..f444dcfeb 100644
--- a/docs/guides/hosting/hosting-options/saas_cloud.md
+++ b/docs/guides/hosting/hosting-options/saas_cloud.md
@@ -1,10 +1,8 @@
---
-title: W&B Multi-tenant Cloud
displayed_sidebar: default
+title: Use W&B Multi-tenant Cloud
---
-# Multi-tenant Cloud
-
W&B Multi-tenant Cloud is a fully-managed platform deployed in W&B's Google Cloud Platform (GCP) account in [GPC's North America regions](https://cloud.google.com/compute/docs/regions-zones). W&B Multi-tenant Cloud utilizes autoscaling in GCP to ensure that the platform scales appropriately based on increases or decreases in traffic.
![](/images/hosting/saas_cloud_arch.png)
diff --git a/docs/guides/hosting/hosting-options/self-managed.md b/docs/guides/hosting/hosting-options/self-managed.md
index 2d38cdf4e..fa84d80d4 100644
--- a/docs/guides/hosting/hosting-options/self-managed.md
+++ b/docs/guides/hosting/hosting-options/self-managed.md
@@ -1,16 +1,14 @@
---
-title: Self managed
+title: Use self-managed infrastructure
description: Deploying W&B in production
displayed_sidebar: default
---
-# Self managed
-
:::info
W&B recommends fully managed deployment options such as [W&B Multi-tenant Cloud](./saas_cloud.md) or [W&B Dedicated Cloud](./dedicated_cloud.md) deployment types. W&B fully managed services are simple and secure to use, with minimum to no configuration required.
:::
-Deploy W&B Server on your [AWS, GCP, or Azure cloud account](#deploy-wb-server-within-self-managed-cloud-accounts) or within your [on-premises infrastructure](#deploy-wb-server-in-on-premises-infrastructure).
+Deploy W&B Server on your [AWS, GCP, or Azure cloud account](#deploy-wb-server-within-self-managed-cloud-accounts) or within your [on-premises infrastructure](#deploy-wb-server-in-on-prem-infrastructure).
Your IT/DevOps/MLOps team is responsible for provisioning your deployment, managing upgrades, and continuously maintaining your self managed W&B Server instance.
diff --git a/docs/guides/hosting/iam/advanced_env_vars.md b/docs/guides/hosting/iam/advanced_env_vars.md
index 1a37de534..a22792783 100644
--- a/docs/guides/hosting/iam/advanced_env_vars.md
+++ b/docs/guides/hosting/iam/advanced_env_vars.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Advanced IAM configuration
---
-# Advanced configuration
-
In addition to basic [environment variables](../env-vars.md), you can use environment variables to configure IAM options for your [Dedicated Cloud](../hosting-options/dedicated_cloud.md) or [Self-managed](../hosting-options/self-managed.md) instance.
Choose any of the following environment variables for your instance depending on your IAM needs.
diff --git a/docs/guides/hosting/iam/automate_iam.md b/docs/guides/hosting/iam/automate_iam.md
index 7351a974b..ecd904da6 100644
--- a/docs/guides/hosting/iam/automate_iam.md
+++ b/docs/guides/hosting/iam/automate_iam.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Automate user and team management
---
-# Automate user and team management
-
## SCIM API
Use SCIM API to manage users, and the teams they belong to, in an efficient and repeatable manner. You can also use the SCIM API to manage custom roles or assign roles to users in your W&B organization. Role endpoints are not part of the official SCIM schema. W&B adds role endpoints to support automated management of custom roles.
@@ -22,7 +21,7 @@ There are broadly three categories of SCIM API - **User**, **Group**, and **Role
:::info
Deactivate a user within a W&B organization with the `DELETE User` endpoint. Deactivated users can no longer sign in. However, deactivated users still appears in the organization's user list.
-To fully remove a deactivated user from the user list, you must [remove the user from the organization](#remove-a-user).
+To fully remove a deactivated user from the user list, you must [remove the user from the organization](manage-users.md#remove-a-user).
It is possible to re-enable a deactivated user, if needed.
:::
diff --git a/docs/guides/hosting/iam/identity_federation.md b/docs/guides/hosting/iam/identity_federation.md
index 0fcd8aafd..4315aed04 100644
--- a/docs/guides/hosting/iam/identity_federation.md
+++ b/docs/guides/hosting/iam/identity_federation.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Manage identity federation with SDK
---
-# Identity federation with SDK
-
Use identity federation to sign in using your organizational credentials through W&B SDK. If your W&B organization admin has configured SSO for your organization, then you already use your organizational credentials to sign-in to the W&B app UI. In that sense, identity federation is like SSO for W&B SDK, but by using JSON Web Tokens (JWTs) directly. You can use identity federation as an alternative to API keys.
[RFC 7523](https://datatracker.ietf.org/doc/html/rfc7523) forms the underlying basis for identity federation with SDK.
diff --git a/docs/guides/hosting/iam/ldap.md b/docs/guides/hosting/iam/ldap.md
index 5610cdecb..3113d0b81 100644
--- a/docs/guides/hosting/iam/ldap.md
+++ b/docs/guides/hosting/iam/ldap.md
@@ -1,12 +1,10 @@
---
displayed_sidebar: default
+title: Configure SSO with LDAP
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# SSO using LDAP
-
Authenticate your credentials with the W&B Server LDAP server. The following guide explains how to configure the settings for W&B Server. It covers mandatory and optional configurations, as well as instructions for configuring the LDAP connection from systems settings UI. it also provides information on the different inputs of the LDAP configuration, such as the address, base distinguished name, and attributes. You can specify these attributes from the W&B App UI or using environment variables. You can setup either an anonymous bind, or bind with an administrator DN and Password.
-
The following table lists and describes required and optional LDAP configurations.
-| Environment variable | Definition | Required |
-| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | --- |
-| `ADDRESS` | This is the address of your LDAP server within the VPC that hosts W&B Server. | Yes |
-| `BASE_DN` | The root path searches start from and required for doing any queries into this directory. | Yes |
+| Environment variable | Definition | Required |
+| -------------------- | ----------------------- | -------- |
+| `ADDRESS` | This is the address of your LDAP server within the VPC that hosts W&B Server. | Yes |
+| `BASE_DN` | The root path searches start from and required for doing any queries into this directory. | Yes |
| `BIND_DN` | Path of the administrative user registered in the LDAP server. This is required if the LDAP server does not support unauthenticated binding. If specified, W&B Server connects to the LDAP server as this user. Otherwise, W&B Server connects using anonymous binding. | No |
-| `BIND_PW` | The password for administrative user, this is used to authenticate the binding. If left blank, W&B Server connects using anonymous binding. | No | |
-| `ATTRIBUTES` | Provide an email and group ID attribute names as comma separated string values. | Yes |
-| `TLS_ENABLE` | Enable TLS. | No |
-| `GROUP_ALLOW_LIST` | Group allowlist. | No |
-| `LOGIN` | This tells W&B Server to use LDAP to authenticate. Set to either `True` or `False`. Optionally set this to false to test the LDAP configuration. Set this to true to start LDAP authentication. | No |
+| `BIND_PW` | The password for administrative user, this is used to authenticate the binding. If left blank, W&B Server connects using anonymous binding. | No |
+| `ATTRIBUTES` | Provide an email and group ID attribute names as comma separated string values. | Yes |
+| `TLS_ENABLE` | Enable TLS. | No |
+| `GROUP_ALLOW_LIST` | Group allowlist. | No |
+| `LOGIN` | This tells W&B Server to use LDAP to authenticate. Set to either `True` or `False`. Optionally set this to false to test the LDAP configuration. Set this to true to start LDAP authentication. | No |
diff --git a/docs/guides/hosting/iam/manage-users.md b/docs/guides/hosting/iam/manage-users.md
index bd0bf131e..cf2144b1e 100644
--- a/docs/guides/hosting/iam/manage-users.md
+++ b/docs/guides/hosting/iam/manage-users.md
@@ -1,7 +1,8 @@
---
displayed_sidebar: default
+title: Manage users
---
-# Manage users
+
Manage W&B users in your organization or team.
W&B strongly recommends and encourages that users authenticate to an organization using Single Sign-On (SSO). To learn more about how to setup SSO with W&B Server, refer to [SSO with OIDC](./sso.md) or [SSO with LDAP](./ldap.md).
diff --git a/docs/guides/hosting/iam/org_team_struct.md b/docs/guides/hosting/iam/org_team_struct.md
index 26526bf24..80e8908c9 100644
--- a/docs/guides/hosting/iam/org_team_struct.md
+++ b/docs/guides/hosting/iam/org_team_struct.md
@@ -1,8 +1,8 @@
---
displayed_sidebar: default
+title: W&B IAM structure
---
-# IAM structure
W&B Platform has three IAM scopes within W&B: [Organizations](#organization), [Teams](#team), and [Projects](#project).
## Organization
diff --git a/docs/guides/hosting/iam/restricted-projects.md b/docs/guides/hosting/iam/restricted-projects.md
index da5a13a3b..1d304e30b 100644
--- a/docs/guides/hosting/iam/restricted-projects.md
+++ b/docs/guides/hosting/iam/restricted-projects.md
@@ -1,10 +1,9 @@
---
description: Restricted Projects for collaborating on AI workflows with sensitive data
displayed_sidebar: default
+title: Manage project access
---
-# Project visibility
-
Define the scope of a W&B project to limit who can view, edit, and submit W&B runs to it. Only the owner of the project or a team admin can set or edit a project's visibility.
## Visibility scopes
diff --git a/docs/guides/hosting/iam/scim.md b/docs/guides/hosting/iam/scim.md
index 9d668cebe..6770d7688 100644
--- a/docs/guides/hosting/iam/scim.md
+++ b/docs/guides/hosting/iam/scim.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Manage users, groups, and roles with SCIM
---
-# SCIM
-
The System for Cross-domain Identity Management (SCIM) API allows instance or organization admins to manage users, groups, and custom roles in their W&B organization. SCIM groups map to W&B teams.
The SCIM API is accessible at `/scim/` and supports the `/Users` and `/Groups` endpoints with a subset of the fields found in the [RC7643 protocol](https://www.rfc-editor.org/rfc/rfc7643). It additionally includes the `/Roles` endpoints which are not part of the official SCIM schema. W&B adds the `/Roles` endpoints to support automated management of custom roles in W&B organizations.
diff --git a/docs/guides/hosting/iam/sso.md b/docs/guides/hosting/iam/sso.md
index bbd4141a8..bee2ee481 100644
--- a/docs/guides/hosting/iam/sso.md
+++ b/docs/guides/hosting/iam/sso.md
@@ -1,12 +1,10 @@
---
displayed_sidebar: default
+title: Configure SSO with OIDC
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# SSO using OIDC
-
Email [contact@wandb.com](mailto:contact@wandb.com) to configure an [Auth0](https://auth0.com) tenant for you with identity providers supported by W&B (such as SAML, Ping Federate, Active Directory, and more).
If you already use Auth0 or have an Open ID Connect compatible server, follow the instructions below to set up authorization with Open ID.
@@ -23,8 +21,6 @@ _wandb/local_ uses Open ID Connect (OIDC) for authentication. Based on your use
Select either a Single Page or Public Client application in your identity provider (IdP).
:::
-
-
@@ -160,8 +153,6 @@ Once you have everything configured you can provide the Issuer, Client ID, and A
![](/images/hosting/system_settings_select_update.png)
-
:::info
If you're unable to log in to your instance after configuring SSO, you can restart the instance with the `LOCAL_RESTORE=true` environment variable set. This will output a temporary password to the containers logs and disable SSO. Once you've resolved any issues with SSO, you must remove that environment variable to enable SSO again.
:::
-
diff --git a/docs/guides/hosting/intro.md b/docs/guides/hosting/intro.md
index 21c9d2bee..00e48be47 100644
--- a/docs/guides/hosting/intro.md
+++ b/docs/guides/hosting/intro.md
@@ -1,19 +1,20 @@
---
slug: /guides/hosting
displayed_sidebar: default
+title: W&B Platform
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# W&B Platform
-
W&B Platform is the foundational infrastructure, tooling and governance scaffolding which supports the W&B products like [Core](../core.md), [Models](../models.md) and [Weave](../weave_platform.md).
W&B Platform is available in three different deployment options:
-* [W&B Multi-tenant Cloud](#wb-saas-cloud)
+
+* [W&B Multi-tenant Cloud](#wb-multi-tenant-cloud)
* [W&B Dedicated Cloud](#wb-dedicated-cloud)
* [W&B Customer-managed](#wb-customer-managed)
+
The following responsibility matrix outlines some of the key differences between the different options:
![](/images/hosting/shared_responsibility_matrix.png)
diff --git a/docs/guides/hosting/monitoring-usage/audit-logging.md b/docs/guides/hosting/monitoring-usage/audit-logging.md
index 9a51083e6..8136e649c 100644
--- a/docs/guides/hosting/monitoring-usage/audit-logging.md
+++ b/docs/guides/hosting/monitoring-usage/audit-logging.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Track user and team activity
---
-
-# Audit logs
Use W&B Server audit logs to track user activity within your teams, and to conform to your enterprise governance requirements. The audit logs are JSON-formatted, and their access mechanism(s) depend on your W&B Server deployment type:
| W&B Server Deployment type | Audit logs access mechanism(s) |
diff --git a/docs/guides/hosting/monitoring-usage/org_dashboard.md b/docs/guides/hosting/monitoring-usage/org_dashboard.md
index 04cec1c70..00f7fb68e 100644
--- a/docs/guides/hosting/monitoring-usage/org_dashboard.md
+++ b/docs/guides/hosting/monitoring-usage/org_dashboard.md
@@ -1,7 +1,7 @@
---
displayed_sidebar: default
+title: View organization
---
-# Organization dashboard
:::important
Organization dashboard is only available with [Dedicated Cloud](../hosting-options/dedicated_cloud.md) and [Self-managed instances](../hosting-options/self-managed.md).
diff --git a/docs/guides/hosting/monitoring-usage/prometheus-logging.md b/docs/guides/hosting/monitoring-usage/prometheus-logging.md
index 38d3851bc..d2da63e86 100644
--- a/docs/guides/hosting/monitoring-usage/prometheus-logging.md
+++ b/docs/guides/hosting/monitoring-usage/prometheus-logging.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Use Prometheus monitoring
---
-# Prometheus monitoring
-
Use [Prometheus](https://prometheus.io/docs/introduction/overview/) with W&B Server. Prometheus installs are exposed as a [kubernetes ClusterIP service](https://github.com/wandb/terraform-kubernetes-wandb/blob/main/main.tf#L225).
:::important
diff --git a/docs/guides/hosting/monitoring-usage/slack-alerts.md b/docs/guides/hosting/monitoring-usage/slack-alerts.md
index 092aa79ea..c36893291 100644
--- a/docs/guides/hosting/monitoring-usage/slack-alerts.md
+++ b/docs/guides/hosting/monitoring-usage/slack-alerts.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Configure Slack alerts
---
-# Slack alerts
-
Integrate W&B Server with [Slack](https://slack.com/).
## Create the Slack application
diff --git a/docs/guides/hosting/operator-airgapped.md b/docs/guides/hosting/operator-airgapped.md
new file mode 100644
index 000000000..9f07a3c16
--- /dev/null
+++ b/docs/guides/hosting/operator-airgapped.md
@@ -0,0 +1,161 @@
+---
+description: Hosting W&B Server with Kubernetes Operator (Airgapped)
+displayed_sidebar: default
+title: "Tutorial: Deploy W&B in airgapped environment with Kubernetes"
+---
+
+## Introduction
+
+This guide provides a comprehensive and user-friendly step-by-step process for deploying the Weights & Biases (W&B) Platform in airgapped environments.
+Since airgapped environments are isolated from the internet, special configurations are required to ensure the proper deployment of W&B components using
+internal repositories for both Helm charts and container images.
+The document assume all commands will be executed in a shell console with proper access to the Kubernetes.
+Although using command line for the matter of documentation, the process also applies to any continuous deliver tooling used to deploy Kubernetes application.
+
+## Step 1: Prerequisites
+
+Before starting, make sure your environment meets the following requirements:
+
+- Kubernetes version >= 1.29
+- Helm version >= 3
+- Kubernetes Metrics installed (required for future support of Horizontal Pod Autoscaler support)
+- Access to an internal container registry with the required W&B images
+- Access to an internal Helm repository for W&B Helm charts
+
+## Step 2: Prepare Internal Container Registry
+
+Before proceeding with the deployment, you must ensure that the following container images are available in your internal container registry.
+These images are critical for the successful deployment of W&B components.
+
+```bash
+wandb/local 0.59.2
+wandb/console 2.12.2
+wandb/controller 1.13.0
+otel/opentelemetry-collector-contrib 0.97.0
+bitnami/redis 7.2.4-debian-12-r9
+quay.io/prometheus/prometheus v2.47.0
+quay.io/prometheus-operator/prometheus-config-reloader v0.67.0
+```
+
+## Step 2: Prepare Internal Helm Chart repository
+
+Along with the container images, you also must ensure that the following helm charts are available in your internal Helm Chart repository.
+
+
+- [W&B Operator](https://github.com/wandb/helm-charts/tree/main/charts/operator)
+- [Operator W&B](https://github.com/wandb/helm-charts/tree/main/charts/operator-wandb)
+
+
+The `operator` chart is used to deploy the W&B Operator (Controller Manager) while the `operator-wandb` chart will be used with the values configured in the CRD to deploy W&B Platform.
+
+## Step 3: Set Up Helm Repository
+
+Now, configure the Helm repository to pull the W&B Helm charts from your internal repository. Run the following commands to add and update the Helm repository:
+
+```bash
+helm repo add local-repo https://charts.yourdomain.com
+helm repo update
+```
+
+## Step 4: Install W&B Operator (Controller Manager)
+
+The W&B Operator (Controller Manager) is responsible for managing the W&B platform components. To install it in an airgapped environment,
+you need to configure it to use your internal container registry.
+
+To do so, uou must override the default image settings to use your internal container registry and set the key `airgapped: true` to indicate this is the deployment type you expect. Update the `values.yaml` file as shown below:
+
+```yaml
+image:
+ repository: registry.yourdomain.com/library/controller
+ tag: 1.13.3
+airgapped: true
+```
+
+All supported values can be found in the official W&B Operator [repository](https://github.com/wandb/helm-charts/blob/main/charts/operator/values.yaml).
+
+## Step 5: Configure Custom Resource Definitions (CRDs)
+
+Once the W&B Operator is installed, configure the Custom Resource Definitions (CRDs) to point to your internal Helm repository and container registry.
+This configuration ensures that all required components are deployed from your internal resources. Below is an example of how to configure the CRD.
+
+```yaml
+apiVersion: apps.wandb.com/v1
+kind: WeightsAndBiases
+metadata:
+ labels:
+ app.kubernetes.io/instance: wandb
+ app.kubernetes.io/name: weightsandbiases
+ name: wandb
+ namespace: default
+
+spec:
+ chart:
+ url: http://charts.yourdomain.com
+ name: operator-wandb
+ version: 0.18.0
+
+ values:
+ global:
+ host: https://wandb.yourdomain.com
+ license: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+ bucket:
+ accessKey: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+ secretKey: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+ name: s3.yourdomain.com
+ provider: s3
+ mysql:
+ database: wandb
+ host: mysql.home.lab
+ password: password
+ port: 3306
+ user: wandb
+
+ # Ensre it's set to use your own MySQL
+ mysql:
+ install: false
+
+ app:
+ image:
+ repository: registry.yourdomain.com/local
+ tag: 0.59.2
+
+ console:
+ image:
+ repository: registry.yourdomain.com/console
+ tag: 2.12.2
+
+ ingress:
+ annotations:
+ nginx.ingress.kubernetes.io/proxy-body-size: 64m
+ class: nginx
+
+
+```
+
+To deploy the W&B Platform, the W&B Operator will use the `operator-wandb` chart from your internal repository and use the values from your CRD to configure the helm chart.
+
+All supported values can be found in the Operator W&B [repository](https://github.com/wandb/helm-charts/blob/main/charts/operator/values.yaml).
+
+## Step 6: Deploy the W&B Platform
+
+Finally, after setting up the CRD and W&B Operator, deploy the W&B platform using the following command:
+
+```bash
+kubectl apply -f wandb.yaml
+```
+
+## Troubleshooting and FAQ
+
+Below are some frequently asked questions (FAQs) and troubleshooting tips to help you during the deployment process:
+
+**I have another ingress class, can I use it?**
+Yes, you can configure your ingress class by modifying the ingress settings in `values.yaml`.
+
+**My certificate bundle has more than one certificate.**
+Split the certificates into multiple entries in the `customCACerts` section of `values.yaml`.
+
+**I don't want the W&B Operator to apply unattended updates.**
+You can disable auto-updates by ensuring you have the latest versions and adjusting the W&B Console settings.
+
+**What if my environment has no connection to external repositories?**
+As long as the `airgapped: true` configuration is enabled, the W&B Operator will not attempt to reach public repositories and will use your internal resources.
diff --git a/docs/guides/hosting/operator.md b/docs/guides/hosting/operator.md
index 07dd31b8f..87861f64a 100644
--- a/docs/guides/hosting/operator.md
+++ b/docs/guides/hosting/operator.md
@@ -1,9 +1,10 @@
---
description: Hosting W&B Server with Kubernetes Operator
displayed_sidebar: default
+title: Run W&B Server on Kubernetes
---
-# W&B Kubernetes Operator
+## W&B Kubernetes Operator
Use the W&B Kubernetes Operator to simplify deploying, administering, troubleshooting, and scaling your W&B Server deployments on Kubernetes. You can think of the operator as a smart assistant for your W&B instance.
@@ -40,14 +41,14 @@ metadata:
The `controller-manager` installs [charts/operator-wandb](https://github.com/wandb/helm-charts/tree/main/charts/operator-wandb) based on the spec of the custom resource, release channel, and a user defined config. The configuration specification hierarchy enables maximum configuration flexibility at the user end and enables W&B to release new images, configurations, features, and Helm updates automatically.
-For a detailed description of the specification hierarchy, see [Configuration Specification Hierarchy](#configuration-specification-hierarchy) and for configuration options, see [Configuration Reference](#configuration-reference).
+For a detailed description of the specification hierarchy, see [Configuration Specification Hierarchy](#configuration-specification-hierarchy) and for configuration options, see [Configuration Reference](#configuration-reference-for-wb-operator).
## Configuration Specification Hierarchy
Configuration specifications follow a hierarchical model where higher-level specifications override lower-level ones. Here’s how it works:
- **Release Channel Values**: This base level configuration sets default values and configurations based on the release channel set by W&B for the deployment.
- **User Input Values**: Users can override the default settings provided by the Release Channel Spec through the System Console.
-- **Custom Resource Values**: The highest level of specification, which comes from the user. Any values specified here override both the User Input and Release Channel specifications. For a detailed description of the configuration options, see [Configuration Reference](#configuration-reference).
+- **Custom Resource Values**: The highest level of specification, which comes from the user. Any values specified here override both the User Input and Release Channel specifications. For a detailed description of the configuration options, see [Configuration Reference](#configuration-reference-for-wb-operator).
This hierarchical model ensures that configurations are flexible and customizable to meet varying needs while maintaining a manageable and systematic approach to upgrades and changes.
@@ -102,7 +103,7 @@ helm repo update
```shell
helm upgrade --install operator wandb/operator -n wandb-cr --create-namespace
```
-3. Configure the W&B operator custom resource to trigger the W&B Server installation. Create an operator.yaml file to customize the W&B Operator deployment, specifying your custom configuration. See [Configuration Reference](#configuration-reference) for details.
+3. Configure the W&B operator custom resource to trigger the W&B Server installation. Create an operator.yaml file to customize the W&B Operator deployment, specifying your custom configuration. See [Configuration Reference](#configuration-reference-for-wb-operator) for details.
Once you have the specification YAML created and filled with your values, run the following and the operator will apply the configuration and install the W&B Server application based on your configuration.
@@ -154,7 +155,7 @@ module "wandb" {
}
```
-Note that the configuration options are the same as described in [Configuration Reference](#configuration-reference), but that the syntax has to follow the HashiCorp Configuration Language (HCL). The W&B custom resource definition will be created by the Terraform module.
+Note that the configuration options are the same as described in [Configuration Reference](#configuration-reference-for-wb-operator), but that the syntax has to follow the HashiCorp Configuration Language (HCL). The W&B custom resource definition will be created by the Terraform module.
To see how Weights&Biases themselves use the Helm Terraform module to deploy “Dedicated Cloud” installations for customers, follow those links:
- [AWS](https://github.com/wandb/terraform-aws-wandb/blob/45e1d746f53e78e73e68f911a1f8cad5408e74b6/main.tf#L225)
@@ -165,11 +166,11 @@ To see how Weights&Biases themselves use the Helm Terraform module to deploy “
W&B provides a set of Terraform Modules for AWS, GCP and Azure. Those modules deploy entire infrastructures including Kubernetes clusters, load balancers, MySQL databases and so on as well as the W&B Server application. The W&B Kubernetes Operator is already pre-baked with those official W&B cloud-specific Terraform Modules with the following versions:
-| Terraform Registry | Source Code | Version |
-| -------------------------------------------------------------------- | ------------------------------------------------ | ------- |
-| [AWS](https://registry.terraform.io/modules/wandb/wandb/aws/latest) | https://github.com/wandb/terraform-aws-wandb | v4.0.0+ |
-| [Azure](https://github.com/wandb/terraform-azurerm-wandb) | https://github.com/wandb/terraform-azurerm-wandb | v2.0.0+ |
-| [GCP](https://github.com/wandb/terraform-google-wandb) | https://github.com/wandb/terraform-google-wandb | v2.0.0+ |
+| Terraform Registry | Source Code | Version |
+| ------------------------------------------------------------------- | ------------------------------------------------ | ------- |
+| [AWS](https://registry.terraform.io/modules/wandb/wandb/aws/latest) | https://github.com/wandb/terraform-aws-wandb | v4.0.0+ |
+| [Azure](https://github.com/wandb/terraform-azurerm-wandb) | https://github.com/wandb/terraform-azurerm-wandb | v2.0.0+ |
+| [GCP](https://github.com/wandb/terraform-google-wandb) | https://github.com/wandb/terraform-google-wandb | v2.0.0+ |
This integration ensures that W&B Kubernetes Operator is ready to use for your instance with minimal setup, providing a streamlined path to deploying and managing W&B Server in your cloud environment.
@@ -189,7 +190,7 @@ Follow these steps to verify the installation:
```shell
pip install wandb
```
-2: Log in to W&B:
+2. Log in to W&B:
```shell
wandb login --host=https://YOUR_DNS_DOMAIN
```
@@ -333,7 +334,7 @@ kubectl get deployment wandb -o yaml
```
In both ways you should now have all the configuration values which are needed for the next step.
-2. Create a file called operator.yaml. Follow the format described in the [Configuration Reference](#configuration-reference). Use the values from step 1.
+2. Create a file called operator.yaml. Follow the format described in the [Configuration Reference](#configuration-reference-for-wb-operator). Use the values from step 1.
3. Scale the current deployment to 0 pods. This step is stops the current deployment.
```shell
@@ -352,7 +353,9 @@ helm upgrade --install operator wandb/operator -n wandb-cr --create-namespace
kubectl apply -f operator.yaml
```
The deployment will take a few minutes to complete.
+
7. Verify the installation. Make sure that everything works by following the steps in [Verify the installation](#verify-the-installation).
+
8. Remove to old installation. Uninstall the old helm chart or delete the resources that were created with manifests.
### Migrate to Operator-based Terraform Helm chart
@@ -367,13 +370,13 @@ Follow these steps to migrate to the Operator-based Helm chart:
-## Configuration Reference
+## Configuration Reference for W&B Server
-This section describes the configuration options for W&B Server application. The application receives its configuration as custom resource definition named [WeightsAndBiases](#how-it-works). Many configuration options have been exposed with below configuration, some need to be set as environment variables.
+This section describes the configuration options for W&B Server application. The application receives its configuration as custom resource definition named [WeightsAndBiases](#how-it-works). Some configuration options are exposed with the below configuration, some need to be set as environment variables.
-The documentation has two lists of environment variables: [Basic](./env-vars) and [Advanced](./iam/advanced_env_vars). Only use environment variables if the configuration option that you need has not yet been exposed with Helm Chart.
+The documentation has two lists of environment variables: [basic](./env-vars) and [advanced](./iam/advanced_env_vars). Only use environment variables if the configuration option that you need are not exposed using Helm Chart.
-The W&B Kubernetes operator configuration file for a production deployment requires the following contents:
+The W&B Server application configuration file for a production deployment requires the following contents:
```yaml
apiVersion: apps.wandb.com/v1
@@ -435,8 +438,6 @@ spec:
password: 8wtX6cJHizAZvYScjDzZcUarK4zZGjpV
port: 3306
user: wandb
- extraEnv:
- AWS_REGION: "var-must-be-set-despite-not-needed"
license: eyJhbGnUzaHgyQjQyQWhEU3...ZieKQ2x5GGfw
ingress:
annotations:
@@ -465,6 +466,7 @@ global:
provider: "s3"
name: ""
kmsKey: ""
+ region: ""
```
**GCP**
@@ -510,10 +512,6 @@ This will only work if the SSL certificate is trusted. W&B does not support self
### MySQL
```yaml
-# disable in-chart MySQL
-mysql:
- install: false
-
global:
mysql:
# Example values, replace with your own
@@ -584,7 +582,7 @@ ingress:
redis:
install: false
-global
+global:
redis:
host: ""
port: 6379
@@ -705,11 +703,65 @@ global:
GLOBAL_ENV: "example"
```
+### Custom certificate authority
+`customCACerts` is a list and can take many certificates. Certificate authorities specified in `customCACerts` only apply to the W&B Server application.
+
+```yaml
+global:
+ customCACerts:
+ - |
+ -----BEGIN CERTIFICATE-----
+ MIIBnDCCAUKgAwIBAg.....................fucMwCgYIKoZIzj0EAwIwLDEQ
+ MA4GA1UEChMHSG9tZU.....................tZUxhYiBSb290IENBMB4XDTI0
+ MDQwMTA4MjgzMFoXDT.....................oNWYggsMo8O+0mWLYMAoGCCqG
+ SM49BAMCA0gAMEUCIQ.....................hwuJgyQRaqMI149div72V2QIg
+ P5GD+5I+02yEp58Cwxd5Bj2CvyQwTjTO4hiVl1Xd0M0=
+ -----END CERTIFICATE-----
+ - |
+ -----BEGIN CERTIFICATE-----
+ MIIBxTCCAWugAwIB.....................qaJcwCgYIKoZIzj0EAwIwLDEQ
+ MA4GA1UEChMHSG9t.......................tZUxhYiBSb290IENBMB4XDTI0
+ MDQwMTA4MjgzMVoX.......................UK+moK4nZYvpNpqfvz/7m5wKU
+ SAAwRQIhAIzXZMW4.......................E8UFqsCcILdXjAiA7iTluM0IU
+ aIgJYVqKxXt25blH/VyBRzvNhViesfkNUQ==
+ -----END CERTIFICATE-----
+```
+
+## Configuration Reference for W&B Operator
+
+This section describes configuration options for W&B Kubernetes operator (`wandb-controller-manager`). The operator receives its configuration in the form of a YAML file.
+
+By default, the W&B Kubernetes operator does not need a configuration file. Create a configuration file if required. For example, you might need a configuration file to specify custom certificate authorities, deploy in an air gap environment and so forth.
+
+Find the full list of spec customization [in the Helm repository](https://github.com/wandb/helm-charts/blob/main/charts/operator/values.yaml).
+
+### Custom CA
+A custom certificate authority (`customCACerts`), is a list and can take many certificates. Those certificate authorities when added only apply to the W&B Kubernetes operator (`wandb-controller-manager`).
+
+```yaml
+customCACerts:
+- |
+ -----BEGIN CERTIFICATE-----
+ MIIBnDCCAUKgAwIBAg.....................fucMwCgYIKoZIzj0EAwIwLDEQ
+ MA4GA1UEChMHSG9tZU.....................tZUxhYiBSb290IENBMB4XDTI0
+ MDQwMTA4MjgzMFoXDT.....................oNWYggsMo8O+0mWLYMAoGCCqG
+ SM49BAMCA0gAMEUCIQ.....................hwuJgyQRaqMI149div72V2QIg
+ P5GD+5I+02yEp58Cwxd5Bj2CvyQwTjTO4hiVl1Xd0M0=
+ -----END CERTIFICATE-----
+- |
+ -----BEGIN CERTIFICATE-----
+ MIIBxTCCAWugAwIB.....................qaJcwCgYIKoZIzj0EAwIwLDEQ
+ MA4GA1UEChMHSG9t.......................tZUxhYiBSb290IENBMB4XDTI0
+ MDQwMTA4MjgzMVoX.......................UK+moK4nZYvpNpqfvz/7m5wKU
+ SAAwRQIhAIzXZMW4.......................E8UFqsCcILdXjAiA7iTluM0IU
+ aIgJYVqKxXt25blH/VyBRzvNhViesfkNUQ==
+ -----END CERTIFICATE-----
+```
## FAQ
#### How to get the W&B Operator Console password
-See [Accessing the W&B Kubernetes Operator Management Console](#accessing-the-wb-management-console).
+See [Accessing the W&B Kubernetes Operator Management Console](#access-the-wb-management-console).
#### How to access the W&B Operator Console if Ingress doesn’t work
@@ -722,7 +774,7 @@ kubectl port-forward svc/wandb-console 8082
Access the console in the browser with https://localhost:8082/console.
-See [Accessing the W&B Kubernetes Operator Management Console](#accessing-the-wb-management-console) on how to get the password (Option 2).
+See [Accessing the W&B Kubernetes Operator Management Console](#access-the-wb-management-console) on how to get the password (Option 2).
#### How to view W&B Server logs
diff --git a/docs/guides/hosting/privacy-settings.md b/docs/guides/hosting/privacy-settings.md
index 9cc55232e..204edd2d1 100644
--- a/docs/guides/hosting/privacy-settings.md
+++ b/docs/guides/hosting/privacy-settings.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Configure privacy settings
---
-# Privacy settings
-
Organization and Team admins can configure a set of privacy settings at the organization and team scopes respectively. When configured at the organization scope, organization admins enforce those settings for all teams in that organization.
:::info
diff --git a/docs/guides/hosting/self-managed/aws-tf.md b/docs/guides/hosting/self-managed/aws-tf.md
index 0963d8dd4..434a15549 100644
--- a/docs/guides/hosting/self-managed/aws-tf.md
+++ b/docs/guides/hosting/self-managed/aws-tf.md
@@ -1,11 +1,9 @@
---
-title: AWS
+title: Host a W&B Server on AWS
description: Hosting W&B Server on AWS.
displayed_sidebar: default
---
-# AWS
-
:::info
W&B recommends fully managed deployment options such as [W&B Multi-tenant Cloud](../hosting-options/saas_cloud.md) or [W&B Dedicated Cloud](../hosting-options//dedicated_cloud.md) deployment types. W&B fully managed services are simple and secure to use, with minimum to no configuration required.
:::
diff --git a/docs/guides/hosting/self-managed/azure-tf.md b/docs/guides/hosting/self-managed/azure-tf.md
index f443f417b..6f829449f 100644
--- a/docs/guides/hosting/self-managed/azure-tf.md
+++ b/docs/guides/hosting/self-managed/azure-tf.md
@@ -1,11 +1,9 @@
---
-title: Azure
+title: Host a W&B Server on Azure
description: Hosting W&B Server on Azure.
displayed_sidebar: default
---
-# Azure
-
:::info
W&B recommends fully managed deployment options such as [W&B Multi-tenant Cloud](../hosting-options/saas_cloud.md) or [W&B Dedicated Cloud](../hosting-options//dedicated_cloud.md) deployment types. W&B fully managed services are simple and secure to use, with minimum to no configuration required.
:::
@@ -79,7 +77,7 @@ The steps on this topic are common for any deployment option covered by this doc
4. **Create the file** `variables.tf`. For every option configured in the `terraform.tfvars` Terraform requires a correspondent variable declaration.
- ```bash
+ ```bash
variable "namespace" {
type = string
description = "String used for prefix resources."
@@ -172,7 +170,7 @@ This is the most straightforward deployment option configuration that will creat
Another deployment option uses `Redis` to cache the SQL queries and speed up the application response when loading the metrics for the experiments.
-You need to add the option `create_redis = true` to the same `main.tf` file we worked on in [Deployment Recommended](azure-tf.md#deployment---recommended-20-mins) to enable the cache.
+You need to add the option `create_redis = true` to the same `main.tf` file we worked on in [Deployment Recommended](#deployment---recommended-20-mins) to enable the cache.
```bash
# Spin up all required services
@@ -195,7 +193,7 @@ module "wandb" {
Deployment option 3 consists of enabling the external `message broker`. This is optional because the W&B brings embedded a broker. This option doesn't bring a performance improvement.
-The Azure resource that provides the message broker is the `Azure Event Grid`, and to enable it, you will need to add the option `use_internal_queue = false` to the same `main.tf` that we worked on the [Deployment Recommended](azure-tf.md#deployment---recommended-20-mins)
+The Azure resource that provides the message broker is the `Azure Event Grid`, and to enable it, you will need to add the option `use_internal_queue = false` to the same `main.tf` that we worked on the [Deployment Recommended](#deployment---recommended-20-mins)
```bash
# Spin up all required services
module "wandb" {
@@ -217,4 +215,4 @@ module "wandb" {
## Other deployment options
You can combine all three deployment options adding all configurations to the same file.
-The [Terraform Module](https://github.com/wandb/terraform-azure-wandb) provides several options that can be combined along with the standard options and the minimal configuration found in [Deployment Recommended](azure-tf.md#deployment---recommended-20-mins)
+The [Terraform Module](https://github.com/wandb/terraform-azure-wandb) provides several options that can be combined along with the standard options and the minimal configuration found in [Deployment Recommended](#deployment---recommended-20-mins)
diff --git a/docs/guides/hosting/self-managed/bare-metal.md b/docs/guides/hosting/self-managed/bare-metal.md
index abba0ec51..5b1275dad 100644
--- a/docs/guides/hosting/self-managed/bare-metal.md
+++ b/docs/guides/hosting/self-managed/bare-metal.md
@@ -1,13 +1,11 @@
---
-title: Install on on-prem infra
+title: Host a W&B Server on-premises
description: Hosting W&B Server on on-premises infrastructure
displayed_sidebar: default
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Install on on-prem infra
-
:::info
W&B recommends fully managed deployment options such as [W&B Multi-tenant Cloud](../hosting-options/saas_cloud.md) or [W&B Dedicated Cloud](../hosting-options//dedicated_cloud.md) deployment types. W&B fully managed services are simple and secure to use, with minimum to no configuration required.
:::
@@ -47,7 +45,7 @@ It is important to keep in mind that these specifications are minimum requiremen
### Database Server
-W&B recommends a [MySQL 8](../self-managed/bare-metal.md#mysql-80) database as a metadata store. The shape of the ML practitioners parameters and metadata will greatly affect the performance of the database. The database is typically incrementally written to as practitioners track their training runs and is more read heavy when queries are executed in reports and dashboard.
+W&B recommends a [MySQL 8](#mysql-database) database as a metadata store. The shape of the ML practitioners parameters and metadata will greatly affect the performance of the database. The database is typically incrementally written to as practitioners track their training runs and is more read heavy when queries are executed in reports and dashboard.
To ensure optimal performance we recommend deploying the W&B database on to a server with the following starting specs:
@@ -61,7 +59,7 @@ To ensure optimal performance we recommend deploying the W&B database on to a se
Again, we recommend monitoring the resource usage and performance of the database to ensure that it operates optimally and to make adjustments as necessary.
-Additionally, we recommend the following [parameter overrides](../self-managed/bare-metal.md#mysql-80) to tune the DB for MySQL 8.
+Additionally, we recommend the following [parameter overrides](#mysql-database) to tune the DB for MySQL 8.
### Object Storage
diff --git a/docs/guides/hosting/self-managed/basic-setup.md b/docs/guides/hosting/self-managed/basic-setup.md
index decaa49c5..d704196d2 100644
--- a/docs/guides/hosting/self-managed/basic-setup.md
+++ b/docs/guides/hosting/self-managed/basic-setup.md
@@ -1,10 +1,9 @@
---
description: Run Weights and Biases on your own machines using Docker
displayed_sidebar: default
+title: "Tutorial: Run a W&B Server using Docker"
---
-# Getting started
-
Follow this "Hello, world!" example to learn the general workflow to install W&B Server for Dedicated Cloud and Self Managed hosting options. By the end of this demo, you will know how to host W&B Server on your local machine using a Trial Mode W&B license.
For demonstration purposes, this demo uses a local development server on port `8080` (`localhost:8080`).
diff --git a/docs/guides/hosting/self-managed/gcp-tf.md b/docs/guides/hosting/self-managed/gcp-tf.md
index 0b0c42397..56b16da22 100644
--- a/docs/guides/hosting/self-managed/gcp-tf.md
+++ b/docs/guides/hosting/self-managed/gcp-tf.md
@@ -1,11 +1,9 @@
---
-title: GCP
+title: Host a W&B Server on GCP
description: Hosting W&B Server on GCP.
displayed_sidebar: default
---
-# GCP
-
:::info
W&B recommends fully managed deployment options such as [W&B Multi-tenant Cloud](../hosting-options/saas_cloud.md) or [W&B Dedicated Cloud](../hosting-options//dedicated_cloud.md) deployment types. W&B fully managed services are simple and secure to use, with minimum to no configuration required.
:::
diff --git a/docs/guides/hosting/server-release-process.md b/docs/guides/hosting/server-release-process.md
index ca52e44bb..cee23904f 100644
--- a/docs/guides/hosting/server-release-process.md
+++ b/docs/guides/hosting/server-release-process.md
@@ -1,10 +1,9 @@
---
description: Release process for W&B Server
displayed_sidebar: default
+title: Release process for W&B Server
---
-# Server release process
-
## Frequency and deployment types
W&B Server releases apply to the **Dedicated Cloud** and **Self-managed** deployments. There are three kinds of server releases:
@@ -14,7 +13,7 @@ W&B Server releases apply to the **Dedicated Cloud** and **Self-managed** deploy
| Patch | Patch releases include critical and high severity bug fixes. Patches are only rarely released, as needed. |
| Feature | The feature release targets a specific release date for a new product feature, which occasionally happens before the standard monthly release. |
-All releases are immediately deployed to all **Dedicated Cloud** instances once the acceptance testing phase is complete. It keeps those managed instances fully updated, making the latest features and fixes available to relevant customers. Customers with **Self-managed** instances are responsible for the [update process](./server-upgrade-process.md) on their own schedule, where they can use [the latest Docker image](https://hub.docker.com/r/wandb/local). Refer to [release support and end of life](#release-support-and-end-of-life).
+All releases are immediately deployed to all **Dedicated Cloud** instances once the acceptance testing phase is complete. It keeps those managed instances fully updated, making the latest features and fixes available to relevant customers. Customers with **Self-managed** instances are responsible for the [update process](./server-upgrade-process.md) on their own schedule, where they can use [the latest Docker image](https://hub.docker.com/r/wandb/local). Refer to [release support and end of life](#release-support-and-end-of-life-policy).
:::info
Some advanced features are available only with the enterprise license. So even if you get the latest docker image but don't have an enterprise license, you would not be able to take advantage of the relevant advanced capabilities.
@@ -34,7 +33,7 @@ Downtime might occur for the following scenarios:
* A new feature or enhancement requires changes to the underlying infrastructure such as compute, storage or network. W&B tries to send relevant advance notifications to **Dedicated Cloud** customers.
* An infrastructure change due to a security patch or to avoid `support end-of-life` for a particular version. For urgent changes, **Dedicated Cloud** customers may not receive advance notifications. The priority here is to keep the fleet secure and fully supported.
-For both cases, updates roll out to all **Dedicated Cloud** instances without exception. Customers with **Self-managed** instances are responsible to manage such updates on their own schedule. Refer to [release support and end of life](#release-support-and-end-of-life).
+For both cases, updates roll out to all **Dedicated Cloud** instances without exception. Customers with **Self-managed** instances are responsible to manage such updates on their own schedule. Refer to [release support and end of life](#release-support-and-end-of-life-policy).
## Release support and end of life policy
W&B supports every server release for six months from the release date. **Dedicated Cloud** instances are automatically updated. Customers with **Self-managed** instances are responsible to update their deployments in time to comply with the support policy. Avoid staying on a version older than six months as it would significantly limit support from W&B.
diff --git a/docs/guides/hosting/server-upgrade-process.md b/docs/guides/hosting/server-upgrade-process.md
index 04d354d13..58e4f26d0 100644
--- a/docs/guides/hosting/server-upgrade-process.md
+++ b/docs/guides/hosting/server-upgrade-process.md
@@ -1,10 +1,9 @@
---
description: Guide for updating W&B (Weights & Biases) version and license across different installation methods.
displayed_sidebar: default
+title: Update W&B license and version
---
-# Update W&B License and Version
-
Update your W&B Server Version and License with the same method you installed W&B Server with. The following table lists how to update your license and version based on different deployment methods:
diff --git a/docs/guides/hosting/smtp.md b/docs/guides/hosting/smtp.md
index 8857f03ea..1375b3e58 100644
--- a/docs/guides/hosting/smtp.md
+++ b/docs/guides/hosting/smtp.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Configure SMTP
---
-# SMTP Configuration
-
In W&B server, adding users to the instance or team will trigger an email invite. To send these email invites, W&B uses a third-party mail server. In some cases, organizations might have strict policies on traffic leaving the corporate network and hence causing these email invites to never be sent to the end user. W&B server offers an option to configure sending these invite emails via an internal SMTP server.
To configure, follow the steps below:
diff --git a/docs/guides/integrations/add-wandb-to-any-library.md b/docs/guides/integrations/add-wandb-to-any-library.md
index 0edc7cadf..57d9cd6a0 100644
--- a/docs/guides/integrations/add-wandb-to-any-library.md
+++ b/docs/guides/integrations/add-wandb-to-any-library.md
@@ -1,16 +1,14 @@
---
displayed_sidebar: default
+title: Add wandb to any library
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Add wandb to Any Library
-
This guide provides best practices on how to integrate W&B into your Python library to get powerful Experiment Tracking, GPU and System Monitoring, Model Checkpointing and more for you own library.
:::note
-If you are still learning how to use W&B, we recommend exploring the other W&B Guides in these docs, such as [Experiment Tracking](https://docs.wandb.ai/guides/track), before reading further.
+If you are still learning how to use W&B, we recommend exploring the other W&B Guides in these docs, such as [Experiment Tracking](/guides/track), before reading further.
:::
Below we cover best tips and best practices when the codebase you are working on is more complicated than a single Python training script or Jupyter notebook. The topics covered are:
@@ -247,13 +245,13 @@ Use `wandb.config.update` to update the config. Updating your configuration dict
wandb.config.update({“model_parameters” = 3500})
```
-For more information on how to define a config file, see [Configure Experiments with wandb.config](https://docs.wandb.ai/guides/track/config)
+For more information on how to define a config file, see [Configure Experiments with wandb.config](/guides/track/config)
### Logging To W&B
#### Log Metrics
-Create a dictionary where the key value is the name of the metric. Pass this dictionary object to [`wandb.log`](https://docs.wandb.ai/guides/track/log):
+Create a dictionary where the key value is the name of the metric. Pass this dictionary object to [`wandb.log`](/guides/track/log):
```python
for epoch in range(NUM_EPOCHS):
@@ -278,7 +276,7 @@ wandb.log(metrics)
![A W&B Workspace with 2 separate sections](/images/integrations/integrations_add_any_lib_log.png)
-For more on `wandb.log`, see [Log Data with wandb.log](https://docs.wandb.ai/guides/track/log)
+For more on `wandb.log`, see [Log Data with wandb.log](/guides/track/log)
#### Preventing x-axis Misalignments
@@ -328,7 +326,7 @@ Some considerations when logging data include:
* For images, you can log sample predictions, segmentation masks etc to see the evolution over time.
* For text, you can log tables of sample predictions for later exploration.
-Refer to [Log Data with wandb.log](https://docs.wandb.ai/guides/track/log) for a full guide on logging media, objects, plots and more.
+Refer to [Log Data with wandb.log](/guides/track/log) for a full guide on logging media, objects, plots and more.
### Distributed Training
@@ -369,7 +367,7 @@ aliases = [“best”, “epoch_10”]
wandb.log_artifact(artifact, aliases=aliases)
```
-For information on how to create a custom alias, see [Create a Custom Alias](https://docs.wandb.ai/guides/artifacts/create-a-custom-alias)
+For information on how to create a custom alias, see [Create a Custom Alias](/guides/artifacts/create-a-custom-alias)
You can log output Artifacts at any frequency (for example, every epoch, every 500 steps and so on) and are automatically versioned.
@@ -394,18 +392,18 @@ local_path = artifact.download(“./tmp”)
Artifacts can be found in the Artifacts section of W&B and can be referenced with aliases generated automatically (“latest”, “v2”, “v3”) or manually when logging (“best_accuracy”…).
-To download an Artifact without creating a `wandb` run (through `wandb.init`), for example in distributed environments or for simple inference, you can instead reference the artifact with the [wandb API](https://docs.wandb.ai/ref/python/public-api):
+To download an Artifact without creating a `wandb` run (through `wandb.init`), for example in distributed environments or for simple inference, you can instead reference the artifact with the [wandb API](/ref/python/public-api):
```python
artifact = wandb.Api().artifact(“user/project/artifact:latest”)
local_path = artifact.download()
```
-For more information, see [Download and Use Artifacts](https://docs.wandb.ai/guides/artifacts/download-and-use-an-artifact).
+For more information, see [Download and Use Artifacts](/guides/artifacts/download-and-use-an-artifact).
### Hyper-parameter Tuning
-If your library would like to leverage W&B hyper-parameter tuning, [W&B Sweeps](https://docs.wandb.ai/guides/sweeps) can also be added to your library
+If your library would like to leverage W&B hyper-parameter tuning, [W&B Sweeps](/guides/sweeps) can also be added to your library
### Advanced Integrations
diff --git a/docs/guides/integrations/autotrain.md b/docs/guides/integrations/autotrain.md
index 93d1abac5..ec6b5d500 100644
--- a/docs/guides/integrations/autotrain.md
+++ b/docs/guides/integrations/autotrain.md
@@ -1,17 +1,15 @@
---
displayed_sidebar: default
+title: Hugging Face AutoTrain
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Hugging Face Autotrain
-
[🤗 AutoTrain](https://huggingface.co/docs/autotrain/index) is a no-code tool for training state-of-the-art models for Natural Language Processing (NLP) tasks, for Computer Vision (CV) tasks, and for Speech tasks and even for Tabular tasks.
[Weights & Biases](http://wandb.com/) is directly integrated into 🤗 AutoTrain, providing experiment tracking and config management. It's as easy as using a single parameter in the CLI command for your experiments!
-| ![An example of how the metrics of your experiment are logged](@site/static/images/integrations/hf-autotrain-1.png) |
+| ![An example of how the metrics of your experiment are logged](/images/integrations/hf-autotrain-1.png) |
|:--:|
| **An example of how the metrics of your experiment are logged.** |
@@ -51,7 +49,7 @@ To demonstrate these changes we will fine-tune an LLM on a math dataset and try
### Training using Autotrain Advanced
-We can start training using the Autotrain Advanced CLI. To leverage the logging functionality, we simply use the `--log` argument. Specifying `--log wandb` will seamlessly log your results to a [W&B run](https://docs.wandb.ai/guides/runs).
+We can start training using the Autotrain Advanced CLI. To leverage the logging functionality, we simply use the `--log` argument. Specifying `--log wandb` will seamlessly log your results to a [W&B run](/guides/runs).
-| ![An example of how all the configs of your experiment are saved.](@site/static/images/integrations/hf-autotrain-2.gif) |
+| ![An example of how all the configs of your experiment are saved.](/images/integrations/hf-autotrain-2.gif) |
|:--:|
| **An example of how all the configs of your experiment are saved.** |
diff --git a/docs/guides/integrations/dagster.md b/docs/guides/integrations/dagster.md
index dce2882ea..e9d0412ad 100644
--- a/docs/guides/integrations/dagster.md
+++ b/docs/guides/integrations/dagster.md
@@ -1,12 +1,11 @@
---
description: Guide on how to integrate W&B with Dagster.
displayed_sidebar: default
+title: Dagster
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Dagster
-
Use Dagster and W&B (W&B) to orchestrate your MLOps pipelines and maintain ML assets. The integration with W&B makes it easy within Dagster to:
* Use and create [W&B Artifacts](../artifacts/intro.md).
@@ -31,7 +30,7 @@ Find your W&B entity by checking the profile page for that user or team in the W
### How to get an API key
1. [Log in to W&B](https://wandb.ai/login). Note: if you are using W&B Server ask your admin for the instance host name.
-2. Collect your API key by navigating to the [authorize page](https://wandb.ai/authorize) or in your user/team settings. For a production environment we recommend using a [service account](https://docs.wandb.ai/guides/technical-faq/general#what-is-a-service-account-and-why-is-it-useful) to own that key.
+2. Collect your API key by navigating to the [authorize page](https://wandb.ai/authorize) or in your user/team settings. For a production environment we recommend using a [service account](/guides/technical-faq/general#what-is-a-service-account-and-why-is-it-useful) to own that key.
3. Set an environment variable for that API key export `WANDB_API_KEY=YOUR_KEY`.
@@ -902,7 +901,7 @@ Interested in Launch? Reach out to your account team to talk about joining the c
Pilot customers need to use AWS EKS or SageMaker to qualify for the beta program. We ultimately plan to support additional platforms.
:::
-Before continuing, we recommend you to have a good understanding of how to use W&B Launch. Consider, reading the Guide on Launch: https://docs.wandb.ai/guides/launch.
+Before continuing, we recommend you to have a good understanding of how to use W&B Launch. Consider, reading the Guide on Launch: /guides/launch.
The Dagster integration helps with:
* Running one or multiple Launch agents in your Dagster instance.
diff --git a/docs/guides/integrations/diffusers.md b/docs/guides/integrations/diffusers.md
index 264cae62a..2faf89b96 100644
--- a/docs/guides/integrations/diffusers.md
+++ b/docs/guides/integrations/diffusers.md
@@ -1,13 +1,11 @@
---
displayed_sidebar: default
+title: Hugging Face Diffusers
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# Hugging Face Diffusers
-
[🤗 Diffusers](https://huggingface.co/docs/diffusers) is the go-to library for state-of-the-art pre-trained diffusion models for generating images, audio, and even 3D structures of molecules. The W&B integration adds rich, flexible experiment tracking, media visualization, pipeline architecture, and configuration management to interactive centralized dashboards without compromising that ease of use.
@@ -24,7 +22,7 @@ from wandb.integration.diffusers import autolog
autolog(init=dict(project="diffusers_logging"))
```
-| ![An example of how the results of your experiment are logged](@site/static/images/integrations/diffusers-autolog-4.gif) |
+| ![An example of how the results of your experiment are logged](/images/integrations/diffusers-autolog-4.gif) |
|:--:|
| **An example of how the results of your experiment are logged.** |
@@ -56,14 +54,14 @@ pip install --upgrade diffusers transformers accelerate wandb
### How the `autolog` works?
-The `autolog()` function can be called with the `init` parameter which accepts a dictionary of the parameters required by [`wandb.init()`](https://docs.wandb.ai/ref/python/init).
+The `autolog()` function can be called with the `init` parameter which accepts a dictionary of the parameters required by [`wandb.init()`](/ref/python/init).
When `autolog()` is called, it initializes a Weights & Biases run, which automatically tracks the inputs and the outputs from [all supported pipeline calls](https://github.com/wandb/wandb/blob/main/wandb/integration/diffusers/autolog.py#L12-L72).
-- Each pipeline call is tracked into its own [table](https://docs.wandb.ai/guides/tables) in the workspace, and the configs associated with the pipeline call is appended to the list of workflows in the configs for that run.
-- The prompts, negative prompts, and the generated media are logged in a [`wandb.Table`](https://docs.wandb.ai/guides/tables).
+- Each pipeline call is tracked into its own [table](/guides/tables) in the workspace, and the configs associated with the pipeline call is appended to the list of workflows in the configs for that run.
+- The prompts, negative prompts, and the generated media are logged in a [`wandb.Table`](/guides/tables).
- All other configs associated with the experiment including seed and the pipeline architecture are stored in the config section for the run.
-- The generated media for each pipeline call are also logged in [media panels](https://docs.wandb.ai/guides/track/log/media) in the run.
+- The generated media for each pipeline call are also logged in [media panels](/guides/track/log/media) in the run.
:::info
You can find a list of supported pipeline calls [here](https://github.com/wandb/wandb/blob/main/wandb/integration/diffusers/autolog.py#L12-L72). In case, you want to request a new feature of this integration or report a bug associated with it, please open an issue on [https://github.com/wandb/wandb/issues](https://github.com/wandb/wandb/issues).
@@ -153,20 +151,20 @@ wandb.finish()
-| ![An example of how the results of your experiment are logged](@site/static/images/integrations/diffusers-autolog-2.gif) |
+| ![An example of how the results of your experiment are logged](/images/integrations/diffusers-autolog-2.gif) |
|:--:|
| **An example of how the results of your experiment are logged.** |
-| ![An example of how the results of your experiment are logged](@site/static/images/integrations/diffusers-autolog-1.gif) |
+| ![An example of how the results of your experiment are logged](/images/integrations/diffusers-autolog-1.gif) |
|:--:|
| **An example of how the results of multiple experiments are logged in your workspace.** |
-| ![An example of how the autolog logs the configs of your experiment](@site/static/images/integrations/diffusers-autolog-3.gif) |
+| ![An example of how the autolog logs the configs of your experiment](/images/integrations/diffusers-autolog-3.gif) |
|:--:|
| **An example of how the autolog logs the configs of your experiment.** |
:::info
-You need to explicitly call [`wandb.finish()`](https://docs.wandb.ai/ref/python/finish) when executing the code in IPython notebook environments after calling the pipeline. This is not necessary when executing python scripts.
+You need to explicitly call [`wandb.finish()`](/ref/python/finish) when executing the code in IPython notebook environments after calling the pipeline. This is not necessary when executing python scripts.
:::
## Tracking multi-pipeline workflows
@@ -308,7 +306,7 @@ wandb.finish()
-| ![An example of how the autolog tracks an Stable Diffusion XL + Refiner experiment](@site/static/images/integrations/diffusers-autolog-6.gif) |
+| ![An example of how the autolog tracks an Stable Diffusion XL + Refiner experiment](/images/integrations/diffusers-autolog-6.gif) |
|:--:|
| **An example of how the autolog tracks an Stable Diffusion XL + Refiner experiment.** |
diff --git a/docs/guides/integrations/fastai/README.md b/docs/guides/integrations/fastai/README.md
index df4ad759a..2500f4448 100644
--- a/docs/guides/integrations/fastai/README.md
+++ b/docs/guides/integrations/fastai/README.md
@@ -1,13 +1,12 @@
---
displayed_sidebar: default
+title: fastai
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Fastai
-
-If you're using **fastai** to train your models, W&B has an easy integration using the `WandbCallback`. Explore the details in[ interactive docs with examples →](https://app.wandb.ai/borisd13/demo\_config/reports/Visualize-track-compare-Fastai-models--Vmlldzo4MzAyNA)
+If you're using **fastai** to train your models, W&B has an easy integration using the `WandbCallback`. Explore the details in[ interactive docs with examples →](https://app.wandb.ai/borisd13/demo_config/reports/Visualize-track-compare-Fastai-models--Vmlldzo4MzAyNA)
## Log with W&B
@@ -70,14 +69,14 @@ If you use version 1 of Fastai, refer to the [Fastai v1 docs](v1.md).
| Args | Description |
| ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| log | Whether to log the model's: "`gradients`" , "`parameters`", "`all`" or `None` (default). Losses & metrics are always logged. |
-| log\_preds | whether we want to log prediction samples (default to `True`). |
-| log\_preds\_every\_epoch | whether to log predictions every epoch or at the end (default to `False`) |
-| log\_model | whether we want to log our model (default to False). This also requires `SaveModelCallback` |
-| model\_name | The name of the `file` to save, overrides `SaveModelCallback` |
-| log\_dataset |
False (default)
True will log folder referenced by learn.dls.path.
a path can be defined explicitly to reference which folder to log.
Note: subfolder "models" is always ignored.
|
-| dataset\_name | name of logged dataset (default to `folder name`). |
-| valid\_dl | `DataLoaders` containing items used for prediction samples (default to random items from `learn.dls.valid`. |
-| n\_preds | number of logged predictions (default to 36). |
+| log_preds | whether we want to log prediction samples (default to `True`). |
+| log_preds_every_epoch | whether to log predictions every epoch or at the end (default to `False`) |
+| log_model | whether we want to log our model (default to False). This also requires `SaveModelCallback` |
+| model_name | The name of the `file` to save, overrides `SaveModelCallback` |
+| log_dataset |
False (default)
True will log folder referenced by learn.dls.path.
a path can be defined explicitly to reference which folder to log.
Note: subfolder "models" is always ignored.
|
+| dataset_name | name of logged dataset (default to `folder name`). |
+| valid_dl | `DataLoaders` containing items used for prediction samples (default to random items from `learn.dls.valid`. |
+| n_preds | number of logged predictions (default to 36). |
| seed | used for defining random samples. |
For custom workflows, you can manually log your datasets and models:
@@ -265,5 +264,5 @@ notebook_launcher(train, num_processes=2)
## Examples
-* [Visualize, track, and compare Fastai models](https://app.wandb.ai/borisd13/demo\_config/reports/Visualize-track-compare-Fastai-models--Vmlldzo4MzAyNA): A thoroughly documented walkthrough
+* [Visualize, track, and compare Fastai models](https://app.wandb.ai/borisd13/demo_config/reports/Visualize-track-compare-Fastai-models--Vmlldzo4MzAyNA): A thoroughly documented walkthrough
* [Image Segmentation on CamVid](http://bit.ly/fastai-wandb): A sample use case of the integration
diff --git a/docs/guides/integrations/fastai/v1.md b/docs/guides/integrations/fastai/v1.md
index e77d938e9..f6b684df7 100644
--- a/docs/guides/integrations/fastai/v1.md
+++ b/docs/guides/integrations/fastai/v1.md
@@ -1,10 +1,8 @@
---
displayed_sidebar: default
+title: fastai v1
---
-# fastai v1
-
-
:::note
This documentation is for fastai v1.
If you use the current version of fastai, you should refer to [fastai page](../intro.md).
diff --git a/docs/guides/integrations/huggingface.md b/docs/guides/integrations/huggingface.md
index 59448fa94..44ea7c818 100644
--- a/docs/guides/integrations/huggingface.md
+++ b/docs/guides/integrations/huggingface.md
@@ -1,16 +1,14 @@
---
displayed_sidebar: default
+title: Hugging Face Transformers
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# Hugging Face Transformers
-
-The [Hugging Face Transformers](https://huggingface.co/transformers/) library makes state-of-the-art NLP models like BERT and training techniques like mixed precision and gradient checkpointing easy to use. The [W&B integration](https://huggingface.co/transformers/main\_classes/callback.html#transformers.integrations.WandbCallback) adds rich, flexible experiment tracking and model versioning to interactive centralized dashboards without compromising that ease of use.
+The [Hugging Face Transformers](https://huggingface.co/transformers/) library makes state-of-the-art NLP models like BERT and training techniques like mixed precision and gradient checkpointing easy to use. The [W&B integration](https://huggingface.co/transformers/main_classes/callback.html#transformers.integrations.WandbCallback) adds rich, flexible experiment tracking and model versioning to interactive centralized dashboards without compromising that ease of use.
## 🤗 Next-level logging in few lines
@@ -23,7 +21,7 @@ from transformers import TrainingArguments, Trainer
args = TrainingArguments(..., report_to="wandb") # turn on W&B logging
trainer = Trainer(..., args=args)
```
-![Explore your experiment results in the W&B interactive dashboard](@site/static/images/integrations/huggingface_gif.gif)
+![Explore your experiment results in the W&B interactive dashboard](/images/integrations/huggingface_gif.gif)
:::info
If you'd rather dive straight into working code, check out this [Google Colab](https://wandb.me/hf).
@@ -220,7 +218,7 @@ However, If you pass a [`run_name`](https://huggingface.co/docs/transformers/mai
:::
#### W&B Model Registry
-Once you have logged your checkpoints to Artifacts, you can then register your best model checkpoints and centralize them across your team using the Weights & Biases **[Model Registry](../model_registry)**. Here you can organize your best models by task, manage model lifecycle, facilitate easy tracking and auditing throughout the ML lifecyle, and [automate](https://docs.wandb.ai/guides/models/automation) downstream actions with webhooks or jobs.
+Once you have logged your checkpoints to Artifacts, you can then register your best model checkpoints and centralize them across your team using the Weights & Biases **[Model Registry](../model_registry)**. Here you can organize your best models by task, manage model lifecycle, facilitate easy tracking and auditing throughout the ML lifecyle, and [automate](/guides/artifacts/project-scoped-automations/#create-a-webhook-automation) downstream actions with webhooks or jobs.
See the [Model Registry](../model_registry) documentation for how to link a model Artifact to the Model Registry.
@@ -319,7 +317,7 @@ with wandb.init(
### Custom logging: log and view evaluation samples during training
-Logging to Weights & Biases via the Transformers `Trainer` is taken care of by the [`WandbCallback`](https://huggingface.co/transformers/main\_classes/callback.html#transformers.integrations.WandbCallback) in the Transformers library. If you need to customize your Hugging Face logging you can modify this callback by subclassing `WandbCallback` and adding additional functionality that leverages additional methods from the Trainer class.
+Logging to Weights & Biases via the Transformers `Trainer` is taken care of by the [`WandbCallback`](https://huggingface.co/transformers/main_classes/callback.html#transformers.integrations.WandbCallback) in the Transformers library. If you need to customize your Hugging Face logging you can modify this callback by subclassing `WandbCallback` and adding additional functionality that leverages additional methods from the Trainer class.
Below is the general pattern to add this new callback to the HF Trainer, and further down is a code-complete example to log evaluation outputs to a W&B Table:
@@ -444,7 +442,7 @@ For a more detailed example please refer to this [colab](https://colab.research.
### Additional W&B settings
-Further configuration of what is logged with `Trainer` is possible by setting environment variables. A full list of W&B environment variables [can be found here](https://docs.wandb.ai/library/environment-variables).
+Further configuration of what is logged with `Trainer` is possible by setting environment variables. A full list of W&B environment variables [can be found here](/guides/hosting/env-vars/).
| Environment Variable | Usage |
| -------------------- |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
diff --git a/docs/guides/integrations/intro.md b/docs/guides/integrations/intro.md
index bba93e4ae..c66b004a6 100644
--- a/docs/guides/integrations/intro.md
+++ b/docs/guides/integrations/intro.md
@@ -1,10 +1,9 @@
---
slug: /guides/integrations
displayed_sidebar: default
+title: Integrations
---
-# Integrations
-
W&B integrations make it fast and easy to set up experiment tracking and data versioning inside existing projects. Check out integrations for ML frameworks such as [PyTorch](pytorch.md), ML libraries such as [Hugging Face](huggingface.md), or cloud services such as [Amazon SageMaker](other/sagemaker.md).
diff --git a/docs/guides/integrations/keras.md b/docs/guides/integrations/keras.md
index 3649795d7..da35e9ebb 100644
--- a/docs/guides/integrations/keras.md
+++ b/docs/guides/integrations/keras.md
@@ -1,21 +1,21 @@
---
displayed_sidebar: default
+title: Keras
---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# Keras
-
-[**Try in a Colab Notebook here →**](http://wandb.me/intro-keras)
+
## The Weights & Biases Keras Callbacks
We have added three new callbacks for Keras and TensorFlow users, available from `wandb` v0.13.4. For the legacy `WandbCallback` scroll down.
-**`WandbMetricsLogger`** : Use this callback for [Experiment Tracking](https://docs.wandb.ai/guides/track). It will log your training and validation metrics along with system metrics to Weights and Biases.
+**`WandbMetricsLogger`** : Use this callback for [Experiment Tracking](/guides/track). It will log your training and validation metrics along with system metrics to Weights and Biases.
-**`WandbModelCheckpoint`** : Use this callback to log your model checkpoints to Weight and Biases [Artifacts](https://docs.wandb.ai/guides/data-and-model-versioning).
+**`WandbModelCheckpoint`** : Use this callback to log your model checkpoints to Weight and Biases [Artifacts](/guides/artifacts).
-**`WandbEvalCallback`**: This base callback will log model predictions to Weights and Biases [Tables](https://docs.wandb.ai/guides/tables) for interactive visualization.
+**`WandbEvalCallback`**: This base callback will log model predictions to Weights and Biases [Tables](/guides/tables) for interactive visualization.
These new callbacks,
@@ -25,7 +25,7 @@ These new callbacks,
## Experiment Tracking with `WandbMetricsLogger`
-[**Try in a Colab Notebook here →**](https://github.com/wandb/examples/blob/master/colabs/keras/Use\_WandbMetricLogger\_in\_your\_Keras\_workflow.ipynb)
+
`WandbMetricsLogger` automatically logs Keras' `logs` dictionary that callback methods such as `on_epoch_end`, `on_batch_end` etc, take as an argument.
@@ -58,11 +58,11 @@ model.fit(
## Model Checkpointing using `WandbModelCheckpoint`
-[**Try in a Colab Notebook here →**](https://github.com/wandb/examples/blob/master/colabs/keras/Use\_WandbModelCheckpoint\_in\_your\_Keras\_workflow.ipynb)
+
Use `WandbModelCheckpoint` callback to save the Keras model (`SavedModel` format) or model weights periodically and uploads them to W&B as a `wandb.Artifact` for model versioning.
-This callback is subclassed from [`tf.keras.callbacks.ModelCheckpoint`](https://www.tensorflow.org/api\_docs/python/tf/keras/callbacks/ModelCheckpoint) ,thus the checkpointing logic is taken care of by the parent callback.
+This callback is subclassed from [`tf.keras.callbacks.ModelCheckpoint`](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint) ,thus the checkpointing logic is taken care of by the parent callback.
This callback provides the following features:
@@ -102,7 +102,7 @@ model.fit(
| `verbose` | (int): Verbosity mode, 0 or 1. Mode 0 is silent, and mode 1 displays messages when the callback takes an action. |
| `save_best_only` | (bool): if `save_best_only=True`, it only saves when the model is considered the "best" and the latest best model according to the quantity monitored (`monitor`) will not be overwritten. |
| `save_weights_only` | (bool): if True, then only the model's weights will be saved. |
-| `mode` | ("auto", "min", or "max"): For val\_acc, this should be ‘max’, for val\_loss this should be ‘min’, etc. |
+| `mode` | ("auto", "min", or "max"): For val_acc, this should be ‘max’, for val_loss this should be ‘min’, etc. |
| `save_weights_only` | (bool): if True, then only the model's weights will be saved. |
| `save_freq` | ("epoch" or int): When using ‘epoch’, the callback saves the model after each epoch. When using an integer, the callback saves the model at end of this many batches. Note that when monitoring validation metrics such as `val_acc` or `val_loss`, `save_freq` must be set to "epoch" as those metrics are only available at the end of an epoch. |
| `options` | (str): Optional `tf.train.CheckpointOptions` object if `save_weights_only` is true or optional `tf.saved_model.SaveOptions` object if `save_weights_only` is false. |
@@ -134,7 +134,7 @@ WandbModelCheckpoint(
## Model Prediction Visualization using `WandbEvalCallback`
-[**Try in a Colab Notebook here →**](https://github.com/wandb/examples/blob/e66f16fbe7ae7a2e636d59350a50059d3f7e5494/colabs/keras/Use_WandbEvalCallback_in_your_Keras_workflow.ipynb)
+
The `WandbEvalCallback` is an abstract base class to build Keras callbacks primarily for model prediction and, secondarily, dataset visualization.
@@ -210,7 +210,7 @@ model.fit(
```
:::info
-💡 The Tables are logged to the W&B [Artifact page](https://docs.wandb.ai/ref/app/pages/project-page#artifacts-tab) by default and not the [Workspace](https://docs.wandb.ai/ref/app/pages/workspaces) page.
+💡 The Tables are logged to the W&B [Artifact page](/guides/artifacts/explore-and-traverse-an-artifact-graph) by default and not the Workspace page.
:::
**`WandbEvalCallback` Reference**
@@ -234,7 +234,7 @@ You can override the `on_train_begin` or `on_epoch_end` methods to have more fin
## WandbCallback [Legacy]
-Use the W&B library [`WandbCallback`](https://docs.wandb.ai/ref/python/integrations/keras/wandbcallback) Class to automatically save all the metrics and the loss values tracked in `model.fit`.
+Use the W&B library [`WandbCallback`](/ref/python/integrations/keras/wandbcallback) Class to automatically save all the metrics and the loss values tracked in `model.fit`.
```python
import wandb
@@ -254,13 +254,13 @@ model.fit(
See this one minute, step-by-step video if this is your first time integrating W&B with Keras: [Get Started with Keras and Weights & Biases in Less Than a Minute](https://www.youtube.com/watch?ab_channel=Weights&Biases&v=4FjDIJ-vO_M)
-For a more detailed video, see [Integrate Weights & Biases with Keras](https://www.youtube.com/watch?v=Bsudo7jbMow\&ab\_channel=Weights%26Biases). The notebook example used can be found here: [Colab Jupyter Notebook](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/keras/Keras\_pipeline\_with\_Weights\_and\_Biases.ipynb).
+For a more detailed video, see [Integrate Weights & Biases with Keras](https://www.youtube.com/watch?v=Bsudo7jbMow\&ab_channel=Weights%26Biases). The notebook example used can be found here: [Colab Jupyter Notebook](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/keras/Keras_pipeline_with_Weights_and_Biases.ipynb).
:::info
-Try W&B and Keras integration example from the video above in a [colab notebook](http://wandb.me/keras-colab). Or see our [example repo](https://github.com/wandb/examples) for scripts, including a [Fashion MNIST example](https://github.com/wandb/examples/blob/master/examples/keras/keras-cnn-fashion/train.py) and the [W&B Dashboard](https://wandb.ai/wandb/keras-fashion-mnist/runs/5z1d85qs) it generates.
+See our [example repo](https://github.com/wandb/examples) for scripts, including a [Fashion MNIST example](https://github.com/wandb/examples/blob/master/examples/keras/keras-cnn-fashion/train.py) and the [W&B Dashboard](https://wandb.ai/wandb/keras-fashion-mnist/runs/5z1d85qs) it generates.
:::
-The `WandbCallback` class supports a wide variety of logging configuration options: specifying a metric to monitor, tracking of weights and gradients, logging of predictions on training\_data and validation\_data, and more.
+The `WandbCallback` class supports a wide variety of logging configuration options: specifying a metric to monitor, tracking of weights and gradients, logging of predictions on training_data and validation_data, and more.
Check out [the reference documentation for the `keras.WandbCallback`](../../ref/python/integrations/keras/wandbcallback.md) for full details.
@@ -294,11 +294,11 @@ The `WandbCallback`
| `class_colors` | (\[float, float, float]) if the input or output is a segmentation mask, an array containing an rgb tuple (range 0-1) for each class. |
| `log_batch_frequency` | (integer) if None, callback will log every epoch. If set to integer, callback will log training metrics every `log_batch_frequency` batches. |
| `log_best_prefix` | (string) if None, no extra summary metrics will be saved. If set to a string, the monitored metric and epoch will be prepended with this value and stored as summary metrics. |
-| `validation_indexes` | (\[wandb.data\_types.\_TableLinkMixin]) an ordered list of index keys to associate with each validation example. If log\_evaluation is True and `validation_indexes` is provided, then a Table of validation data will not be created and instead each prediction will be associated with the row represented by the `TableLinkMixin`. The most common way to obtain such keys are is use `Table.get_index()` which will return a list of row keys. |
-| `validation_row_processor` | (Callable) a function to apply to the validation data, commonly used to visualize the data. The function will receive an `ndx` (int) and a `row` (dict). If your model has a single input, then `row["input"]` will be the input data for the row. Else, it will be keyed based on the name of the input slot. If your fit function takes a single target, then `row["target"]` will be the target data for the row. Else, it will be keyed based on the name of the output slots. For example, if your input data is a single ndarray, but you wish to visualize the data as an Image, then you can provide `lambda ndx, row: {"img": wandb.Image(row["input"])}` as the processor. Ignored if log\_evaluation is False or `validation_indexes` are present. |
+| `validation_indexes` | (\[wandb.data_types._TableLinkMixin]) an ordered list of index keys to associate with each validation example. If log_evaluation is True and `validation_indexes` is provided, then a Table of validation data will not be created and instead each prediction will be associated with the row represented by the `TableLinkMixin`. The most common way to obtain such keys are is use `Table.get_index()` which will return a list of row keys. |
+| `validation_row_processor` | (Callable) a function to apply to the validation data, commonly used to visualize the data. The function will receive an `ndx` (int) and a `row` (dict). If your model has a single input, then `row["input"]` will be the input data for the row. Else, it will be keyed based on the name of the input slot. If your fit function takes a single target, then `row["target"]` will be the target data for the row. Else, it will be keyed based on the name of the output slots. For example, if your input data is a single ndarray, but you wish to visualize the data as an Image, then you can provide `lambda ndx, row: {"img": wandb.Image(row["input"])}` as the processor. Ignored if log_evaluation is False or `validation_indexes` are present. |
| `output_row_processor` | (Callable) same as `validation_row_processor`, but applied to the model's output. `row["output"]` will contain the results of the model output. |
| `infer_missing_processors` | (bool) Determines if `validation_row_processor` and `output_row_processor` should be inferred if missing. Defaults to True. If `labels` are provided, we will attempt to infer classification-type processors where appropriate. |
-| `log_evaluation_frequency` | (int) Determines the frequency which evaluation results will be logged. Default 0 (only at the end of training). Set to 1 to log every epoch, 2 to log every other epoch, and so on. Has no effect when log\_evaluation is False. |
+| `log_evaluation_frequency` | (int) Determines the frequency which evaluation results will be logged. Default 0 (only at the end of training). Set to 1 to log every epoch, 2 to log every other epoch, and so on. Has no effect when log_evaluation is False. |
## Frequently Asked Questions
diff --git a/docs/guides/integrations/langchain.md b/docs/guides/integrations/langchain.md
index bd4ca7807..74bc5feb9 100644
--- a/docs/guides/integrations/langchain.md
+++ b/docs/guides/integrations/langchain.md
@@ -1,11 +1,10 @@
---
displayed_sidebar: default
+title: LangChain
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# LangChain
-
[LangChain](https://python.langchain.com/) is a framework for developing applications powered by language models.
## Using LangChain in Weights & Biases
diff --git a/docs/guides/integrations/lightgbm.md b/docs/guides/integrations/lightgbm.md
index d3230a31a..ae2476073 100644
--- a/docs/guides/integrations/lightgbm.md
+++ b/docs/guides/integrations/lightgbm.md
@@ -1,9 +1,11 @@
---
description: Track your trees with W&B.
displayed_sidebar: default
+title: LightGBM
---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-# LightGBM
+
The `wandb` library includes a special callback for [LightGBM](https://lightgbm.readthedocs.io/en/latest/). It's also easy to use the generic logging features of Weights & Biases to track large experiments, like hyperparameter sweeps.
@@ -19,7 +21,7 @@ log_summary(gbm, save_model_checkpoint=True)
```
:::info
-Looking for working code examples? Check out [our repository of examples on GitHub](https://github.com/wandb/examples/tree/master/examples/boosting-algorithms) or try out a [Colab notebook](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/boosting/Simple\_LightGBM\_Integration.ipynb).
+Looking for working code examples? Check out [our repository of examples on GitHub](https://github.com/wandb/examples/tree/master/examples/boosting-algorithms).
:::
## Tuning your hyperparameters with Sweeps
@@ -27,7 +29,9 @@ Looking for working code examples? Check out [our repository of examples on GitH
Attaining the maximum performance out of models requires tuning hyperparameters, like tree depth and learning rate. Weights & Biases includes [Sweeps](../sweeps/), a powerful toolkit for configuring, orchestrating, and analyzing large hyperparameter testing experiments.
:::info
-To learn more about these tools and see an example of how to use Sweeps with XGBoost, check out [this interactive Colab notebook.](http://wandb.me/xgb-sweeps-colab)
+To learn more about these tools and see an example of how to use Sweeps with XGBoost, check out this interactive Colab notebook.
+
+
:::
![tl;dr: trees outperform linear learners on this classification dataset.](/images/integrations/lightgbm_sweeps.png)
diff --git a/docs/guides/integrations/lightning.md b/docs/guides/integrations/lightning.md
index f797ecdbd..f0db7478d 100644
--- a/docs/guides/integrations/lightning.md
+++ b/docs/guides/integrations/lightning.md
@@ -1,13 +1,12 @@
---
displayed_sidebar: default
+title: PyTorch Lightning
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# PyTorch Lightning
-
-[**Try in a Colab Notebook here →**](https://wandb.me/lightning)
+
PyTorch Lightning provides a lightweight wrapper for organizing your PyTorch code and easily adding advanced features such as distributed training and 16-bit precision. W&B provides a lightweight wrapper for logging your ML experiments. But you don't need to combine the two yourself: Weights & Biases is incorporated directly into the PyTorch Lightning library via the [**`WandbLogger`**](https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.loggers.wandb.html#module-lightning.pytorch.loggers.wandb).
@@ -57,7 +56,7 @@ fabric.log_dict({"important_metric": important_metric})
-![Interactive dashboards accessible anywhere, and more!](@site/static/images/integrations/n6P7K4M.gif)
+![Interactive dashboards accessible anywhere, and more!](/images/integrations/n6P7K4M.gif)
## Sign up and Log in to wandb
@@ -325,7 +324,7 @@ for epoch in range(num_epochs):
### Log the min/max of your metric
-Using wandb's [`define_metric`](https://docs.wandb.ai/ref/python/run#define\_metric) function you can define whether you'd like your W&B summary metric to display the min, max, mean or best value for that metric. If `define`_`metric` _ isn't used, then the last value logged with appear in your summary metrics. See the `define_metric` [reference docs here](https://docs.wandb.ai/ref/python/run#define\_metric) and the [guide here](https://docs.wandb.ai/guides/track/log#customize-axes-and-summaries-with-define\_metric) for more.
+Using wandb's [`define_metric`](/ref/python/run#define_metric) function you can define whether you'd like your W&B summary metric to display the min, max, mean or best value for that metric. If `define`_`metric` _ isn't used, then the last value logged with appear in your summary metrics. See the `define_metric` [reference docs here](/ref/python/run#define_metric) and the [guide here](/guides/track/log/customize-logging-axes) for more.
To tell W&B to keep track of the max validation accuracy in the W&B summary metric, you just need to call `wandb.define_metric` once, e.g. you can call it at the beginning of training like so:
@@ -371,8 +370,8 @@ fabric.log_dict({"val_accuracy": val_accuracy})
### Model Checkpointing
-To save model checkpoints as W&B [Artifacts](https://docs.wandb.ai/guides/data-and-model-versioning),
-use the Lightning [`ModelCheckpoint`](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch\_lightning.callbacks.ModelCheckpoint.html#pytorch\_lightning.callbacks.ModelCheckpoint) callback and set the `log_model` argument in the `WandbLogger`:
+To save model checkpoints as W&B [Artifacts](/guides/artifacts/),
+use the Lightning [`ModelCheckpoint`](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.callbacks.ModelCheckpoint.html#pytorch_lightning.callbacks.ModelCheckpoint) callback and set the `log_model` argument in the `WandbLogger`:
```python
# log model only if `val_accuracy` increases
@@ -406,7 +405,7 @@ fabric = L.Fabric(loggers=[wandb_logger], callbacks=[checkpoint_callback])
-The _latest_ and _best_ aliases are automatically set to easily retrieve a model checkpoint from a W&B [Artifact](https://docs.wandb.ai/guides/data-and-model-versioning):
+The _latest_ and _best_ aliases are automatically set to easily retrieve a model checkpoint from a W&B [Artifact](/guides/artifacts/):
```python
# reference can be retrieved in artifacts panel
@@ -474,11 +473,11 @@ optimizer.load_state_dict(full_checkpoint["optimizer"])
-The model checkpoints you log will be viewable through the [W&B Artifacts](https://docs.wandb.ai/guides/artifacts) UI, and include the full model lineage (see an example model checkpoint in the UI [here](https://wandb.ai/wandb/arttest/artifacts/model/iv3_trained/5334ab69740f9dda4fed/lineage?_gl=1*yyql5q*_ga*MTQxOTYyNzExOS4xNjg0NDYyNzk1*_ga_JH1SJHJQXJ*MTY5MjMwNzI2Mi4yNjkuMS4xNjkyMzA5NjM2LjM3LjAuMA..)).
+The model checkpoints you log will be viewable through the [W&B Artifacts](/guides/artifacts) UI, and include the full model lineage (see an example model checkpoint in the UI [here](https://wandb.ai/wandb/arttest/artifacts/model/iv3_trained/5334ab69740f9dda4fed/lineage?_gl=1*yyql5q*_ga*MTQxOTYyNzExOS4xNjg0NDYyNzk1*_ga_JH1SJHJQXJ*MTY5MjMwNzI2Mi4yNjkuMS4xNjkyMzA5NjM2LjM3LjAuMA..)).
-To bookmark your best model checkpoints and centralize them across your team, you can link them to the [W&B Model Registry](https://docs.wandb.ai/guides/models).
+To bookmark your best model checkpoints and centralize them across your team, you can link them to the [W&B Model Registry](/guides/models).
-Here you can organize your best models by task, manage model lifecycle, facilitate easy tracking and auditing throughout the ML lifecyle, and [automate](https://docs.wandb.ai/guides/models/automation) downstream actions with webhooks or jobs.
+Here you can organize your best models by task, manage model lifecycle, facilitate easy tracking and auditing throughout the ML lifecyle, and [automate](/guides/artifacts/project-scoped-automations/#create-a-webhook-automation) downstream actions with webhooks or jobs.
### Log images, text and more
diff --git a/docs/guides/integrations/mmengine.md b/docs/guides/integrations/mmengine.md
index 31aeeb741..494a33a18 100644
--- a/docs/guides/integrations/mmengine.md
+++ b/docs/guides/integrations/mmengine.md
@@ -1,12 +1,9 @@
---
displayed_sidebar: default
+title: MMEngine
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-
-# MMEngine
MMEngine by [OpenMMLab](https://github.com/open-mmlab) is a foundational library for training deep learning models based on PyTorch. MMEngine implements a next-generation training architecture for the OpenMMLab algorithm library, providing a unified execution foundation for over 30 algorithm libraries within OpenMMLab. Its core components include the training engine, evaluation engine, and module management.
@@ -69,7 +66,7 @@ visualizer = Visualizer.get_instance(**visualization_cfg)
```
:::info
-You pass a dictionary of arguments for [W&B run initialization](https://docs.wandb.ai/ref/python/init) input parameters to `init_kwargs`.
+You pass a dictionary of arguments for [W&B run initialization](/ref/python/init) input parameters to `init_kwargs`.
:::
Next, you simply initialize a `runner` with the `visualizer`, and call `runner.train()`.
@@ -91,7 +88,7 @@ runner = Runner(
runner.train()
```
-| ![An example of your experiment tracked using the `WandbVisBackend`](@site/static/images/integrations/mmengine.png) |
+| ![An example of your experiment tracked using the `WandbVisBackend`](/images/integrations/mmengine.png) |
|:--:|
| **An example of your experiment tracked using the `WandbVisBackend`.** |
diff --git a/docs/guides/integrations/nim.md b/docs/guides/integrations/nim.md
index f39ab3184..4e2c37383 100644
--- a/docs/guides/integrations/nim.md
+++ b/docs/guides/integrations/nim.md
@@ -1,11 +1,10 @@
---
displayed_sidebar: default
+title: NVIDIA NeMo Inference Microservice Deploy Job
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# NVIDIA NeMo Inference Microservice Deploy Job
Deploy a model artifact from W&B to a NVIDIA NeMo Inference Microservice. To do this, use W&B Launch. W&B Launch converts model artifacts to NVIDIA NeMo Model and deploys to a running NIM/Triton server.
diff --git a/docs/guides/integrations/other/accelerate.md b/docs/guides/integrations/other/accelerate.md
index 9929b6a1b..fb5e9f9f7 100644
--- a/docs/guides/integrations/other/accelerate.md
+++ b/docs/guides/integrations/other/accelerate.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/accelerate
description: Training and inference at scale made simple, efficient and adaptable
displayed_sidebar: default
+title: Hugging Face Accelerate
---
-# Hugging Face Accelerate
-
Accelerate is a library that enables the same PyTorch code to be run across any distributed configuration by adding just four lines of code, making training and inference at scale made simple, efficient and adaptable.
Accelerate includes a Weights & Biases Tracker which we show how to use below. You can also read more about Accelerate Trackers in **[their docs here](https://huggingface.co/docs/accelerate/main/en/usage_guides/tracking)**
@@ -41,7 +40,7 @@ Explaining more, you need to:
1. Pass `log_with="wandb"` when initialising the Accelerator class
2. Call the [`init_trackers`](https://huggingface.co/docs/accelerate/main/en/package_reference/accelerator#accelerate.Accelerator.init_trackers) method and pass it:
- a project name via `project_name`
-- any parameters you want to pass to [`wandb.init`](https://docs.wandb.ai/ref/python/init) via a nested dict to `init_kwargs`
+- any parameters you want to pass to [`wandb.init`](/ref/python/init) via a nested dict to `init_kwargs`
- any other experiment config information you want to log to your wandb run, via `config`
3. Use the `.log` method to log to Weigths & Biases; the `step` argument is optional
4. Call `.end_training` when finished training
diff --git a/docs/guides/integrations/other/azure-openai-fine-tuning.md b/docs/guides/integrations/other/azure-openai-fine-tuning.md
index 6ab535918..6660e0588 100644
--- a/docs/guides/integrations/other/azure-openai-fine-tuning.md
+++ b/docs/guides/integrations/other/azure-openai-fine-tuning.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/azure-openai-fine-tuning
description: How to Fine-Tune Azure OpenAI models using W&B.
displayed_sidebar: default
+title: Azure OpenAI Fine-Tuning
---
-# Azure OpenAI Fine-Tuning
-
## Introduction
Fine-tuning GPT-3.5 or GPT-4 models on Microsoft Azure using W&B allows for detailed tracking and analysis of model performance. This guide extends the concepts from the [OpenAI Fine-Tuning guide](/guides/integrations/openai) with specific steps and features for Azure OpenAI.
diff --git a/docs/guides/integrations/other/catalyst.md b/docs/guides/integrations/other/catalyst.md
index 347762d75..f23d40f2f 100644
--- a/docs/guides/integrations/other/catalyst.md
+++ b/docs/guides/integrations/other/catalyst.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/catalyst
description: How to integrate W&B for Catalyst, a Pytorch framework.
displayed_sidebar: default
+title: Catalyst
---
-# Catalyst
-
> [Catalyst](https://github.com/catalyst-team/catalyst) is a PyTorch framework for deep learning R&D that focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new.
Catalyst has an awesome W&B integration for logging parameters, metrics, images, and other artifacts.
diff --git a/docs/guides/integrations/other/cohere-fine-tuning.md b/docs/guides/integrations/other/cohere-fine-tuning.md
new file mode 100644
index 000000000..e44907497
--- /dev/null
+++ b/docs/guides/integrations/other/cohere-fine-tuning.md
@@ -0,0 +1,67 @@
+---
+slug: /guides/integrations/cohere-fine-tunining
+description: How to Fine-Tune Cohere models using W&B.
+displayed_sidebar: default
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Cohere fine-tuning
+
+With Weights & Biases you can log your Cohere model's fine-tuning metrics and configuration to analyze and understand the performance of your models and share the results with your colleagues.
+
+This [guide from Cohere](https://docs.cohere.com/page/convfinqa-finetuning-wandb) has a full example of how to kick off a fine-tuning run and you can find the [Cohere API docs here](https://docs.cohere.com/reference/createfinetunedmodel#request.body.settings.wandb)
+
+## Log your Cohere fine-tuning results
+
+To add Cohere fine-tuning logging to your W&B workspace:
+
+1. Create a `WandbConfig` with your W&B API key, W&B `entity` and `project` name. You can find your W&B API key at https://wandb.ai/authorize
+
+2. Pass this config to the `FinetunedModel` object along with your model name, dataset and hyperparameters to kick off your fine-tuning run.
+
+
+```python
+from cohere.finetuning import WandbConfig, FinetunedModel
+
+# create a config with your W&B details
+wandb_ft_config = WandbConfig(
+ api_key="",
+ entity="my-entity", # must be a valid enitity associated with the provided API key
+ project="cohere-ft",
+)
+
+... # set up your datasets and hyperparameters
+
+# start a fine-tuning run on cohere
+cmd_r_finetune = co.finetuning.create_finetuned_model(
+ request=FinetunedModel(
+ name="command-r-ft",
+ settings=Settings(
+ base_model=...
+ dataset_id=...
+ hyperparameters=...
+ wandb=wandb_ft_config # pass your W&B config here
+ ),
+ ),
+)
+```
+
+You can then view your model's fine-tuning training and validation metrics and hyperparameters in the W&B project that you created.
+
+![](/images/integrations/cohere_ft.png)
+
+
+## Frequently Asked Questions
+
+### How can I organize my runs?
+
+Your W&B runs are automatically organized and can be filtered/sorted based on any configuration parameter such as job type, base model, learning rate and any other hyper-parameter.
+
+In addition, you can rename your runs, add notes or create tags to group them.
+
+
+## Resources
+
+* **[Cohere Fine-tuning Example](https://github.com/cohere-ai/notebooks/blob/kkt_ft_cookbooks/notebooks/finetuning/convfinqa_finetuning_wandb.ipynb)**
diff --git a/docs/guides/integrations/other/composer.md b/docs/guides/integrations/other/composer.md
index 357206f80..a0566ab04 100644
--- a/docs/guides/integrations/other/composer.md
+++ b/docs/guides/integrations/other/composer.md
@@ -2,11 +2,11 @@
slug: /guides/integrations/composer
description: State of the art algorithms to train your neural networks
displayed_sidebar: default
+title: MosaicML Composer
---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# MosaicML Composer
-
-[**Try in a Colab Notebook here →**](https://github.com/wandb/examples/blob/master/colabs/mosaicml/MosaicML_Composer_and_wandb.ipynb)
+
[Composer](https://github.com/mosaicml/composer) is a library for training neural networks better, faster, and cheaper. It contains many state-of-the-art methods for accelerating neural network training and improving generalization, along with an optional [Trainer](https://docs.mosaicml.com/projects/composer/en/stable/trainer/using_the_trainer.html) API that makes _composing_ many different enhancements easy.
@@ -21,7 +21,7 @@ from composer.loggers import WandBLogger
trainer = Trainer(..., logger=WandBLogger())
```
-![Interactive dashboards accessible anywhere, and more!](@site/static/images/integrations/n6P7K4M.gif)
+![Interactive dashboards accessible anywhere, and more!](/images/integrations/n6P7K4M.gif)
## Using Composer's `WandBLogger`
@@ -45,7 +45,7 @@ Below the parameters for WandbLogger, see the [Composer documentation](https://d
| `tags` | W&B tags (List[str], optional)
| `log_artifacts` | Whether to log checkpoints to wandb, default: `false` (bool, optional)|
| `rank_zero_only` | Whether to log only on the rank-zero process. When logging artifacts, it is highly recommended to log on all ranks. Artifacts from ranks ≥1 are not stored, which may discard pertinent information. For example, when using Deepspeed ZeRO, it would be impossible to restore from checkpoints without artifacts from all ranks, default: `True` (bool, optional)
-| `init_kwargs` | Params to pass to `wandb.init` such as your wandb `config` etc [See here](https://docs.wandb.ai/ref/python/init) for the full list `wandb.init` accepts
+| `init_kwargs` | Params to pass to `wandb.init` such as your wandb `config` etc [See here](/ref/python/init) for the full list `wandb.init` accepts
A typical usage would be:
diff --git a/docs/guides/integrations/other/databricks.md b/docs/guides/integrations/other/databricks.md
index 98417074e..5d691367b 100644
--- a/docs/guides/integrations/other/databricks.md
+++ b/docs/guides/integrations/other/databricks.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/databricks
description: How to integrate W&B with Databricks.
displayed_sidebar: default
+title: Databricks
---
-# Databricks
-
W&B integrates with [Databricks](https://www.databricks.com/) by customizing the W&B Jupyter notebook experience in the Databricks environment.
## Databricks Configuration
diff --git a/docs/guides/integrations/other/deepchecks.md b/docs/guides/integrations/other/deepchecks.md
index e7156815e..a67517f06 100644
--- a/docs/guides/integrations/other/deepchecks.md
+++ b/docs/guides/integrations/other/deepchecks.md
@@ -2,15 +2,15 @@
slug: /guides/integrations/deepchecks
description: How to integrate W&B with DeepChecks.
displayed_sidebar: default
+title: DeepChecks
---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# DeepChecks
-
-[**Try in a Colab Notebook here →**](https://colab.research.google.com/github/deepchecks/deepchecks/blob/0.5.0-1-g5380093/docs/source/examples/guides/export\_outputs\_to\_wandb.ipynb)
+
DeepChecks helps you validate your machine learning models and data, such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models, all with with minimal effort.
-[Read more about DeepChecks and the wandb integration ->](https://docs.deepchecks.com/en/stable/examples/guides/export\_outputs\_to\_wandb.html)
+[Read more about DeepChecks and the wandb integration ->](https://docs.deepchecks.com/en/stable/examples/guides/export_outputs_to_wandb.html)
## Getting Started
diff --git a/docs/guides/integrations/other/deepchem.md b/docs/guides/integrations/other/deepchem.md
index 21ddb6b57..887b62d08 100644
--- a/docs/guides/integrations/other/deepchem.md
+++ b/docs/guides/integrations/other/deepchem.md
@@ -2,13 +2,11 @@
slug: /guides/integrations/deepchem
description: How to integrate W&B with DeepChem library.
displayed_sidebar: default
+title: DeepChem
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# DeepChem
-
The [DeepChem library](https://github.com/deepchem/deepchem) provides open source tools that democratize the use of deep-learning in drug discovery, materials science, chemistry, and biology. This W&B integration adds simple and easy-to-use experiment tracking and model checkpointing while training models using DeepChem.
## 🧪 DeepChem logging in 3 lines of code
@@ -19,7 +17,7 @@ model = TorchModel(…, wandb_logger=logger)
model.fit(…)
```
-![](@site/static/images/integrations/cd.png)
+![](/images/integrations/cd.png)
## Report & Google Colab
diff --git a/docs/guides/integrations/other/docker.md b/docs/guides/integrations/other/docker.md
index c941ea912..150786a20 100644
--- a/docs/guides/integrations/other/docker.md
+++ b/docs/guides/integrations/other/docker.md
@@ -2,13 +2,12 @@
slug: /guides/integrations/docker
description: How to integrate W&B with Docker.
displayed_sidebar: default
+title: Docker
---
-# Docker
-
## Docker Integration
-W&B can store a pointer to the Docker image that your code ran in, giving you the ability to restore a previous experiment to the exact environment it was run in. The wandb library looks for the **WANDB\_DOCKER** environment variable to persist this state. We provide a few helpers that automatically set this state.
+W&B can store a pointer to the Docker image that your code ran in, giving you the ability to restore a previous experiment to the exact environment it was run in. The wandb library looks for the **WANDB_DOCKER** environment variable to persist this state. We provide a few helpers that automatically set this state.
### Local Development
@@ -16,12 +15,12 @@ W&B can store a pointer to the Docker image that your code ran in, giving you th
### Production
-The `wandb docker-run` command is provided for production workloads. It's meant to be a drop in replacement for `nvidia-docker`. It's a simple wrapper to the `docker run` command that adds your credentials and the **WANDB\_DOCKER** environment variable to the call. If you do not pass the "--runtime" flag and `nvidia-docker` is available on the machine, this also ensures the runtime is set to nvidia.
+The `wandb docker-run` command is provided for production workloads. It's meant to be a drop in replacement for `nvidia-docker`. It's a simple wrapper to the `docker run` command that adds your credentials and the **WANDB_DOCKER** environment variable to the call. If you do not pass the "--runtime" flag and `nvidia-docker` is available on the machine, this also ensures the runtime is set to nvidia.
### Kubernetes
-If you run your training workloads in Kubernetes and the k8s API is exposed to your pod \(which is the case by default\). wandb will query the API for the digest of the docker image and automatically set the **WANDB\_DOCKER** environment variable.
+If you run your training workloads in Kubernetes and the k8s API is exposed to your pod \(which is the case by default\). wandb will query the API for the digest of the docker image and automatically set the **WANDB_DOCKER** environment variable.
## Restoring
-If a run was instrumented with the **WANDB\_DOCKER** environment variable, calling `wandb restore username/project:run_id` will checkout a new branch restoring your code then launch the exact docker image used for training pre-populated with the original command.
+If a run was instrumented with the **WANDB_DOCKER** environment variable, calling `wandb restore username/project:run_id` will checkout a new branch restoring your code then launch the exact docker image used for training pre-populated with the original command.
diff --git a/docs/guides/integrations/other/farama-gymnasium.md b/docs/guides/integrations/other/farama-gymnasium.md
index 700f4ea45..4af0401e9 100644
--- a/docs/guides/integrations/other/farama-gymnasium.md
+++ b/docs/guides/integrations/other/farama-gymnasium.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/farama-gymnasium
description: How to integrate W&B with Farama Gymnasium.
displayed_sidebar: default
+title: Farama Gymnasium
---
-# Farama Gymnasium
-
If you're using [Farama Gymnasium](https://gymnasium.farama.org/#) we will automatically log videos of your environment generated by `gymnasium.wrappers.Monitor`. Just set the `monitor_gym` keyword argument to [`wandb.init`](../../../ref/python/init.md) to `True`.
Our gymnasium integration is very light. We simply [look at the name of the video file](https://github.com/wandb/wandb/blob/c5fe3d56b155655980611d32ef09df35cd336872/wandb/integration/gym/__init__.py#LL69C67-L69C67) being logged from `gymnasium` and name it after that or fall back to `"videos"` if we don't find a match. If you want more control, you can always just manually [log a video](../../track/log/media.md).
diff --git a/docs/guides/integrations/other/hydra.md b/docs/guides/integrations/other/hydra.md
index c8a08af01..65fdca1a9 100644
--- a/docs/guides/integrations/other/hydra.md
+++ b/docs/guides/integrations/other/hydra.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/hydra
description: How to integrate W&B with Hydra.
displayed_sidebar: default
+title: Hydra
---
-# Hydra
-
> [Hydra](https://hydra.cc) is an open-source Python framework that simplifies the development of research and other complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line.
You can continue to use Hydra for configuration management while taking advantage of the power of W&B.
@@ -26,7 +25,7 @@ def run_experiment(cfg):
## Track Hyperparameters
-Hydra uses [omegaconf](https://omegaconf.readthedocs.io/en/2.1\_branch/) as the default way to interface with configuration dictionaries. `OmegaConf`'s dictionary are not a subclass of primitive dictionaries so directly passing Hydra's `Config` to `wandb.config` leads to unexpected results on the dashboard. It's necessary to convert `omegaconf.DictConfig` to the primitive `dict` type before passing to `wandb.config`.
+Hydra uses [omegaconf](https://omegaconf.readthedocs.io/en/2.1_branch/) as the default way to interface with configuration dictionaries. `OmegaConf`'s dictionary are not a subclass of primitive dictionaries so directly passing Hydra's `Config` to `wandb.config` leads to unexpected results on the dashboard. It's necessary to convert `omegaconf.DictConfig` to the primitive `dict` type before passing to `wandb.config`.
```python
@hydra.main(config_path="configs/", config_name="defaults")
@@ -90,4 +89,4 @@ Hydra supports passing extra parameters through the command line which aren't pr
$ python program.py +experiment=some_experiment
```
-You cannot sweep over such `+` configurations similar to what one does while configuring [Hydra Experiments](https://hydra.cc/docs/patterns/configuring\_experiments/). To work around this, you can initialize the experiment parameter with a default empty file and use W&B Sweep to override those empty configs on each call. For more information, read [**this W&B Report**](http://wandb.me/hydra)**.**
+You cannot sweep over such `+` configurations similar to what one does while configuring [Hydra Experiments](https://hydra.cc/docs/patterns/configuring_experiments/). To work around this, you can initialize the experiment parameter with a default empty file and use W&B Sweep to override those empty configs on each call. For more information, read [**this W&B Report**](http://wandb.me/hydra)**.**
diff --git a/docs/guides/integrations/other/ignite.md b/docs/guides/integrations/other/ignite.md
index 8e4f71dc4..3a02407ac 100644
--- a/docs/guides/integrations/other/ignite.md
+++ b/docs/guides/integrations/other/ignite.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/ignite
description: How to integrate W&B with PyTorch Ignite.
displayed_sidebar: default
+title: PyTorch Ignite
---
-# PyTorch Ignite
-
* See the resulting visualizations in this [example W&B report →](https://app.wandb.ai/example-team/pytorch-ignite-example/reports/PyTorch-Ignite-with-W%26B--Vmlldzo0NzkwMg)
* Try running the code yourself in this [example hosted notebook →](https://colab.research.google.com/drive/15e-yGOvboTzXU4pe91Jg-Yr7sae3zBOJ#scrollTo=ztVifsYAmnRr)
@@ -179,12 +178,12 @@ if __name__ == "__main__":
We get these visualizations on running the above code:
-![](https://i.imgur.com/CoBDShx.png)
+![](/images/integrations/pytorch-ignite-1.png)
-![](https://i.imgur.com/Fr6Dqd0.png)
+![](/images/integrations/pytorch-ignite-2.png)
-![](https://i.imgur.com/Fr6Dqd0.png)
+![](/images/integrations/pytorch-ignite-3.png)
-![](https://i.imgur.com/rHNPyw3.png)
+![](/images/integrations/pytorch-ignite-4.png)
Refer [Ignite Docs](https://pytorch.org/ignite/contrib/handlers.html#module-ignite.contrib.handlers.wandb_logger) for more detailed documentation
diff --git a/docs/guides/integrations/other/kubeflow-pipelines-kfp.md b/docs/guides/integrations/other/kubeflow-pipelines-kfp.md
index ac22c42bd..eb578f9fd 100644
--- a/docs/guides/integrations/other/kubeflow-pipelines-kfp.md
+++ b/docs/guides/integrations/other/kubeflow-pipelines-kfp.md
@@ -2,14 +2,11 @@
slug: /guides/integrations/kubeflow-pipelines-kfp
description: How to integrate W&B with Kubeflow Pipelines.
displayed_sidebar: default
+title: Kubeflow Pipelines (kfp)
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-
-# Kubeflow Pipelines (kfp)
-
## Overview
[Kubeflow Pipelines (kfp) ](https://www.kubeflow.org/docs/components/pipelines/introduction/)is a platform for building and deploying portable, scalable machine learning (ML) workflows based on Docker containers.
@@ -101,7 +98,7 @@ Click on any Run in the Kubeflow Pipelines UI that has been logged with W&B.
### Via the web app UI
-The web app UI has the same content as the `Visualizations` tab in Kubeflow Pipelines, but with more space! Learn [more about the web app UI here](https://docs.wandb.ai/ref/app).
+The web app UI has the same content as the `Visualizations` tab in Kubeflow Pipelines, but with more space! Learn [more about the web app UI here](/guides/app.
![View details about a particular run (and link back to the Kubeflow UI)](/images/integrations/kubeflow_pipelines.png)
@@ -109,18 +106,18 @@ The web app UI has the same content as the `Visualizations` tab in Kubeflow Pipe
### Via the Public API (for programmatic access)
-* For programmatic access, [see our Public API](https://docs.wandb.ai/ref/python/public-api).
+* For programmatic access, [see our Public API](/ref/python/public-api).
### Concept mapping from Kubeflow Pipelines to W&B
Here's a mapping of Kubeflow Pipelines concepts to W&B
-| Kubeflow Pipelines | W&B | Location in W&B |
-| ------------------ | --------------------------------------------------------- | ------------------------------------------------------------------------------------------------- |
-| Input Scalar | ``[`config`](https://docs.wandb.ai/guides/track/config)`` | [Overview tab](https://docs.wandb.ai/ref/app/pages/run-page#overview-tab) |
-| Output Scalar | ``[`summary`](https://docs.wandb.ai/guides/track/log)`` | [Overview tab](https://docs.wandb.ai/ref/app/pages/run-page#overview-tab) |
-| Input Artifact | Input Artifact | [Artifacts tab](https://docs.wandb.ai/ref/app/pages/run-page#artifacts-tab) |
-| Output Artifact | Output Artifact | [Artifacts tab](https://docs.wandb.ai/ref/app/pages/run-page#artifacts-tab) |
+| Kubeflow Pipelines | W&B | Location in W&B |
+| ------------------ | --- | --------------- |
+| Input Scalar | [`config`](/guides/track/config) | [Overview tab](/guides/app/pages/run-page#overview-tab) |
+| Output Scalar | [`summary`](/guides/track/log) | [Overview tab](/guides/app/pages/run-page#overview-tab) |
+| Input Artifact | Input Artifact | [Artifacts tab](/guides/app/pages/run-page#artifacts-tab) |
+| Output Artifact | Output Artifact | [Artifacts tab](/guides/app/pages/run-page#artifacts-tab) |
## Fine-grain logging
@@ -153,7 +150,7 @@ def train_model(
### With implicit wandb integrations
-If you're using a [framework integration we support](https://docs.wandb.ai/guides/integrations), you can also pass in the callback directly:
+If you're using a [framework integration we support](/guides/integrations), you can also pass in the callback directly:
```python
@wandb_log
diff --git a/docs/guides/integrations/other/metaflow.md b/docs/guides/integrations/other/metaflow.md
index 2aad6a2bf..a17b4dd5d 100644
--- a/docs/guides/integrations/other/metaflow.md
+++ b/docs/guides/integrations/other/metaflow.md
@@ -2,14 +2,11 @@
slug: /guides/integrations/metaflow
description: How to integrate W&B with Metaflow.
displayed_sidebar: default
+title: Metaflow
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-
-# Metaflow
-
## Overview
[Metaflow](https://docs.metaflow.org) is a framework created by [Netflix](https://netflixtechblog.com) for creating and running ML workflows.
@@ -149,7 +146,7 @@ Here's a cheatsheet:
| `datasets` |
True: Log instance variables that are a dataset
False
|
| `models` |
True: Log instance variables that are a model
False
|
| `others` |
True: Log anything else that is serializable as a pickle
False
|
-| `settings` |
wandb.Settings(...): Specify your own wandb settings for this step or flow
None: Equivalent to passing wandb.Settings()
By default, if:
settings.run_group is None, it will be set to {flow_name}/{run_id}
settings.run_job_type is None, it will be set to {run_job_type}/{step_name}
|
+| `settings` |
wandb.Settings(...): Specify your own wandb settings for this step or flow
None: Equivalent to passing wandb.Settings()
By default, if:
settings.run_group is None, it will be set to \{flow_name\}/\{run_id\}
settings.run_job_type is None, it will be set to \{run_job_type\}/\{step_name\}
|
## Frequently Asked Questions
@@ -182,4 +179,4 @@ We currently support these types:
Yes! If you have an artifact that is an output of step A and an input to step B, we automatically construct the lineage DAG for you.
-For an example of this behavior, please see this[ notebook](https://colab.research.google.com/drive/1wZG-jYzPelk8Rs2gIM3a71uEoG46u\_nG#scrollTo=DQQVaKS0TmDU) and its corresponding [W&B Artifacts page](https://wandb.ai/megatruong/metaflow\_integration/artifacts/dataset/raw\_df/7d14e6578d3f1cfc72fe/graph)
+For an example of this behavior, please see this[ notebook](https://colab.research.google.com/drive/1wZG-jYzPelk8Rs2gIM3a71uEoG46u_nG#scrollTo=DQQVaKS0TmDU) and its corresponding [W&B Artifacts page](https://wandb.ai/megatruong/metaflow_integration/artifacts/dataset/raw_df/7d14e6578d3f1cfc72fe/graph)
diff --git a/docs/guides/integrations/other/mmf.md b/docs/guides/integrations/other/mmf.md
index 92210ca62..c85ee8e70 100644
--- a/docs/guides/integrations/other/mmf.md
+++ b/docs/guides/integrations/other/mmf.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/mmf
description: How to integrate W&B with Meta AI's MMF.
displayed_sidebar: default
+title: MMF
---
-# MMF
-
The `WandbLogger` class in [Meta AI's MMF](https://github.com/facebookresearch/mmf) library will enable Weights & Biases to log the training/validation metrics, system (GPU and CPU) metrics, model checkpoints and configuration parameters.
### Current features
@@ -43,7 +42,7 @@ training:
log_model_checkpoint: true
# Additional argument values that you want to pass to wandb.init().
- # Check out the documentation at https://docs.wandb.ai/ref/python/init
+ # Check out the documentation at /ref/python/init
# to see what arguments are available, such as:
# job_type: 'train'
# tags: ['tag1', 'tag2']
diff --git a/docs/guides/integrations/other/openai-api.md b/docs/guides/integrations/other/openai-api.md
index b6a69f792..d92fdc0f2 100644
--- a/docs/guides/integrations/other/openai-api.md
+++ b/docs/guides/integrations/other/openai-api.md
@@ -2,12 +2,11 @@
slug: /guides/integrations/openai-api
description: How to use W&B with the OpenAI API.
displayed_sidebar: default
+title: OpenAI API
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-
-# OpenAI API
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
Use the Weights & Biases OpenAI API integration to log requests, responses, token counts and model metadata with 1 line of code for all OpenAI models, including fine-tuned models.
@@ -15,7 +14,7 @@ Use the Weights & Biases OpenAI API integration to log requests, responses, toke
The W&B autolog integration works with `openai <= 0.28.1`. Install the correct version of `openai` with `pip install openai==0.28.1`.
:::
-**[Try in a Colab Notebook here →](https://github.com/wandb/examples/blob/master/colabs/openai/OpenAI_API_Autologger_Quickstart.ipynb)**
+
With just 1 line of code you can now automatically log inputs and outputs from the OpenAI Python SDK to Weights & Biases!
diff --git a/docs/guides/integrations/other/openai-fine-tuning.md b/docs/guides/integrations/other/openai-fine-tuning.md
index 3937c7310..7b25bf269 100644
--- a/docs/guides/integrations/other/openai-fine-tuning.md
+++ b/docs/guides/integrations/other/openai-fine-tuning.md
@@ -2,13 +2,11 @@
slug: /guides/integrations/openai
description: How to Fine-Tune OpenAI models using W&B.
displayed_sidebar: default
+title: OpenAI Fine-Tuning
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# OpenAI Fine-Tuning
-
With Weights & Biases you can log your OpenAI GPT-3.5 or GPT-4 model's fine-tuning metrics and configuration to Weights & Biases to analyse and understand the performance of your newly fine-tuned models and share the results with your colleagues. You can check out the models that can be fine-tuned [here](https://platform.openai.com/docs/guides/fine-tuning/what-models-can-be-fine-tuned).
:::info
@@ -78,7 +76,7 @@ WandbLogger.sync(
| wait_for_job_success | Once an OpenAI fine-tuning job is started it usually takes a bit of time. To ensure that your metrics are logged to W&B as soon as the fine-tune job is finished, this setting will check every 60 seconds for the status of the fine-tune job to change to "succeeded". Once the fine-tune job is detected as being successful, the metrics will be synced automatically to W&B. Set to True by default. |
| model_artifact_name | The name of the model artifact that is logged. Defaults to `"model-metadata"`. |
| model_artifact_type | The type of the model artifact that is logged. Defaults to `"model"`. |
-| \*\*kwargs\_wandb\_init | Aany additional argument passed directly to [`wandb.init()`](../../../ref/python/init.md) |
+| \*\*kwargs_wandb_init | Aany additional argument passed directly to [`wandb.init()`](../../../ref/python/init.md) |
## Dataset Versioning and Visualization
diff --git a/docs/guides/integrations/other/openai-gym.md b/docs/guides/integrations/other/openai-gym.md
index b4c34f87a..b10f7a3c7 100644
--- a/docs/guides/integrations/other/openai-gym.md
+++ b/docs/guides/integrations/other/openai-gym.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/openai-gym
description: How to integrate W&B with OpenAI Gym.
displayed_sidebar: default
+title: OpenAI Gym
---
-# OpenAI Gym
-
:::info
"The team that has been maintaining Gym since 2021 has moved all future development to [Gymnasium](https://github.com/Farama-Foundation/Gymnasium), a drop in replacement for Gym (import gymnasium as gym), and Gym will not be receiving any future updates." ([Source](https://github.com/openai/gym#the-team-that-has-been-maintaining-gym-since-2021-has-moved-all-future-development-to-gymnasium-a-drop-in-replacement-for-gym-import-gymnasium-as-gym-and-gym-will-not-be-receiving-any-future-updates-please-switch-over-to-gymnasium-as-soon-as-youre-able-to-do-so-if-youd-like-to-read-more-about-the-story-behind-this-switch-please-check-out-this-blog-post))
@@ -14,7 +13,7 @@ Since Gym is no longer an actively maintained project, try out our integration w
If you're using [OpenAI Gym](https://gym.openai.com/) we will automatically log videos of your environment generated by `gym.wrappers.Monitor`. Just set the `monitor_gym` keyword argument to [`wandb.init`](../../../ref/python/init.md) to `True` or call `wandb.gym.monitor()`.
-Our gym integration is very light. We simply [look at the name of the video file](https://github.com/wandb/wandb/blob/master/wandb/integration/gym/\_\_init\_\_.py#L15) being logged from `gym` and name it after that or fall back to `"videos"` if we don't find a match. If you want more control, you can always just manually [log a video](../../track/log/media.md).
+Our gym integration is very light. We simply [look at the name of the video file](https://github.com/wandb/wandb/blob/master/wandb/integration/gym/__init__.py#L15) being logged from `gym` and name it after that or fall back to `"videos"` if we don't find a match. If you want more control, you can always just manually [log a video](../../track/log/media.md).
The [OpenRL Benchmark](http://wandb.me/openrl-benchmark-report) by[ CleanRL](https://github.com/vwxyzjn/cleanrl) uses this integration for its OpenAI Gym examples. You can find source code (including [the specific code used for specific runs](https://wandb.ai/cleanrl/cleanrl.benchmark/runs/2jrqfugg/code?workspace=user-costa-huang)) that demonstrates how to use gym with
diff --git a/docs/guides/integrations/other/paddledetection.md b/docs/guides/integrations/other/paddledetection.md
index 305c41f41..8e8e5ade0 100644
--- a/docs/guides/integrations/other/paddledetection.md
+++ b/docs/guides/integrations/other/paddledetection.md
@@ -2,13 +2,11 @@
slug: /guides/integrations/paddledetection
description: How to integrate W&B with PaddleDetection.
displayed_sidebar: default
+title: PaddleDetection
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# PaddleDetection
-
[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) is an end-to-end object-detection development kit based on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle). It implements varied mainstream object detection, instance segmentation, tracking and keypoint detection algorithms in modular design with configurable modules such as network components, data augmentations and losses.
PaddleDetection now comes with a built in W&B integration which logs all your training and validation metrics, as well as your model checkpoints and their corresponding metadata.
@@ -59,7 +57,7 @@ To use wandb via arguments to `train.py` in [PaddleDetection](https://github.com
* Add the `--use_wandb` flag
* The first wandb arguments must be preceded by `-o` (you only need to pass this once)
-* Each individual wandb argument must contain the prefix `wandb-` . For example any argument to be passed to [`wandb.init`](https://docs.wandb.ai/ref/python/init) would get the `wandb-` prefix
+* Each individual wandb argument must contain the prefix `wandb-` . For example any argument to be passed to [`wandb.init`](/ref/python/init) would get the `wandb-` prefix
```shell
python tools/train.py
diff --git a/docs/guides/integrations/other/paddleocr.md b/docs/guides/integrations/other/paddleocr.md
index 071593a45..4eb051d71 100644
--- a/docs/guides/integrations/other/paddleocr.md
+++ b/docs/guides/integrations/other/paddleocr.md
@@ -2,18 +2,16 @@
slug: /guides/integrations/paddleocr
description: How to integrate W&B with PaddleOCR.
displayed_sidebar: default
+title: PaddleOCR
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# PaddleOCR
-
[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice implemented in PaddlePaddle. PaddleOCR support a variety of cutting-edge algorithms related to OCR, and developed industrial solution. PaddleOCR now comes with a Weights & Biases integration for logging training and evaluation metrics along with model checkpoints with corresponding metadata.
## Example Blog & Colab
-[**Read here**](https://wandb.ai/manan-goel/text\_detection/reports/Train-and-Debug-Your-OCR-Models-with-PaddleOCR-and-W-B--VmlldzoyMDUwMDIw) to see how to train a model with PaddleOCR on the ICDAR2015 dataset. This also comes with a [**Google Colab**](https://colab.research.google.com/drive/1id2VTIQ5-M1TElAkzjzobUCdGeJeW-nV?usp=sharing) and the corresponding live W&B dashboard is available [**here**](https://wandb.ai/manan-goel/text\_detection). There is also a Chinese version of this blog here: [**W&B对您的OCR模型进行训练和调试**](https://wandb.ai/wandb\_fc/chinese/reports/W-B-OCR---VmlldzoyMDk1NzE4)
+[**Read here**](https://wandb.ai/manan-goel/text_detection/reports/Train-and-Debug-Your-OCR-Models-with-PaddleOCR-and-W-B--VmlldzoyMDUwMDIw) to see how to train a model with PaddleOCR on the ICDAR2015 dataset. This also comes with a [**Google Colab**](https://colab.research.google.com/drive/1id2VTIQ5-M1TElAkzjzobUCdGeJeW-nV?usp=sharing) and the corresponding live W&B dashboard is available [**here**](https://wandb.ai/manan-goel/text_detection). There is also a Chinese version of this blog here: [**W&B对您的OCR模型进行训练和调试**](https://wandb.ai/wandb_fc/chinese/reports/W-B-OCR---VmlldzoyMDk1NzE4)
## Using PaddleOCR with Weights & Biases
@@ -56,7 +54,7 @@ Global:
use_wandb: True
```
-Any additional, optional arguments that you might like to pass to [`wandb.init`](https://docs.wandb.ai/guides/track/launch) can also be added under the `wandb` header in the yaml file:
+Any additional, optional arguments that you might like to pass to [`wandb.init`](/guides/track/launch) can also be added under the `wandb` header in the yaml file:
```
wandb:
diff --git a/docs/guides/integrations/other/prodigy.md b/docs/guides/integrations/other/prodigy.md
index 14dde75e0..46636064f 100644
--- a/docs/guides/integrations/other/prodigy.md
+++ b/docs/guides/integrations/other/prodigy.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/prodigy
description: How to integrate W&B with Prodigy.
displayed_sidebar: default
+title: Prodigy
---
-# Prodigy
-
[Prodigy](https://prodi.gy/) is an annotation tool for creating training and evaluation data for machine learning models, error analysis, data inspection & cleaning. [W&B Tables](../../tables/tables-walkthrough.md) allow you to log, visualize, analyze, and share datasets (and more!) inside W&B.
The [W&B integration with Prodigy](https://github.com/wandb/wandb/blob/master/wandb/integration/prodigy/prodigy.py) adds simple and easy-to-use functionality to upload your Prodigy-annotated dataset directly to W&B for use with Tables.
@@ -26,9 +25,9 @@ and get visual, interactive, shareable tables like this one:
## Quickstart
-Use `wandb.integration.prodigy.upload_dataset` to upload your annotated prodigy dataset directly from the local Prodigy database to W&B in our [Table](https://docs.wandb.ai/ref/python/data-types/table) format. For more information on Prodigy, including installation & setup, please refer to the [Prodigy documentation](https://prodi.gy/docs/).
+Use `wandb.integration.prodigy.upload_dataset` to upload your annotated prodigy dataset directly from the local Prodigy database to W&B in our [Table](/ref/python/data-types/table) format. For more information on Prodigy, including installation & setup, please refer to the [Prodigy documentation](https://prodi.gy/docs/).
-W&B will automatically try to convert images and named entity fields to [`wandb.Image`](https://docs.wandb.ai/ref/python/data-types/image) and [`wandb.Html`](https://docs.wandb.ai/ref/python/data-types/html)respectively. Extra columns may be added to the resulting table to include these visualizations.
+W&B will automatically try to convert images and named entity fields to [`wandb.Image`](/ref/python/data-types/image) and [`wandb.Html`](/ref/python/data-types/html)respectively. Extra columns may be added to the resulting table to include these visualizations.
## Read through a detailed example
@@ -36,4 +35,4 @@ Explore the [Visualizing Prodigy Datasets Using W&B Tables](https://wandb.ai/ksh
## Also using spaCy?
-W&B also has an integration with spaCy, see the [docs here](https://docs.wandb.ai/guides/integrations/spacy).
+W&B also has an integration with spaCy, see the [docs here](/guides/integrations/spacy).
diff --git a/docs/guides/integrations/other/ray-tune.md b/docs/guides/integrations/other/ray-tune.md
index 1601fc689..04869d85a 100644
--- a/docs/guides/integrations/other/ray-tune.md
+++ b/docs/guides/integrations/other/ray-tune.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/ray-tune
description: How to integrate W&B with Ray Tune.
displayed_sidebar: default
+title: Ray Tune
---
-# Ray Tune
-
W&B integrates with [Ray](https://github.com/ray-project/ray) by offering two lightweight integrations.
One is the `WandbLoggerCallback`, which automatically logs metrics reported to Tune to the Wandb API. The other one is the `@wandb_mixin` decorator, which can be used with the function API. It automatically initializes the Wandb API with Tune’s training information. You can just use the Wandb API like you would normally do, e.g. using `wandb.log()` to log your training process.
@@ -59,7 +58,7 @@ analysis = tune.run(
best_trial = analysis.get_best_trial("mean_accuracy", "max", "last")
```
-## wandb\_mixin
+## wandb_mixin
```python
ray.tune.integration.wandb.wandb_mixin(func)
@@ -90,7 +89,7 @@ The content of the wandb config entry is passed to `wandb.init()` as keyword arg
Wandb’s `group`, `run_id` and `run_name` are automatically selected by Tune, but can be overwritten by filling out the respective configuration values.
-Please see here for all other valid configuration settings: [https://docs.wandb.com/library/init](https://docs.wandb.com/library/init)
+Please see the [`init()` reference](/ref/python/init/) for all other valid configuration settings.
### Example:
@@ -124,4 +123,4 @@ tune.run(
We've created a few examples for you to see how the integration works:
* [Colab](http://wandb.me/raytune-colab): A simple demo to try the integration.
-* [Dashboard](https://wandb.ai/anmolmann/ray\_tune): View dashboard generated from the example.
+* [Dashboard](https://wandb.ai/anmolmann/ray_tune): View dashboard generated from the example.
diff --git a/docs/guides/integrations/other/sagemaker.md b/docs/guides/integrations/other/sagemaker.md
index 981b29f96..79ac16993 100644
--- a/docs/guides/integrations/other/sagemaker.md
+++ b/docs/guides/integrations/other/sagemaker.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/sagemaker
description: How to integrate W&B with Amazon SageMaker.
displayed_sidebar: default
+title: SageMaker
---
-# SageMaker
-
## SageMaker Integration
W&B integrates with [Amazon SageMaker](https://aws.amazon.com/sagemaker/), automatically reading hyperparameters, grouping distributed runs, and resuming runs from checkpoints.
diff --git a/docs/guides/integrations/other/simpletransformers.md b/docs/guides/integrations/other/simpletransformers.md
index d53ba1db2..dc62917bf 100644
--- a/docs/guides/integrations/other/simpletransformers.md
+++ b/docs/guides/integrations/other/simpletransformers.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/simpletransformers
description: How to integrate W&B with the Transformers library by Hugging Face.
displayed_sidebar: default
+title: Simple Transformers
---
-# Simple Transformers
-
This library is based on the Transformers library by Hugging Face. Simple Transformers lets you quickly train and evaluate Transformer models. Only 3 lines of code are needed to initialize a model, train the model, and evaluate a model. It supports Sequence Classification, Token Classification \(NER\),Question Answering,Language Model Fine-Tuning, Language Model Training, Language Generation, T5 Model, Seq2Seq Tasks , Multi-Modal Classification and Conversational AI.
## The Weights & Biases framework
diff --git a/docs/guides/integrations/other/skorch.md b/docs/guides/integrations/other/skorch.md
index bebebd60a..0c94adc0e 100644
--- a/docs/guides/integrations/other/skorch.md
+++ b/docs/guides/integrations/other/skorch.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/skorch
description: How to integrate W&B with Skorch.
displayed_sidebar: default
+title: Skorch
---
-# Skorch
-
You can use Weights & Biases with Skorch to automatically log the model with the best performance – along with all model performance metrics, the model topology and compute resources after each epoch. Every file saved in wandb_run.dir is automatically logged to W&B servers.
See [example run](https://app.wandb.ai/borisd13/skorch/runs/s20or4ct?workspace=user-borisd13).
@@ -51,8 +50,8 @@ net.fit(X, y)
| `initialize`\(\) | \(Re-\)Set the initial state of the callback. |
| `on_batch_begin`\(net\[, X, y, training\]\) | Called at the beginning of each batch. |
| `on_batch_end`\(net\[, X, y, training\]\) | Called at the end of each batch. |
-| `on_epoch_begin`\(net\[, dataset\_train, …\]\) | Called at the beginning of each epoch. |
+| `on_epoch_begin`\(net\[, dataset_train, …\]\) | Called at the beginning of each epoch. |
| `on_epoch_end`\(net, \*\*kwargs\) | Log values from the last history step and save best model |
-| `on_grad_computed`\(net, named\_parameters\[, X, …\]\) | Called once per batch after gradients have been computed but before an update step was performed. |
+| `on_grad_computed`\(net, named_parameters\[, X, …\]\) | Called once per batch after gradients have been computed but before an update step was performed. |
| `on_train_begin`\(net, \*\*kwargs\) | Log model topology and add a hook for gradients |
| `on_train_end`\(net\[, X, y\]\) | Called at the end of training. |
diff --git a/docs/guides/integrations/other/stable-baselines-3.md b/docs/guides/integrations/other/stable-baselines-3.md
index 06bfcc9de..95947a3d4 100644
--- a/docs/guides/integrations/other/stable-baselines-3.md
+++ b/docs/guides/integrations/other/stable-baselines-3.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/stable-baselines-3
description: How to integrate W&B with Stable Baseline 3.
displayed_sidebar: default
+title: Stable Baselines 3
---
-# Stable Baselines 3
-
[Stable Baselines 3](https://github.com/DLR-RM/stable-baselines3) \(SB3\) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. W&B's SB3 integration will:
* Record metrics such as losses and episodic returns
@@ -24,7 +23,7 @@ from wandb.integration.sb3 import WandbCallback
model.learn(..., callback=WandbCallback())
```
-![](@site/static/images/integrations/stable_baselines_demo.gif)
+![](/images/integrations/stable_baselines_demo.gif)
## WandbCallback Arguments
diff --git a/docs/guides/integrations/other/w-and-b-for-julia.md b/docs/guides/integrations/other/w-and-b-for-julia.md
index 1f8834a53..3e84cfe67 100644
--- a/docs/guides/integrations/other/w-and-b-for-julia.md
+++ b/docs/guides/integrations/other/w-and-b-for-julia.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/w-and-b-for-julia
description: How to integrate W&B with Julia.
displayed_sidebar: default
+title: W&B for Julia
---
-# W&B for Julia
-
For those running machine learning experiments in the Julia programming language, a community contributor has created an unofficial set of Julia bindings called [wandb.jl](https://github.com/avik-pal/Wandb.jl) that you can use.
## Example
diff --git a/docs/guides/integrations/other/yolox.md b/docs/guides/integrations/other/yolox.md
index 2c6866fbb..3e630f5c9 100644
--- a/docs/guides/integrations/other/yolox.md
+++ b/docs/guides/integrations/other/yolox.md
@@ -2,10 +2,9 @@
slug: /guides/integrations/yolox
description: How to integrate W&B with YOLOX.
displayed_sidebar: default
+title: YOLOX
---
-# YOLOX
-
[YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) is an anchor-free version of YOLO with strong performance for object detection. YOLOX contains a Weights & Biases integration that enables you to turn on logging of training, validation and system metrics, as well as interactive validation predictions with just 1 command line argument.
## Getting Started
diff --git a/docs/guides/integrations/prompts/intro.md b/docs/guides/integrations/prompts/intro.md
index 3197bde91..db6ab5636 100644
--- a/docs/guides/integrations/prompts/intro.md
+++ b/docs/guides/integrations/prompts/intro.md
@@ -32,11 +32,11 @@ Use Traces for LLM chaining, plug-in or pipelining use cases. You can use your o
Traces consists of three main components:
-- [Trace table](https://docs.wandb.ai/guides/prompts#trace-table): Overview of the inputs and outputs of a chain.
-- [Trace timeline](https://docs.wandb.ai/guides/prompts#trace-timeline): Displays the execution flow of the chain and is color-coded according to component types.
-- [Model architecture](https://docs.wandb.ai/guides/prompts#model-architecture): View details about the structure of the chain and the parameters used to initialize each component of the chain.
+- [Trace table](#trace-table): Overview of the inputs and outputs of a chain.
+- [Trace timeline](#trace-timeline): Displays the execution flow of the chain and is color-coded according to component types.
+- [Model architecture](#model-architecture): View details about the structure of the chain and the parameters used to initialize each component of the chain.
-**Trace Table**
+#### Trace Table
The Trace Table provides an overview of the inputs and outputs of a chain. The trace table also provides information about the composition of a trace event in the chain, whether or not the chain ran successfully, and any error messages returned when running the chain.
@@ -44,7 +44,7 @@ The Trace Table provides an overview of the inputs and outputs of a chain. The t
Click on a row number on the left hand side of the Table to view the Trace Timeline for that instance of the chain.
-**Trace Timeline**
+#### Trace Timeline
The Trace Timeline view displays the execution flow of the chain and is color-coded according to component types. Select a trace event to display the inputs, outputs, and metadata of that trace.
@@ -54,11 +54,11 @@ Trace events that raise an error are outlined in red. Click on a trace event col
![Screenshot of a Trace Timeline error.](/images/prompts/trace_timeline_error.png)
-**Model Architecture**
+#### Model Architecture
The Model Architecture view provides details about the structure of the chain and the parameters used to initialize each component of the chain. Click on a trace event to learn more details about that event.
-**Evaluation**
+## Evaluation
To iterate on an application, we need a way to evaluate if it's improving. To do so, a common practice is to test it against the same dataset when there is a change. See this tutorial to learn how to evaluate LLM applications using W&B.
[Tutorial: Evaluate LLM application performance](https://github.com/wandb/examples/blob/master/colabs/prompts/prompts_evaluation.ipynb)
@@ -67,10 +67,10 @@ To iterate on an application, we need a way to evaluate if it's improving. To do
Weights and Biases also has lightweight integrations for:
-- [LangChain](https://docs.wandb.ai/guides/integrations/langchain)
-- [OpenAI API](https://docs.wandb.ai/guides/integrations/openai-api)
-- [OpenAI GPT-3.5 Fine-Tuning](https://docs.wandb.ai/guides/integrations/openai)
-- [Hugging Face Transformers](https://docs.wandb.ai/guides/integrations/huggingface)
+- [LangChain](/guides/integrations/langchain)
+- [OpenAI API](/guides/integrations/openai-api)
+- [OpenAI GPT-3.5 Fine-Tuning](/guides/integrations/openai)
+- [Hugging Face Transformers](/guides/integrations/huggingface)
## Getting Started
@@ -78,7 +78,7 @@ We recommend you go through the Prompts [Quickstart](./quickstart.md) guide, w
## Next Steps
-- Check out more detailed documentation on [Trace](https://colab.research.google.com/github/wandb/weave/blob/master/examples/prompts/trace_debugging/trace_quickstart_langchain.ipynb), or our [OpenAI](https://docs.wandb.ai/guides/prompts/openai) Integration.
+- Check out more detailed documentation on [Trace](https://colab.research.google.com/github/wandb/weave/blob/master/examples/prompts/trace_debugging/trace_quickstart_langchain.ipynb), or our [OpenAI](/guides/integrations/prompts/openai/) Integration.
- Try one of our [demo colabs](https://github.com/wandb/examples/tree/master/colabs/prompts), which offer more detailed explanations of how to use Prompts for LLMOps.
- You can use existing W&B features like Tables and Runs to track LLM application performance. See this tutorial to learn more:
[Tutorial: Evaluate LLM application performance](https://github.com/wandb/examples/blob/master/colabs/prompts/prompts_evaluation.ipynb)
diff --git a/docs/guides/integrations/prompts/openai.md b/docs/guides/integrations/prompts/openai.md
index 382daa30c..0a6eba192 100644
--- a/docs/guides/integrations/prompts/openai.md
+++ b/docs/guides/integrations/prompts/openai.md
@@ -1,9 +1,9 @@
---
description: OpenAI integrations for Weights & Biases
displayed_sidebar: default
+title: OpenAI and Weights & Biases
---
-# OpenAI and Weights & Biases
Weights & Biases has 2 OpenAI integrations to help you train better models faster and keep track of your prompts and generations:
## OpenAI ChatGPT-3.5 and GPT-4 fine-tuning
diff --git a/docs/guides/integrations/prompts/quickstart.md b/docs/guides/integrations/prompts/quickstart.md
index a769bf7c6..c18dd1478 100644
--- a/docs/guides/integrations/prompts/quickstart.md
+++ b/docs/guides/integrations/prompts/quickstart.md
@@ -1,16 +1,11 @@
---
description: The Prompts Quickstart shows how to visualise and debug the execution flow of your LLM chains and pipelines
displayed_sidebar: default
+title: "Tutorial: Visualize and debug LLMs with Prompts"
---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-
-# Prompts Quickstart
-
-[**Try in a Colab Notebook here →**](http://wandb.me/prompts-quickstart)
-
-
- Prompts Quickstart
-
+
This Quickstart guide will walk you how to use [Trace](./intro.md) to visualize and debug calls to LangChain, LlamaIndex or your own LLM Chain or Pipeline:
@@ -408,7 +403,7 @@ wandb_callback.finish()
Thats it! Now you can log your queries to your index using Weights & Biases. If you come across any issues, please file an issue on the [wandb repo](https://github.com/wandb/wandb/issues) with the tag `llamaindex`
### 6. [Optional] Save your Index data in Weights & Biaes Artifacts
-Weights & Biases [Artifacts](guides/artifacts) is a versioned data and model storage product.
+Weights & Biases [Artifacts](../../artifacts/) is a versioned data and model storage product.
By logging your index to Artifacts and then using it when needed, you can assosciate a particular version of your index with the logged Trace outputs, ensuring full visibility into what data was in your index for a particular call to your index.
diff --git a/docs/guides/integrations/pytorch-geometric.md b/docs/guides/integrations/pytorch-geometric.md
index 32bb9d431..97250c9de 100644
--- a/docs/guides/integrations/pytorch-geometric.md
+++ b/docs/guides/integrations/pytorch-geometric.md
@@ -1,12 +1,10 @@
---
displayed_sidebar: default
+title: PyTorch Geometric
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# PyTorch Geometric
-
[PyTorch Geometric](https://github.com/pyg-team/pytorch_geometric) or PyG is one of the most popular libraries for geometric deep learning and W&B works extremely well with it for visualizing graphs and tracking experiments.
## Getting Started
@@ -70,7 +68,7 @@ wandb.log({"eda/graph": wandb.Html("graph.html")})
wandb.finish()
```
-| ![This image shows the input graph as an interactive HTML visualization.](@site/static/images/integrations/pyg_graph_wandb.png) |
+| ![This image shows the input graph as an interactive HTML visualization.](/images/integrations/pyg_graph_wandb.png) |
|:--:|
| **This image shows the input graph as an interactive HTML visualization.** |
@@ -126,7 +124,7 @@ wandb.log({‘graph’: wandb.Plotly(create_vis(graph))})
wandb.finish()
```
-| ![This visualization was created using the function shown in the snippet above and longed inside a W&B Table.](@site/static/images/integrations/pyg_graph_plotly.png) |
+| ![This visualization was created using the function shown in the snippet above and longed inside a W&B Table.](/images/integrations/pyg_graph_plotly.png) |
|:--:|
| **This visualization was created using the function shown in the snippet above and longed inside a W&B Table.** |
@@ -143,7 +141,7 @@ wandb.log({
})
```
-| ![Plots from W&B showing how the hits@K metric changes over epochs for different values of K.](@site/static/images/integrations/pyg_metrics.png) |
+| ![Plots from W&B showing how the hits@K metric changes over epochs for different values of K.](/images/integrations/pyg_metrics.png) |
|:--:|
| **Plots from W&B showing how the hits@K metric changes over epochs for different values of K.** |
diff --git a/docs/guides/integrations/pytorch.md b/docs/guides/integrations/pytorch.md
index 1d60eda3b..c122c2f3f 100644
--- a/docs/guides/integrations/pytorch.md
+++ b/docs/guides/integrations/pytorch.md
@@ -1,15 +1,19 @@
---
displayed_sidebar: default
+title: PyTorch
---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# PyTorch
-
-[**Try in a Colab Notebook here →**](http://wandb.me/intro)
+
PyTorch is one of the most popular frameworks for deep learning in Python, especially among researchers. W&B provides first class support for PyTorch, from logging gradients to profiling your code on the CPU and GPU.
:::info
-Try our integration out in a [colab notebook](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/pytorch/Simple\_PyTorch\_Integration.ipynb) (with video walkthrough below) or see our [example repo](https://github.com/wandb/examples) for scripts, including one on hyperparameter optimization using [Hyperband](https://arxiv.org/abs/1603.06560) on [Fashion MNIST](https://github.com/wandb/examples/tree/master/examples/pytorch/pytorch-cnn-fashion), plus the [W&B Dashboard](https://wandb.ai/wandb/keras-fashion-mnist/runs/5z1d85qs) it generates.
+Try our integration out in a Colab notebook.
+
+
+
+You can also see our [example repo](https://github.com/wandb/examples) for scripts, including one on hyperparameter optimization using [Hyperband](https://arxiv.org/abs/1603.06560) on [Fashion MNIST](https://github.com/wandb/examples/tree/master/examples/pytorch/pytorch-cnn-fashion), plus the [W&B Dashboard](https://wandb.ai/wandb/keras-fashion-mnist/runs/5z1d85qs) it generates.
:::
:::info
-All W&B logging features are compatible with data-parallel multi-GPU training, e.g. with [PyTorch DDP](https://pytorch.org/tutorials/intermediate/ddp\_tutorial.html).
+All W&B logging features are compatible with data-parallel multi-GPU training, e.g. with [PyTorch DDP](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
:::
## Core Experiment Tracking
diff --git a/docs/guides/intro.md b/docs/guides/intro.md
index 23e7fe393..a875126fd 100644
--- a/docs/guides/intro.md
+++ b/docs/guides/intro.md
@@ -4,13 +4,12 @@ description: >-
if you are a first time user.
slug: /guides
displayed_sidebar: default
+title: What is W&B?
---
-# What is W&B?
-
Weights & Biases (W&B) is the AI developer platform, with tools for training models, fine-tuning models, and leveraging foundation models.
-![](@site/static/images/general/architecture.png)
+![](/images/general/architecture.png)
W&B consists of three major components: [Models](/guides/models.md), [Weave](https://wandb.github.io/weave/), and [Core](/guides/core.md):
diff --git a/docs/guides/launch/add-job-to-queue.md b/docs/guides/launch/add-job-to-queue.md
index dab6fd4bb..9ab6840e0 100644
--- a/docs/guides/launch/add-job-to-queue.md
+++ b/docs/guides/launch/add-job-to-queue.md
@@ -1,19 +1,16 @@
---
displayed_sidebar: default
+title: Add job to queue
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Enqueue jobs
-
The following page describes how to add launch jobs to a launch queue.
:::info
Ensure that you, or someone on your team, has already configured a launch queue. For more information, see the [Set up Launch](./setup-launch.md) page.
:::
-
-
## Add jobs to your queue
Add jobs to your queue interactively with the W&B App or programmatically with the W&B CLI.
diff --git a/docs/guides/launch/create-launch-job.md b/docs/guides/launch/create-launch-job.md
index 76897c58a..378bfd197 100644
--- a/docs/guides/launch/create-launch-job.md
+++ b/docs/guides/launch/create-launch-job.md
@@ -1,13 +1,11 @@
---
displayed_sidebar: default
+title: Create a launch job
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-
-# Create a Launch job
Launch jobs are blueprints for reproducing W&B runs. Jobs are W&B Artifacts that capture the source code, dependencies, and inputs required to execute a workload.
diff --git a/docs/guides/launch/intro.md b/docs/guides/launch/intro.md
index 20e71b2cc..18915fc42 100644
--- a/docs/guides/launch/intro.md
+++ b/docs/guides/launch/intro.md
@@ -2,12 +2,11 @@
slug: /guides/launch
description: Easily scale and manage ML jobs using W&B Launch.
displayed_sidebar: default
+title: Launch
---
import Translate, {translate} from '@docusaurus/Translate';
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# Launch
-
Easily scale training [runs](../runs/intro.md) from your desktop to a compute resource like Amazon SageMaker, Kubernetes and more with W&B Launch. Once W&B Launch is configured, you can quickly run training scripts, model evaluation suites, prepare models for production inference, and more with a few clicks and commands.
diff --git a/docs/guides/launch/job-inputs.md b/docs/guides/launch/job-inputs.md
index e6d134ddd..b62cc4e1c 100644
--- a/docs/guides/launch/job-inputs.md
+++ b/docs/guides/launch/job-inputs.md
@@ -1,11 +1,10 @@
---
displayed_sidebar: default
+title: Manage job inputs
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Manage job inputs
-
The core experience of Launch is easily experimenting with different job inputs like hyperparameters and datasets, and routing these jobs to appropriate hardware. Once a job is created, users beyond the original author can adjust these inputs via the W&B GUI or CLI. For information on how job inputs can be set when launching from the CLI or UI, see the [Enqueue jobs](./add-job-to-queue.md) guide.
This section describes how to programmatically control the inputs that can be tweaked for a job.
diff --git a/docs/guides/launch/launch-faqs.md b/docs/guides/launch/launch-faqs.md
index a032787ae..84dd15775 100644
--- a/docs/guides/launch/launch-faqs.md
+++ b/docs/guides/launch/launch-faqs.md
@@ -1,17 +1,11 @@
---
description: Answers to frequently asked question about W&B Launch.
displayed_sidebar: default
+title: Launch FAQ
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Launch FAQs
-
-
- Frequently Asked Questions About Launch
-
-
-
## Getting Started
@@ -35,7 +29,7 @@ wandb job create image -p -e
1. Create your queue before you start your agent, so that you can set your agent to point to it easily. If you don’t do this, your agent will give errors and not work until you add a queue.
2. Create a W&B service account to start up the agent, so that it's not tied to an individual user account.
- 3. Use `wandb.config` to read and write your hyperparameters, so that they can be overwritten when re-running a job. Check out [this guide](https://docs.wandb.ai/guides/launch/create-launch-job#making-your-code-job-friendly) if you use argsparse.
+ 3. Use `wandb.config` to read and write your hyperparameters, so that they can be overwritten when re-running a job. Check out [this guide](/guides/track/config/#set-the-configuration-with-argparse) if you use argsparse.
### I do not like clicking- can I use Launch without going through the UI?
@@ -143,13 +137,12 @@ Queues are scoped to a team of users. You define the owning entity when you crea
### Does Launch support parallelization? How can I limit the resources consumed by a job?
-Yes, Launch supports scaling jobs across mulitple GPUs and multiple nodes. See [this guide](https://docs.wandb.ai/tutorials/volcano) for details.
+Yes, Launch supports scaling jobs across mulitple GPUs and multiple nodes. See [this guide](/tutorials/volcano) for details.
On an inter-job level, an individual launch agent is configured with a `max_jobs` parameter that determines how many jobs that agent can run simultaneously. Additionally, you can point to as many agents as you want at a particular queue, so long as those agents are connected to an infrastructure that they can launch into.
-You can limit the CPU/GPU, memory, and other requirements at the launch queue or job run level, in the resource config. For more information about setting up queues with resource limits on Kubernetes see [here](https://docs.wandb.ai/guides/launch/kubernetes#queue-configuration).
+You can limit the CPU/GPU, memory, and other requirements at the launch queue or job run level, in the resource config. For more information about setting up queues with resource limits on Kubernetes see [here](setup-launch-kubernetes).
-
For sweeps, in the SDK you can add a block to the queue config
```yaml title="queue config"
diff --git a/docs/guides/launch/launch-queue-observability.md b/docs/guides/launch/launch-queue-observability.md
index 76eeb7afd..dbca2a6ae 100644
--- a/docs/guides/launch/launch-queue-observability.md
+++ b/docs/guides/launch/launch-queue-observability.md
@@ -1,12 +1,10 @@
---
displayed_sidebar: default
+title: Monitor launch queue
---
-# Queue monitoring dashboard (beta)
-
Use the interactive **Queue monitoring dashboard** to view when a launch queue is in heavy use or idle, visualize workloads that are running, and spot inefficient jobs. The launch queue dashboard is especially useful for deciding whether or not you are effectively using your compute hardware or cloud resources.
-
For deeper analysis, the page links to the W&B experiment tracking workspace and to external infrastructure monitoring providers like Datadog, NVIDIA Base Command, or cloud consoles.
:::info
diff --git a/docs/guides/launch/launch-terminology.md b/docs/guides/launch/launch-terminology.md
index 4e7cd27c5..377ca12b8 100644
--- a/docs/guides/launch/launch-terminology.md
+++ b/docs/guides/launch/launch-terminology.md
@@ -1,11 +1,10 @@
---
displayed_sidebar: default
+title: Launch terms and concepts
---
-# Terms and concepts
With W&B Launch, you enqueue [jobs](#launch-job) onto [queues](#launch-queue) to create runs. Jobs are python scripts instrumented with W&B. Queues hold a list of jobs to execute on a [target resource](#target-resources). [Agents](#launch-agent) pull jobs from queues and execute the jobs on target resources. W&B tracks launch jobs similarly to how W&B tracks [runs](../runs/intro.md).
-
### Launch job
A launch job is a specific type of [W&B Artifact](../artifacts/intro.md) that represents a task to complete. For example, common launch jobs include training a model or triggering a model evaluation. Job definitions include:
@@ -13,17 +12,14 @@ A launch job is a specific type of [W&B Artifact](../artifacts/intro.md) that re
- Information about the input (config parameter) and output (metrics logged).
- Information about the environment. (for example, `requirements.txt`, base `Dockerfile`).
-
There are three main kinds of job definitions:
-
| Job types | Definition | How to run this job type |
| ---------- | --------- | -------------- |
|Artifact-based (or code-based) jobs| Code and other assets are saved as a W&B artifact.| To run artifact-based jobs, Launch agent must be configured with a builder. |
|Git-based jobs| Code and other assets are cloned from a certain commit, branch, or tag in a git repository. | To run git-based jobs, Launch agent must be configured with a builder and git repository credentials. |
|Image-based jobs|Code and other assets are baked into a Docker image. | To run image-based jobs, Launch agent might need to be configured with image repository credentials. |
-
:::tip
While Launch jobs can perform activities not related to model training--for example, deploy a model to a Triton inference server--all jobs must call `wandb.init` to complete successfully. This creates a run for tracking purposes in a W&B workspace.
:::
diff --git a/docs/guides/launch/launch-view-jobs.md b/docs/guides/launch/launch-view-jobs.md
index cdb81ee66..dfc88d5db 100644
--- a/docs/guides/launch/launch-view-jobs.md
+++ b/docs/guides/launch/launch-view-jobs.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: View launch jobs
---
-# View launch jobs
-
The following page describes how to view information about launch jobs added to queues.
## View jobs
diff --git a/docs/guides/launch/setup-agent-advanced.md b/docs/guides/launch/setup-agent-advanced.md
index eb91e34bd..07aba9834 100644
--- a/docs/guides/launch/setup-agent-advanced.md
+++ b/docs/guides/launch/setup-agent-advanced.md
@@ -1,5 +1,6 @@
---
displayed_sidebar: default
+title: Set up launch agent
---
import Tabs from '@theme/Tabs';
diff --git a/docs/guides/launch/setup-launch-docker.md b/docs/guides/launch/setup-launch-docker.md
index a4ca2fb1e..ce6c760b7 100644
--- a/docs/guides/launch/setup-launch-docker.md
+++ b/docs/guides/launch/setup-launch-docker.md
@@ -1,12 +1,10 @@
---
displayed_sidebar: default
+title: "Tutorial: Set up W&B Launch with Docker"
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Set up for Docker
-
The following guide describes how to configure W&B Launch to use Docker on a local machine for both the launch agent environment and for the queue's target resource.
Using Docker to execute jobs and as the launch agent's environment on the same local machine is particularly useful if your compute is installed on a machine that does not have a cluster management system (such as Kubernetes).
diff --git a/docs/guides/launch/setup-launch-kubernetes.md b/docs/guides/launch/setup-launch-kubernetes.md
index 2aebd9071..2c21d6897 100644
--- a/docs/guides/launch/setup-launch-kubernetes.md
+++ b/docs/guides/launch/setup-launch-kubernetes.md
@@ -1,33 +1,26 @@
---
displayed_sidebar: default
+title: "Tutorial: Set up W&B Launch on Kubernetes"
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Set up for Kubernetes
-
You can use W&B Launch to push ML workloads to a Kubernetes cluster, giving ML engineers a simple interface right in W&B to use the resources you already manage with Kubernetes.
-
W&B maintains an [official Launch agent image](https://hub.docker.com/r/wandb/launch-agent) that can be deployed to your cluster with a [Helm chart](https://github.com/wandb/helm-charts/tree/main/charts/launch-agent) that W&B maintains.
-
W&B uses the [Kaniko](https://github.com/GoogleContainerTools/kaniko) builder to enable the Launch agent to build Docker images in a Kubernetes cluster. To learn more on how to set up Kaniko for the Launch agent, or how to disable job building and only use prebuilt Docker images, see [Advanced agent set up](./setup-agent-advanced.md).
-
:::note
To install Helm and apply or upgrade W&B's Launch agent Helm chart, you need `kubectl` access to the cluster with sufficient permissions to create, update, and delete Kubernetes resources. Typically, a user with cluster-admin or a custom role with equivalent permissions is required.
:::
-
## Configure a queue for Kubernetes
The Launch queue configuration for a Kubernetes target resource will resemble either a [Kubernetes Job spec](https://kubernetes.io/docs/concepts/workloads/controllers/job/) or a [Kubernetes Custom Resource spec](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/).
-
You can control any aspect of the Kubernetes workload resource spec when you create a Launch queue.
You can use W&B Launch to submit launch jobs to Amazon SageMaker to train machine learning models using provided or custom algorithms on the SageMaker platform. SageMaker takes care of spinning up and releasing compute resources, so it can be a good choice for teams without an EKS cluster.
@@ -43,7 +42,7 @@ Ensure you have the following AWS resources configured in your preferred AWS reg
2. One or more [S3 buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html) to store inputs and outputs for your SageMaker Training jobs.
3. An IAM role for Amazon SageMaker that permits SageMaker to run training jobs and interact with Amazon ECR and Amazon S3.
-Make a note of the ARNs for these resources. You will need the ARNs when you define the [Launch queue configuration](#configure-a-queue-for-sagemaker).
+Make a note of the ARNs for these resources. You will need the ARNs when you define the [Launch queue configuration](#configure-launch-queue-for-sagemaker).
@@ -51,11 +50,12 @@ Make a note of the ARNs for these resources. You will need the ARNs when you def
1. From the IAM screen in AWS, create a new policy.
2. Toggle to the JSON policy editor, then paste the following policy based on your use case. Substitute values enclosed with `<>` with your own values:
+
diff --git a/docs/guides/launch/setup-launch.md b/docs/guides/launch/setup-launch.md
index 08e95d22a..1d08dfbb3 100644
--- a/docs/guides/launch/setup-launch.md
+++ b/docs/guides/launch/setup-launch.md
@@ -1,11 +1,10 @@
---
displayed_sidebar: default
+title: Launch quickstart
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Set up Launch
-
This page describes the high-level steps required to set up W&B Launch:
1. **Set up a queue**: Queues are FIFO and possess a queue configuration. A queue's configuration controls where and how jobs are executed on a target resource.
@@ -42,7 +41,7 @@ Agents are highly flexible and can be configured to support a wide variety of us
:::
:::tip
-W&B recommends you start agents with a [service account's](https://docs.wandb.ai/guides/technical-faq/general#what-is-a-service-account-and-why-is-it-useful) API key, rather than a specific user's API key. There are two benefits to using a service account's API key:
+W&B recommends you start agents with a [service account's](/guides/technical-faq/general#what-is-a-service-account-and-why-is-it-useful) API key, rather than a specific user's API key. There are two benefits to using a service account's API key:
1. The agent isn't dependent on an individual user.
2. The author associated with a run created through Launch is viewed by Launch as the user who submitted the launch job, rather than the user associated with the agent.
:::
diff --git a/docs/guides/launch/setup-queue-advanced.md b/docs/guides/launch/setup-queue-advanced.md
index 7cc0bfd36..7c9162486 100644
--- a/docs/guides/launch/setup-queue-advanced.md
+++ b/docs/guides/launch/setup-queue-advanced.md
@@ -1,9 +1,9 @@
---
displayed_sidebar: default
+title: Configure launch queue
---
-# Advanced queue set up
-The following page describes how to configure additional launch queue options.
+The following page describes how to configure launch queue options.
## Set up queue config templates
Administer and manage guardrails on compute consumption with Queue Config Templates. Set defaults, minimums, and maximum values for fields such as memory consumption, GPU, and runtime duration.
diff --git a/docs/guides/launch/setup-vertex.md b/docs/guides/launch/setup-vertex.md
index 8d3d828ea..afe904df3 100644
--- a/docs/guides/launch/setup-vertex.md
+++ b/docs/guides/launch/setup-vertex.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: "Tutorial: Set up W&B Launch on Vertex AI"
---
-# Set up Vertex AI
-
You can use W&B Launch to submit jobs for execution as Vertex AI training jobs. With Vertex AI training jobs, you can train machine learning models using either provided, or custom algorithms on the Vertex AI platform. Once a launch job is initiated, Vertex AI manages the underlying infrastructure, scaling, and orchestration.
W&B Launch works with Vertex AI through the `CustomJob` class in the `google-cloud-aiplatform` SDK. The parameters of a `CustomJob` can be controlled with the launch queue configuration. Vertex AI cannot be configured to pull images from a private registry outside of GCP. This means that you must store container images in GCP or in a public registry if you want to use Vertex AI with W&B Launch. See the Vertex AI documentation for more information on making container images accessible to Vertex jobs.
diff --git a/docs/guides/launch/sweeps-on-launch.md b/docs/guides/launch/sweeps-on-launch.md
index 0020f1ff5..29b408e7e 100644
--- a/docs/guides/launch/sweeps-on-launch.md
+++ b/docs/guides/launch/sweeps-on-launch.md
@@ -1,13 +1,12 @@
---
description: Discover how to automate hyperparamter sweeps on launch.
displayed_sidebar: default
+title: Create sweeps with W&B Launch
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# Sweeps on Launch
-
Create a hyperparameter tuning job ([sweeps](../sweeps/intro.md)) with W&B Launch. With sweeps on launch, a sweep scheduler is pushed to a Launch Queue with the specified hyperparameters to sweep over. The sweep scheduler starts as it is picked up by the agent, launching sweep runs onto the same queue with chosen hyperparameters. This continues until the sweep finishes or is stopped.
@@ -251,7 +250,7 @@ If the initial sweep used a training job with an alias like 'latest', resuming c
1. Identify the sweep name/ID for a previously run launch sweep. The sweep ID is an eight character string (for example, `hhd16935`) that you can find in your project on the W&B App.
2. If you change the scheduler parameters, construct an updated config file.
- 3. In your terminal, execute the following command. Replace content wrapped in "<" and ">" with your information:
+ 3. In your terminal, execute the following command. Replace content wrapped in `<` and `>` with your information:
```bash
wandb launch-sweep --resume_id --queue
diff --git a/docs/guides/launch/walkthrough.md b/docs/guides/launch/walkthrough.md
index 1c7e0108c..f5bffc726 100644
--- a/docs/guides/launch/walkthrough.md
+++ b/docs/guides/launch/walkthrough.md
@@ -1,13 +1,12 @@
---
description: Getting started guide for W&B Launch.
displayed_sidebar: default
+title: "Tutorial: W&B Launch basics"
---
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Walkthrough
-
This page walks through the basics of the W&B Launch workflow.
:::tip
diff --git a/docs/guides/model_registry/access_controls.md b/docs/guides/model_registry/access_controls.md
index 9d7ac350f..4ba54ce86 100644
--- a/docs/guides/model_registry/access_controls.md
+++ b/docs/guides/model_registry/access_controls.md
@@ -1,10 +1,9 @@
---
description: Use model registry role based access controls (RBAC) to control who can update protected aliases.
displayed_sidebar: default
+title: Manage data governance and access control
---
-# Data governance and access control
-
Use *protected aliases* to represent key stages of your model development pipeline. Only *Model Registry Administrators* can add, modify, or remove protected aliases. Model registry admins can define and use protected aliases. W&B blocks non admin users from adding or removing protected aliases from model versions.
:::info
diff --git a/docs/guides/model_registry/consume-models.md b/docs/guides/model_registry/consume-models.md
index 96b677488..8182acf41 100644
--- a/docs/guides/model_registry/consume-models.md
+++ b/docs/guides/model_registry/consume-models.md
@@ -1,14 +1,12 @@
---
-description: ''
+description: 'How to download a model with W&B Python SDK'
displayed_sidebar: default
+title: Download a model version
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-
-# Download a model version
-
-Use the W&B Python SDK to download a model artifact that you linked to the Model Registry. Downloading a model is particularly useful if you want to load or consume a model in a future to evaluate a model's performance, make predictions with a dataset, or use ship the model to production.
+Use the W&B Python SDK to download a model artifact that you linked to the Model Registry.
:::info
You are responsible for providing additional Python functions, API calls to reconstruct, deserialize your model into a form that you can work with.
@@ -38,7 +36,6 @@ Reference a model version with one of following formats listed:
See [`use_model`](../../ref/python/run.md#use_model) in the API Reference guide for more information on possible parameters and return type.
-
Example: Download and use a logged model
For example, in the proceeding code snippet a user called the `use_model` API. They specified the name of the model artifact they want to fetch and they also provided a version/alias. They then stored the path that returned from the API to the `downloaded_model_path` variable.
@@ -83,11 +80,12 @@ run = wandb.init(project="", entity="")
downloaded_model_path = run.use_model(name="")
```
Reference a model version with one of following formats listed:
+
* `latest` - Use `latest` alias to specify the model version that is most recently linked.
* `v#` - Use `v0`, `v1`, `v2`, and so on to fetch a specific version in the Registered Model
* `alias` - Specify the custom alias that you and your team assigned to your model version
-See [`use_model`](../../ref/python/run.md#use_model) in the API Reference guide for more information on possible parameters and return type.
+See [`use_model`](../../ref/python/run.md#use_model) in the API Reference guide for more information on possible parameters and return type.
@@ -99,8 +97,6 @@ See [`use_model`](../../ref/python/run.md#use_model) in the API Reference guide
5. Click on the download button next to the model file you want to download.
![](/images/models/download_model_ui.gif)
-
-
diff --git a/docs/guides/model_registry/create-model-cards.md b/docs/guides/model_registry/create-model-cards.md
index 5dfc6b018..dccd09cbc 100644
--- a/docs/guides/model_registry/create-model-cards.md
+++ b/docs/guides/model_registry/create-model-cards.md
@@ -1,10 +1,9 @@
---
-description: ''
+description: 'Add descriptions to model card to document your model'
displayed_sidebar: default
+title: Document machine learning model
---
-# Document machine learning model
-
Add a description to the model card of your registered model to document aspects of your machine learning model. Some topics worth documenting include:
* **Summary**: A summary of what the model is. The purpose of the model. The machine learning framework the model uses, and so forth.
diff --git a/docs/guides/model_registry/create-registered-model.md b/docs/guides/model_registry/create-registered-model.md
index 925217387..bc3d1e075 100644
--- a/docs/guides/model_registry/create-registered-model.md
+++ b/docs/guides/model_registry/create-registered-model.md
@@ -1,12 +1,11 @@
---
-description: ''
+description: Create a registered model to hold all the candidate models for your modeling tasks.
displayed_sidebar: default
+title: Create a registered model
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Create a registered model
-
Create a [registered model](./model-management-concepts.md#registered-model) to hold all the candidate models for your modeling tasks. You can create a registered model interactively within the Model Registry or programmatically with the Python SDK.
## Programmatically create registered a model
@@ -41,7 +40,7 @@ Interactively create a registered model within the [Model Registry App](https://
:::tip
-Manual linking a model to the model registry is useful for one-off models. However, it is often useful to [programmatically link model versions to the model registry](#programmatically-link-a-model).
+Manual linking a model to the model registry is useful for one-off models. However, it is often useful to [programmatically link model versions to the model registry](link-model-version#programmatically-link-a-model).
For example, suppose you have a nightly job. It is tedious to manually link a model created each night. Instead, you could create a script that evaluates the model, and if the model improves in performance, link that model to the model registry with the W&B Python SDK.
:::
diff --git a/docs/guides/model_registry/delete-models.md b/docs/guides/model_registry/delete-models.md
index e208a2391..b410f767e 100644
--- a/docs/guides/model_registry/delete-models.md
+++ b/docs/guides/model_registry/delete-models.md
@@ -1,6 +1,5 @@
---
-description: ''
+description: How to delete models
displayed_sidebar: default
+title: Delete models
---
-
-# Delete models
diff --git a/docs/guides/model_registry/intro.md b/docs/guides/model_registry/intro.md
index d09f9e547..09a6f7923 100644
--- a/docs/guides/model_registry/intro.md
+++ b/docs/guides/model_registry/intro.md
@@ -2,10 +2,9 @@
slug: /guides/model_registry
description: Model registry to manage the model lifecycle from training to production
displayed_sidebar: default
+title: Model registry
---
-# Model registry
-
:::info
W&B will no longer support W&B Model Registry after 2024. Users are encouraged to instead use [W&B Registry](../registry/intro.md) for linking and sharing their model artifacts versions. W&B Registry broadens the capabilities of the legacy W&B Model Registry. For more information about W&B Registry, see the [Registry docs](../registry/intro.md).
diff --git a/docs/guides/model_registry/link-model-version.md b/docs/guides/model_registry/link-model-version.md
index a334d98a2..deb736e95 100644
--- a/docs/guides/model_registry/link-model-version.md
+++ b/docs/guides/model_registry/link-model-version.md
@@ -1,12 +1,11 @@
---
-description: ''
+description: Link a model version to a registered model with the W&B App or programmatically with the Python SDK.
displayed_sidebar: default
+title: Link a model version
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Link a model version
-
Link a model version to a registered model with the W&B App or programmatically with the Python SDK.
## Programmatically link a model
diff --git a/docs/guides/model_registry/log-model-to-experiment.md b/docs/guides/model_registry/log-model-to-experiment.md
index 0e392f37a..1f3a972be 100644
--- a/docs/guides/model_registry/log-model-to-experiment.md
+++ b/docs/guides/model_registry/log-model-to-experiment.md
@@ -1,12 +1,11 @@
---
-description: ''
+description: Track a model, the model's dependencies, and other information relevant to that model with the W&B Python SDK.
displayed_sidebar: default
+title: Track a model
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Track a model
-
Track a model, the model's dependencies, and other information relevant to that model with the W&B Python SDK.
Under the hood, W&B creates a lineage of [model artifact](./model-management-concepts.md#model-artifact) that you can view with the W&B App UI or programmatically with the W&B Python SDK. See the [Create model lineage map](./model-lineage.md) for more information.
diff --git a/docs/guides/model_registry/model-lineage.md b/docs/guides/model_registry/model-lineage.md
index f67346002..0573743d5 100644
--- a/docs/guides/model_registry/model-lineage.md
+++ b/docs/guides/model_registry/model-lineage.md
@@ -1,10 +1,8 @@
---
description: ''
displayed_sidebar: default
+title: Create model lineage map
---
-
-
-# Create model lineage map
A useful feature of logging model artifacts to W&B are lineage graphs. Lineage graphs show artifacts logged by a run as well as artifacts used by specific run.
This means that, when you log a model artifact, you at a minimum have access to view the W&B run that used or produced the model artifact. If you [track a dependency](#track-an-artifact-dependency), you also see the inputs used by the model artifact.
diff --git a/docs/guides/model_registry/model-management-concepts.md b/docs/guides/model_registry/model-management-concepts.md
index eb8df8dee..0bb48a76b 100644
--- a/docs/guides/model_registry/model-management-concepts.md
+++ b/docs/guides/model_registry/model-management-concepts.md
@@ -1,14 +1,9 @@
---
description: 'Model Registry terms and concepts'
displayed_sidebar: default
+title: Model Registry Terms and Concepts
---
-# Terms and concepts
-
-
- Model Registry terms and concepts
-
-
The following terms describe key components of the W&B Model Registry: [*model version*](#model-version), [*model artifact*](#model-artifact), and [*registered model*](#registered-model).
## Model version
@@ -18,7 +13,7 @@ A model version is an immutable directory of data and metadata that describes a
A model version belongs to one, and only one, [model artifact](#model-artifact). A model version can belong to zero or more, [registered models](#registered-model). Model versions are stored in a model artifact in the order they are logged to the model artifact. W&B automatically creates a new model version if it detects that a model you log (to the same model artifact) has different contents than a previous model version.
-Store files within model versions that are produced from the serialization process provided by your modeling library (for example, [PyTorch](https://pytorch.org/tutorials/beginner/saving\_loading\_models.html) and [Keras](https://www.tensorflow.org/guide/keras/save\_and\_serialize)).
+Store files within model versions that are produced from the serialization process provided by your modeling library (for example, [PyTorch](https://pytorch.org/tutorials/beginner/saving_loading_models.html) and [Keras](https://www.tensorflow.org/guide/keras/save_and_serialize)).
@@ -59,9 +54,9 @@ For example, suppose you create a model artifact. During model training, you per
The proceeding image shows a model artifact that contains three model versions: v0, v1, and v2.
-![](@site/static/images/models/mr1c.png)
+![](/images/models/mr1c.png)
-View an [example model artifact here](https://wandb.ai/timssweeney/model\_management\_docs\_official\_v0/artifacts/model/mnist-zws7gt0n).
+View an [example model artifact here](https://wandb.ai/timssweeney/model_management_docs_official_v0/artifacts/model/mnist-zws7gt0n).
## Registered model
A registered model is a collection of pointers (links) to model versions. You can think of a registered model as a folder of "bookmarks" of candidate models for the same ML task. Each "bookmark" of a registered model is a pointer to a [model version](#model-version) that belongs to a [model artifact](#model-artifact). You can use [model tags](#model-tags) to group your registered models.
diff --git a/docs/guides/model_registry/model-registry-automations.md b/docs/guides/model_registry/model-registry-automations.md
index 1aad5f095..07507a180 100644
--- a/docs/guides/model_registry/model-registry-automations.md
+++ b/docs/guides/model_registry/model-registry-automations.md
@@ -6,10 +6,6 @@ displayed_sidebar: default
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-
-
-# Triggering CI/CD events with model registry changes
-
Create an automation to trigger workflow steps, such as automated model testing and deployment. To create an automation, define the [action](#action-types) you want to occur based on an [event type](#event-types).
For example, you can create a trigger that automatically deploys a model to GitHub when you add a new version of a registered model.
diff --git a/docs/guides/model_registry/notifications.md b/docs/guides/model_registry/notifications.md
index 0bd8d91e8..b60b37ea0 100644
--- a/docs/guides/model_registry/notifications.md
+++ b/docs/guides/model_registry/notifications.md
@@ -1,10 +1,9 @@
---
description: Get Slack notifications when a new model version is linked to the model registry.
displayed_sidebar: default
+title: Create alerts and notifications
---
-# Create alerts and notifications
-
Receive Slack notifications when a new model version is linked to the model registry.
diff --git a/docs/guides/model_registry/organize-models.md b/docs/guides/model_registry/organize-models.md
index 4d25799b2..8f5d54aa9 100644
--- a/docs/guides/model_registry/organize-models.md
+++ b/docs/guides/model_registry/organize-models.md
@@ -1,7 +1,7 @@
---
displayed_sidebar: default
+title: Organize models
---
-# Organize models
Use model tags to organize registered models into categories and to search over those categories.
diff --git a/docs/guides/model_registry/walkthrough.md b/docs/guides/model_registry/walkthrough.md
index 2b6f975b1..64bf5a3dd 100644
--- a/docs/guides/model_registry/walkthrough.md
+++ b/docs/guides/model_registry/walkthrough.md
@@ -1,12 +1,11 @@
---
description: Learn how to use W&B for Model Management
displayed_sidebar: default
+title: "Tutorial: Use W&B for model management"
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Walkthrough
-
The following walkthrough shows you how to log a model to W&B. By the end of the walkthrough you will:
* Create and train a model with the MNIST dataset and the Keras framework.
diff --git a/docs/guides/models.md b/docs/guides/models.md
index 8e67519b3..c0e8c2b9a 100644
--- a/docs/guides/models.md
+++ b/docs/guides/models.md
@@ -1,12 +1,11 @@
---
displayed_sidebar: default
+title: W&B Models
---
-# W&B Models
-
W&B Models is the system of record for ML Practitioners who want to organize their models, boost productivity and collaboration, and deliver production ML at scale.
-![](@site/static/images/general/models.png)
+![](/images/general/models.png)
With W&B Models, you can:
diff --git a/docs/guides/registry/configure_registry.md b/docs/guides/registry/configure_registry.md
index e44d030e5..a718dd358 100644
--- a/docs/guides/registry/configure_registry.md
+++ b/docs/guides/registry/configure_registry.md
@@ -1,8 +1,7 @@
---
displayed_sidebar: default
+title: Configure registry access
---
-
-# Configure registry access
Registry admins can limit who can access a registry by navigating to a registry's settings and assigning a user's role to [Admin, Member, or Viewer](#registry-roles-permissions). Users can have different roles in different registries. For example, a user can have a view role in "Registry A" and a member role in the "Registry B".
diff --git a/docs/guides/registry/create_collection.md b/docs/guides/registry/create_collection.md
index 3b24cb1b7..f6d56d76b 100644
--- a/docs/guides/registry/create_collection.md
+++ b/docs/guides/registry/create_collection.md
@@ -1,11 +1,10 @@
---
displayed_sidebar: default
+title: Create a collection
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Create a collection
-
Create a collection within a registry to organize your artifacts. A *collection* is a set of linked artifact versions in a registry. Each collection represents a distinct task or use case and serves as a container for a curated selection of artifact versions related to that task.
diff --git a/docs/guides/registry/create_registry.md b/docs/guides/registry/create_registry.md
index 98c49bec1..5930882a0 100644
--- a/docs/guides/registry/create_registry.md
+++ b/docs/guides/registry/create_registry.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Create a custom registry
---
-# Create a custom registry
-
Create a [custom registry](./registry_types.md#custom-registry) for each step of your ML workflow.
Custom registries are particularly useful for organizing project-specific requirements that differ from the default, [core registry](./registry_types.md#core-registry).
diff --git a/docs/guides/registry/download_use_artifact.md b/docs/guides/registry/download_use_artifact.md
index 092684a22..05040e2bd 100644
--- a/docs/guides/registry/download_use_artifact.md
+++ b/docs/guides/registry/download_use_artifact.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Download and use an artifact from a registry
---
-# Download and use an artifact from a registry
-
Use the W&B Python SDK to use and download an artifact that you linked to the W&B Registry.
:::note
@@ -42,7 +41,7 @@ Where:
* `v#` - Use `v0`, `v1`, `v2`, and so on to fetch a specific version in the collection.
* `alias` - Specify the custom alias attached to the artifact version
-See [`use_artifact`](../../ref/python/run.md#use_artifact) and [`download`](https://docs.wandb.ai/ref/python/artifact#download) in the API Reference guide for more information on possible parameters and return type.
+See [`use_artifact`](../../ref/python/run.md#use_artifact) and [`download`](/ref/python/artifact#download) in the API Reference guide for more information on possible parameters and return type.
Example: Use and download an artifact linked to the W&B Registry
diff --git a/docs/guides/registry/intro.md b/docs/guides/registry/intro.md
index 883b57cdc..79c86682f 100644
--- a/docs/guides/registry/intro.md
+++ b/docs/guides/registry/intro.md
@@ -1,10 +1,9 @@
---
slug: /guides/registry
displayed_sidebar: default
+title: Registry
---
-# Registry
-
:::info
W&B Registry is now in public preview. Visit [this](#enable-wb-registry) section to learn how to enable it for your deployment type.
:::
@@ -51,7 +50,7 @@ run.link_artifact(artifact=logged_artifact, target_path=f"/wand
run.finish()
```
-See learn more about linking to a registry, visit [this](https://docs.wandb.ai/guides/registry/link_version) guide.
+See learn more about linking to a registry, visit [this](/guides/registry/link_version) guide.
## Enable W&B Registry
@@ -61,7 +60,7 @@ Based on your deployment type, satisfy the following conditions to enable W&B Re
| ----- | ----- |
| Multi-tenant Cloud | No action required. W&B Registry is available on the W&B App. |
| Dedicated Cloud | Contact your account team. The Solutions Architect (SA) Team enables W&B Registry within your instance's operator console. Ensure your instance is on server release version 0.59.2 or newer.|
-| Self-Managed | Enable the environment variable called `ENABLE_REGISTRY_UI`. To learn more about enabling environment variables in server, visit [these docs](https://docs.wandb.ai/guides/hosting/env-vars). In self-managed instances, your infrastructure administrator should enable this environment variable and set it to `true`. Ensure your instance is on server release version 0.59.2 or newer.|
+| Self-Managed | Enable the environment variable called `ENABLE_REGISTRY_UI`. To learn more about enabling environment variables in server, visit [these docs](/guides/hosting/env-vars). In self-managed instances, your infrastructure administrator should enable this environment variable and set it to `true`. Ensure your instance is on server release version 0.59.2 or newer.|
## Resources to get started
diff --git a/docs/guides/registry/link_version.md b/docs/guides/registry/link_version.md
index 8686b1f18..40ae0bc00 100644
--- a/docs/guides/registry/link_version.md
+++ b/docs/guides/registry/link_version.md
@@ -1,11 +1,10 @@
---
displayed_sidebar: default
+title: Link an artifact version to a registry
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Link an artifact version to a registry
-
Programmatically or interactively link artifact versions to a registry.
W&B recommends that you check the artifact types that the registry permits. Each registry controls the types of artifacts that can be linked to that registry.
@@ -159,7 +158,7 @@ You can confirm the name of your team by:
2. Copy the site's URL. It has the form of `https://wandb.ai/`. Where `` is the both the name of your team and the team's entity.
#### Log from a team entity
-1. Specify the team as the entity when you initialize a run with [`wandb.init()`](https://docs.wandb.ai/ref/python/init). If you do not specify the `entity` when you initialize a run, the run uses your default entity which may or may not be your team entity.
+1. Specify the team as the entity when you initialize a run with [`wandb.init()`](/ref/python/init). If you do not specify the `entity` when you initialize a run, the run uses your default entity which may or may not be your team entity.
```python
import wandb
diff --git a/docs/guides/registry/model_registry_eol.md b/docs/guides/registry/model_registry_eol.md
index a7ada8462..ded743022 100644
--- a/docs/guides/registry/model_registry_eol.md
+++ b/docs/guides/registry/model_registry_eol.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Migrate from legacy Model Registry
---
-# Migrating from legacy Model Registry
-
W&B will transition assets from the legacy [W&B Model Registry](../model_registry/intro.md) to the new [W&B Registry](./intro.md). This migration will be fully managed and triggered by W&B, requiring no intervention from users. The process is designed to be as seamless as possible, with minimal disruption to existing workflows.
The transition will take place once the new W&B Registry includes all the functionalities currently available in the Model Registry. W&B will attempt to preserve current workflows, codebases, and references.
diff --git a/docs/guides/registry/organize-with-tags.md b/docs/guides/registry/organize-with-tags.md
index acc1fced3..0cce240ea 100644
--- a/docs/guides/registry/organize-with-tags.md
+++ b/docs/guides/registry/organize-with-tags.md
@@ -1,13 +1,11 @@
---
displayed_sidebar: default
description: Use tags to organize collections or artifact versions within collections. You can add, remove, edit tags with the Python SDK or W&B App UI.
+title: Organize versions with tags
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-
-# Organize collections or artifact versions with tags
-
Create and add tags to organize your collections or artifact versions within your registry. Add, modify, view, or remove tags to a collection or artifact version with the W&B App UI or the W&B Python SDK.
:::tip When to use a tag versus using an alias
@@ -311,4 +309,23 @@ Use the W&B App UI to search existing tags in collections and artifact versions:
2. Click on a registry card
3. Within the search bar, type in the name of a tag
-![](/images/registry/search_tags.gif)
\ No newline at end of file
+![](/images/registry/search_tags.gif)
+
+
+## Find artifact versions with a specific tag
+
+Use the W&B Python SDK to find artifact versions that have a set of tags:
+
+```python
+import wandb
+
+api = wandb.Api()
+tagged_artifact_versions = api.artifacts(
+ type_name = "",
+ name = "",
+ tags = ["", ""]
+)
+
+for artifact_version in tagged_artifact_versions:
+ print(artifact_version.tags)
+```
diff --git a/docs/guides/registry/registry_types.md b/docs/guides/registry/registry_types.md
index 6bcfca549..e1eabfe67 100644
--- a/docs/guides/registry/registry_types.md
+++ b/docs/guides/registry/registry_types.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Registry types
---
-# Registry types
-
W&B supports two types of registries: [Core registries](#core-registry) and [Custom registries](#custom-registry).
## Core registry
diff --git a/docs/guides/reports/clone-and-export-reports.md b/docs/guides/reports/clone-and-export-reports.md
index b5e17103a..f96a5b799 100644
--- a/docs/guides/reports/clone-and-export-reports.md
+++ b/docs/guides/reports/clone-and-export-reports.md
@@ -1,15 +1,11 @@
---
description: Export a W&B Report as a PDF or LaTeX.
displayed_sidebar: default
+title: Clone and export reports
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-
-# Clone and export reports
-
-
- Clone and export W&B Reports
-
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
## Export reports
@@ -27,13 +23,13 @@ Export a report as a PDF or LaTeX. Within your report, select the kebab icon to
Within your report, select the kebab icon to expand the dropdown menu. Choose the **Clone this report** button. Pick a destination for your cloned report in the modal. Choose **Clone report**.
-![](@site/static/images/reports/clone_reports.gif)
+![](/images/reports/clone_reports.gif)
Clone a report to reuse a project's template and format. Cloned projects are visible to your team if you clone a project within the team's account. Projects cloned within an individual's account are only visible to that user.
-[**Try in a Colab Notebook here →**](http://wandb.me/report\_api)
+
Load a Report from a URL to use it as a template.
diff --git a/docs/guides/reports/collaborate-on-reports.md b/docs/guides/reports/collaborate-on-reports.md
index 525e8c271..43ea50cf7 100644
--- a/docs/guides/reports/collaborate-on-reports.md
+++ b/docs/guides/reports/collaborate-on-reports.md
@@ -1,19 +1,14 @@
---
description: Collaborate and share W&B Reports with peers, co-workers, and your team.
displayed_sidebar: default
+title: Collaborate on reports
---
-# Collaborate on reports
-
-
- Collaborate and Share W&B Reports
-
-
Once you have saved a report, you can select the **Share** button to collaborate. A draft copy of the report is created when you select the **Edit** button. Draft reports auto-save. Select **Save to report** to publish your changes to the shared report.
A warning notification will appear if an edit conflict occurs. This can occur if you and another collaborator edit the same report at the same time. The warning notification will guide you to resolve potential edit conflicts.
-![Report sharing modal for a report in a 'Public' project](@site/static/images/reports/share-report.gif)
+![Report sharing modal for a report in a 'Public' project](/images/reports/share-report.gif)
### Comment on reports
diff --git a/docs/guides/reports/create-a-report.md b/docs/guides/reports/create-a-report.md
index 401733588..30eda780e 100644
--- a/docs/guides/reports/create-a-report.md
+++ b/docs/guides/reports/create-a-report.md
@@ -3,16 +3,11 @@ description: >-
Create a W&B Report with the App UI or programmatically with the Weights &
Biases SDK.
displayed_sidebar: default
+title: Create a report
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Create a report
-
-
- Create a W&B Report
-
-
Create a report interactively with the W&B App UI or programmatically with the W&B Python SDK.
:::info
@@ -62,7 +57,7 @@ pip install wandb wandb-workspaces
import wandb
import wandb_workspaces.reports.v2 as wr
```
-3. Create a report with `wandb_workspaces.reports.v2.Report`. Create a report instance with the Report Class Public API ([`wandb.apis.reports`](https://docs.wandb.ai/ref/python/public-api/api#reports)). Specify a name for the project.
+3. Create a report with `wandb_workspaces.reports.v2.Report`. Create a report instance with the Report Class Public API ([`wandb.apis.reports`](/ref/python/public-api/api#reports)). Specify a name for the project.
```python
@@ -75,6 +70,6 @@ report = wr.Report(project="report_standard")
report.save()
```
-For information on how to edit a report interactively with the App UI or programmatically, see [Edit a report](https://docs.wandb.ai/guides/reports/edit-a-report).
+For information on how to edit a report interactively with the App UI or programmatically, see [Edit a report](/guides/reports/edit-a-report).
diff --git a/docs/guides/reports/cross-project-reports.md b/docs/guides/reports/cross-project-reports.md
index a062e11f6..139270eea 100644
--- a/docs/guides/reports/cross-project-reports.md
+++ b/docs/guides/reports/cross-project-reports.md
@@ -1,15 +1,9 @@
---
description: Compare runs from two different projects with cross-project reports.
displayed_sidebar: default
+title: Compare runs across projects
---
-# Cross-project reports
-
-
- Compare runs from two different projects.
-
-
-
Compare runs from two different projects with cross-project reports. Use the project selector in the run set table to pick a project.
![Compare runs across different projects](/images/reports/howto_pick_a_different_project_to_draw_runs_from.gif)
@@ -24,7 +18,7 @@ If you need to compare runs from two projects and the columns are not working, a
Share a view-only link to a report that is in a private project or team project.
-![](@site/static/images/reports/magic-links.gif)
+![](/images/reports/magic-links.gif)
View-only report links add a secret access token to the URL, so anyone who opens the link can view the page. The magic link can also be used to let anyone view the report without logging in first. For customers on [W&B Local](../hosting/intro.md) private cloud installations, these links will still be behind your firewall, so only members of your team with access to your private instance _and_ access to the view-only link will be able to view the report.
diff --git a/docs/guides/reports/edit-a-report.md b/docs/guides/reports/edit-a-report.md
index adda81a82..0e77a1566 100644
--- a/docs/guides/reports/edit-a-report.md
+++ b/docs/guides/reports/edit-a-report.md
@@ -3,19 +3,11 @@ description: >-
Edit a report interactively with the App UI or programmatically with the
W&B SDK.
displayed_sidebar: default
+title: Edit a report
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-
-# Edit a report
-
-
- Edit a W&B Report
-
-
-
Edit a report interactively with the App UI or programmatically with the W&B SDK.
Reports consist of _blocks_. Blocks make up the body of a report. Within these blocks you can add text, images, embedded visualizations, plots from experiments and run, and panels grids.
@@ -402,10 +394,10 @@ Highlight a whole panel grid section by selecting the drag handle in the upper r
Select a panel grid and press `delete` on your keyboard to delete a panel grid.
-![](@site/static/images/reports/delete_panel_grid.gif)
+![](/images/reports/delete_panel_grid.gif)
### Collapse headers to organize Reports
Collapse headers in a Report to hide content within a text block. When the report is loaded, only headers that are expanded will show content. Collapsing headers in reports can help organize your content and prevent excessive data loading. The proceeding gif demonstrates the process.
-![](@site/static/images/reports/collapse_headers.gif)
+![](/images/reports/collapse_headers.gif)
diff --git a/docs/guides/reports/embed-reports.md b/docs/guides/reports/embed-reports.md
index c5d8f1b23..bef2aae57 100644
--- a/docs/guides/reports/embed-reports.md
+++ b/docs/guides/reports/embed-reports.md
@@ -3,15 +3,9 @@ description: >-
Embed W&B reports directly into Notion or with an HTML IFrame
element.
displayed_sidebar: default
+title: Embed a report
---
-# Embed reports
-
-
- Embed reports into popular applications.
-
-
-
### HTML iframe element
Select the **Share** button on the upper right hand corner within a report. A modal window will appear. Within the modal window, select **Copy embed code**. The copied code will render within an Inline Frame (IFrame) HTML element. Paste the copied code into an iframe HTML element of your choice.
diff --git a/docs/guides/reports/intro.md b/docs/guides/reports/intro.md
index 898c3dbb2..875e7ba8c 100644
--- a/docs/guides/reports/intro.md
+++ b/docs/guides/reports/intro.md
@@ -2,12 +2,11 @@
slug: /guides/reports
description: Project management and collaboration tools for machine learning projects
displayed_sidebar: default
+title: Reports
---
import Translate, {translate} from '@docusaurus/Translate';
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# Collaborative Reports
-
Use W&B Reports to organize Runs, embed and automate visualizations, describe your findings, and share updates with collaborators. Easily export your report as a LaTeX zip file or convert the file to PDF.
@@ -39,7 +38,7 @@ See the [Create a report](./create-a-report.md) page for more information on how
## How to get started
Depending on your use case, explore the following resources to get started with W&B Reports:
-* Check out our [video demonstration](https://www.youtube.com/watch?v=2xeJIv\_K\_eI) to get an overview of W&B Reports.
+* Check out our [video demonstration](https://www.youtube.com/watch?v=2xeJIv_K_eI) to get an overview of W&B Reports.
* Explore the [Reports gallery](./reports-gallery.md) for examples of live reports.
* Try the [Programmatic Workspaces](../../tutorials/workspaces.md) tutorial to learn how to create and customize your workspace.
* Read curated Reports in [W&B Fully Connected](http://wandb.me/fc).
diff --git a/docs/guides/reports/reports-faq.md b/docs/guides/reports/reports-faq.md
index 91fe9ca06..b788ef05e 100644
--- a/docs/guides/reports/reports-faq.md
+++ b/docs/guides/reports/reports-faq.md
@@ -1,14 +1,9 @@
---
description: Answers to frequently asked question about W&B Reports.
displayed_sidebar: default
+title: Reports FAQ
---
-# Reports FAQ
-
-
- Frequently Asked Questions About Reports
-
-
### Upload a CSV to a report
If you currently want to upload a CSV to a report you can do it via the `wandb.Table` format. Loading the CSV in your Python script and logging it as a `wandb.Table` object will allow you to render the data as a table in a report.
@@ -27,7 +22,7 @@ Reload the page to refresh data in a report and get the latest results from your
Using the search bar, you can search and filter down the reports list. You can then delete an unwanted report from the list or select all and press 'Delete Reports' to remove them from your project.
-![Delete unwanted reports and drafts](@site/static/images/reports/delete_runs.gif)
+![Delete unwanted reports and drafts](/images/reports/delete_runs.gif)
### Incorporating LaTeX
@@ -59,13 +54,13 @@ A comprehensive list of LaTeX can be found [here](https://en.wikibooks.org/wiki/
When multiple users work on a report together, they should be credited as such. In our reports, you can do just that and add all the users that worked together on a report in the Author line. This is done by clicking Edit on the top right of the page. From there, a '+' will be seen on the right of the author line to where you can add all of the team members that have contributed to the report.
-![](@site/static/images/reports/reports_faq_add_multiple_reports.gif)
+![](/images/reports/reports_faq_add_multiple_reports.gif)
### Embedding Reports
You are able to share the report that you have created by embedding it. This is done simply by clicking Share on the top right of your report and copying the embedded code at the bottom of the pop-up window that appears.
-![](@site/static/images/reports/emgedding_reports.gif)
+![](/images/reports/emgedding_reports.gif)
### WYSIWYG FAQ
@@ -93,13 +88,13 @@ Absolutely! Type "/mark" anywhere in the document and hit enter to insert a Mark
Sorry! We're constantly working on performance improvements, but WYSIWYG reports may run slowly on older hardware or exceptionally large reports. You can assuage the problem for now by collapsing sections of the report that you're not currently working on, like so:
-![](@site/static/images/reports/wandb-reports-editor-1.gif)
+![](/images/reports/wandb-reports-editor-1.gif)
**How do I delete a panel grid?**
Select the panel grid, and hit delete/backspace. The easiest way to select a panel grid is by clicking its drag handle on the top-right, like so:
-![](@site/static/images/reports/wandb-reports-editor-3.gif)
+![](/images/reports/wandb-reports-editor-3.gif)
**How do I insert a table?**
diff --git a/docs/guides/reports/reports-gallery.md b/docs/guides/reports/reports-gallery.md
index c12f6b71c..ebcdf9f11 100644
--- a/docs/guides/reports/reports-gallery.md
+++ b/docs/guides/reports/reports-gallery.md
@@ -1,9 +1,9 @@
---
description: Reports gallery
displayed_sidebar: default
+title: Example reports
---
-# Reports gallery
### Notes: Add a visualization with a quick summary
Capture an important observation, an idea for future work, or a milestone reached in the development of a project. All experiment runs in your report will link to their parameters, metrics, logs, and code, so you can save the full context of your work.
diff --git a/docs/guides/runs/alert.md b/docs/guides/runs/alert.md
index 16828e3f3..d11f68393 100644
--- a/docs/guides/runs/alert.md
+++ b/docs/guides/runs/alert.md
@@ -1,15 +1,10 @@
---
description: Send alerts, triggered from your Python code, to your Slack or email
displayed_sidebar: default
+title: Send an alert
---
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# Send alerts
-
-
- Send Alerts from your Python Code
-
-
Create alerts with Slack or email if your run crashes or with a custom trigger. For example, you can create an alert if the gradient of your training loop starts to blow up (reports NaN) or a step in your ML pipeline completes. Alerts apply to all projects where you initialize runs, including both personal and team projects.
diff --git a/docs/guides/runs/forking.md b/docs/guides/runs/forking.md
index 7920388f2..975669566 100644
--- a/docs/guides/runs/forking.md
+++ b/docs/guides/runs/forking.md
@@ -1,9 +1,9 @@
---
description: Forking a W&B run
displayed_sidebar: default
+title: Fork a run
---
-# Fork from a run
:::caution
The ability to fork a run is in private preview. Contact W&B Support at support@wandb.com to request access to this feature.
:::
@@ -14,7 +14,7 @@ Forking a run enables you to explore different parameters or models from a speci
:::info
* Forking a run requires [`wandb`](https://pypi.org/project/wandb/) SDK version >= 0.16.5
-* Forking a run requires monotonically increasing steps. You can not use non-monotonic steps defined with [`define_metric()`](https://docs.wandb.ai/ref/python/run#define_metric) to set a fork point because it would disrupt the essential chronological order of run history and system metrics.
+* Forking a run requires monotonically increasing steps. You can not use non-monotonic steps defined with [`define_metric()`](/ref/python/run#define_metric) to set a fork point because it would disrupt the essential chronological order of run history and system metrics.
:::
@@ -42,7 +42,7 @@ forked_run = wandb.init(
Use an immutable run ID to ensure you have a consistent and unchanging reference to a specific run. Follow these steps to obtain the immutable run ID from the user interface:
-1. **Access the Overview Tab:** Navigate to the [**Overview tab**](https://docs.wandb.ai/guides/app/pages/run-page#overview-tab) on the source run's page.
+1. **Access the Overview Tab:** Navigate to the [**Overview tab**](/guides/app/pages/run-page#overview-tab) on the source run's page.
2. **Copy the Immutable Run ID:** Click on the `...` menu (three dots) located in the top-right corner of the **Overview** tab. Select the `Copy Immutable Run ID` option from the dropdown menu.
@@ -84,7 +84,7 @@ run2.finish()
```
:::tip Rewind and forking compatibility
-Forking compliments a [`rewind`](https://docs.wandb.ai/guides/runs/rewind) by providing more flexibility in managing and experimenting with your runs.
+Forking compliments a [`rewind`](/guides/runs/rewind) by providing more flexibility in managing and experimenting with your runs.
When you fork from a run, W&B creates a new branch off a run at a specific point to try different parameters or models.
diff --git a/docs/guides/runs/grouping.md b/docs/guides/runs/grouping.md
index 1c3051728..5e926fcdf 100644
--- a/docs/guides/runs/grouping.md
+++ b/docs/guides/runs/grouping.md
@@ -1,15 +1,9 @@
---
description: Group training and evaluation runs into larger experiments
displayed_sidebar: default
+title: Group runs into experiments
---
-# Group runs
-
-
- Group W&B Runs
-
-
-
Group individual jobs into experiments by passing a unique **group** name to **wandb.init()**.
## Use cases
@@ -38,7 +32,7 @@ Suppose you set grouping in `wandb.init()`, we will group runs by default in the
![](/images/track/distributed_training_wgrouping_1.png)
-From the project page above, you can click a **Group** in the left sidebar to get to a dedicated page like [this one](https://wandb.ai/carey/group-demo/groups/exp\_5?workspace=user-carey):
+From the project page above, you can click a **Group** in the left sidebar to get to a dedicated page like [this one](https://wandb.ai/carey/group-demo/groups/exp_5?workspace=user-carey):
![](/images/track/distributed_training_wgrouping_2.png)
@@ -67,4 +61,4 @@ Click the edit button in the upper right corner of a graph and select the **Adva
### Can we group runs by tags?
-Because a run can have multiple tags we don't support grouping by this field. Our recommendation would be to add a value to the [`config`](../track/config.md) object of these runs and then group by this config value. You can do this with [our API](../track/config.md#update-config-files).
+Because a run can have multiple tags we don't support grouping by this field. Our recommendation would be to add a value to the [`config`](../track/config.md) object of these runs and then group by this config value. You can do this with [our API](../track/config#set-the-configuration-after-your-run-has-finished).
diff --git a/docs/guides/runs/intro.md b/docs/guides/runs/intro.md
index ae016a8e9..c5f5bbe18 100644
--- a/docs/guides/runs/intro.md
+++ b/docs/guides/runs/intro.md
@@ -2,8 +2,8 @@
slug: /guides/runs
description: Learn about the basic building block of W&B, Runs.
displayed_sidebar: default
+title: Runs
---
-# Runs
A single unit of computation logged by W&B is called a *run*. You can think of a W&B run as an atomic element of your whole project. You should initiate a new run when you:
diff --git a/docs/guides/runs/manage-runs.md b/docs/guides/runs/manage-runs.md
index c099beb6e..ae254640a 100644
--- a/docs/guides/runs/manage-runs.md
+++ b/docs/guides/runs/manage-runs.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Manage runs
---
-# Manage runs
-
### Move runs to a team
On the project page:
diff --git a/docs/guides/runs/resuming.md b/docs/guides/runs/resuming.md
index ac57d877f..3cb3d5a1e 100644
--- a/docs/guides/runs/resuming.md
+++ b/docs/guides/runs/resuming.md
@@ -1,17 +1,13 @@
---
description: Resume a paused or exited W&B Run
displayed_sidebar: default
+title: Resume a run
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Resume runs
-
-
- Resume W&B Runs
-
-
Specify how a run should behave in the event that that run stops or crashes. To resume or enable a run to automatically resume, you will need to specify the unique run ID associated with that run for the `id` parameter:
+
```python
run = wandb.init(entity="", \
project="", id="", resume="")
@@ -129,7 +125,7 @@ If you can not share a filesystem, specify the `WANDB_RUN_ID` environment variab
## Resume preemptible Sweeps runs
Automatically requeue interrupted [sweep](../sweeps/intro.md) runs. This is particularly useful if you run a sweep agent in a compute environment that is subject to preemption such as a SLURM job in a preemptible queue, an EC2 spot instance, or a Google Cloud preemptible VM.
-Use the [`mark_preempting`](../../ref/python/run.md#markpreempting) function to enable W&B to automatically requeue interrupted sweep runs. For example, the following code snippet
+Use the [`mark_preempting`](../../ref/python/run/#mark_preempting) function to enable W&B to automatically requeue interrupted sweep runs. For example, the following code snippet
```python
run = wandb.init() # Initialize a run
diff --git a/docs/guides/runs/rewind.md b/docs/guides/runs/rewind.md
index 7392f2ee4..2c98ccfad 100644
--- a/docs/guides/runs/rewind.md
+++ b/docs/guides/runs/rewind.md
@@ -1,6 +1,7 @@
---
description: Rewind
displayed_sidebar: default
+title: Rewind a run
---
# Rewind a run
@@ -15,7 +16,7 @@ W&B currently does not support:
:::info
* To rewind a run, you must have [W&B Python SDK](https://pypi.org/project/wandb/) version >= `0.17.1`.
-* You must use monotonically increasing steps. You can not use non-monotonic steps defined with [`define_metric()`](https://docs.wandb.ai/ref/python/run#define_metric) because it will disrupt the required chronological order of run history and system metrics.
+* You must use monotonically increasing steps. You can not use non-monotonic steps defined with [`define_metric()`](/ref/python/run#define_metric) because it will disrupt the required chronological order of run history and system metrics.
:::
Rewind a run to correct or modify the history of a run without losing the original data. In addition, when you
@@ -27,13 +28,13 @@ rewind a run, you can log new data from that point in time. The summary metrics
When you rewind a run, W&B resets the state of the run to the specified step, preserving the original data and maintaining a consistent run ID. This means that:
-- **Run archiving**: Original runs are archived and accessible from the [**Run Overview**](https://docs.wandb.ai/guides/app/pages/run-page#overview-tab) tab.
+- **Run archiving**: Original runs are archived and accessible from the [**Run Overview**](/guides/app/pages/run-page#overview-tab) tab.
- **Artifact association**: Artifacts are associated with the run that produced them.
- **Immutable run IDs**: Introduced for consistent forking from a precise state.
- **Copy immutable run ID**: A button to copy the immutable run ID for improved run management.
:::tip Rewind and forking compatibility
-Forking compliments a [`rewind`](https://docs.wandb.ai/guides/runs/rewind) by providing more flexibility in managing and experimenting with your runs.
+Forking compliments a [`rewind`](/guides/runs/rewind) by providing more flexibility in managing and experimenting with your runs.
When you fork from a run, W&B creates a new branch off a run at a specific point to try different parameters or models.
@@ -43,7 +44,7 @@ When you rewind a run, W&B let's you correct or modify the run history itself.
## Rewind a run
-Use `resume_from` with [`wandb.init()`](https://docs.wandb.ai/ref/python/init) to "rewind" a run’s history to a specific step. Specify the name of the run and the step you want to rewind from:
+Use `resume_from` with [`wandb.init()`](/ref/python/init) to "rewind" a run’s history to a specific step. Specify the name of the run and the step you want to rewind from:
```python
import wandb
@@ -79,14 +80,14 @@ run2.finish()
After you rewind a run, you can explore archived run with the W&B App UI. Follow these steps to view archived runs:
-1. **Access the Overview Tab:** Navigate to the [**Overview tab**](https://docs.wandb.ai/guides/app/pages/run-page#overview-tab) on the run's page. This tab provides a comprehensive view of the run's details and history.
+1. **Access the Overview Tab:** Navigate to the [**Overview tab**](/guides/app/pages/run-page#overview-tab) on the run's page. This tab provides a comprehensive view of the run's details and history.
2. **Locate the Forked From field:** Within the **Overview** tab, find the `Forked From` field. This field captures the history of the resumptions. The **Forked From** field includes a link to the source run, allowing you to trace back to the original run and understand the entire rewind history.
By using the `Forked From` field, you can effortlessly navigate the tree of archived resumptions and gain insights into the sequence and origin of each rewind.
## Fork from a run that you rewind
-To fork from a rewound run, use the [**`fork_from`**](https://docs.wandb.ai/guides/runs/forking) argument in `wandb.init()` and specify the source run ID and the step from the source run to fork from:
+To fork from a rewound run, use the [**`fork_from`**](/guides/runs/forking) argument in `wandb.init()` and specify the source run ID and the step from the source run to fork from:
```python
import wandb
diff --git a/docs/guides/sweeps/add-w-and-b-to-your-code.md b/docs/guides/sweeps/add-w-and-b-to-your-code.md
index 11a8a7d50..948b4226f 100644
--- a/docs/guides/sweeps/add-w-and-b-to-your-code.md
+++ b/docs/guides/sweeps/add-w-and-b-to-your-code.md
@@ -1,16 +1,11 @@
---
description: Add W&B to your Python code script or Jupyter Notebook.
displayed_sidebar: default
+title: Add W&B (wandb) to your code
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Add W&B to your code
-
-
- Add W&B to your Python code
-
-
There are numerous ways to add the W&B Python SDK to your script or Jupyter Notebook. Outlined below is a "best practice" example of how to integrate the W&B Python SDK into your own code.
### Original training script
diff --git a/docs/guides/sweeps/define-sweep-configuration.md b/docs/guides/sweeps/define-sweep-configuration.md
index dc52c194e..fda7c9cab 100644
--- a/docs/guides/sweeps/define-sweep-configuration.md
+++ b/docs/guides/sweeps/define-sweep-configuration.md
@@ -1,16 +1,11 @@
---
description: Learn how to create configuration files for sweeps.
displayed_sidebar: default
+title: Configure sweeps
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Sweep configuration structure
-
-
- Define sweep configuration for hyperparameter tuning.
-
-
A W&B Sweep combines a strategy for exploring hyperparameter values with the code that evaluates them. The strategy can be as simple as trying every option or as complex as Bayesian Optimization and Hyperband ([BOHB](https://arxiv.org/abs/1807.01774)).
Define a sweep configuration either in a [Python dictionary](https://docs.python.org/3/tutorial/datastructures.html#dictionaries) or a [YAML](https://yaml.org/) file. How you define your sweep configuration depends on how you want to manage your sweep.
@@ -25,7 +20,7 @@ The following guide describes how to format your sweep configuration. See [Sweep
Both sweep configuration format options (YAML and Python dictionary) utilize key-value pairs and nested structures.
-Use top-level keys within your sweep configuration to define qualities of your sweep search such as the name of the sweep ([`name`](./sweep-config-keys.md#name) key), the parameters to search through ([`parameters`](./sweep-config-keys.md#parameters) key), the methodology to search the parameter space ([`method`](./sweep-config-keys.md#method) key), and more.
+Use top-level keys within your sweep configuration to define qualities of your sweep search such as the name of the sweep ([`name`](./sweep-config-keys.md) key), the parameters to search through ([`parameters`](./sweep-config-keys.md#parameters) key), the methodology to search the parameter space ([`method`](./sweep-config-keys.md#method) key), and more.
For example, the proceeding code snippets show the same sweep configuration defined within a YAML file and within a Python dictionary. Within the sweep configuration there are five top level keys specified: `program`, `name`, `method`, `metric` and `parameters`.
diff --git a/docs/guides/sweeps/existing-project.md b/docs/guides/sweeps/existing-project.md
index 56f526a4f..27aa556d8 100644
--- a/docs/guides/sweeps/existing-project.md
+++ b/docs/guides/sweeps/existing-project.md
@@ -3,15 +3,10 @@ description: >-
Tutorial on how to create sweep jobs from a pre-existing W&B
project.
displayed_sidebar: default
+title: "Tutorial: Create sweep job from project"
---
-# Tutorial - Create sweeps from existing projects
-
-
- Create sweeps from existing projects Tutorial
-
-
-The proceeding tutorial will walk through the steps of how to create sweep jobs from a pre-existing W&B project. We will use the [Fashion MNIST dataset](https://github.com/zalandoresearch/fashion-mnist) to train a PyTorch convolutional neural network how to classify images. The required code an dataset is located in the W&B repo: [https://github.com/wandb/examples/tree/master/examples/pytorch/pytorch-cnn-fashion](https://github.com/wandb/examples/tree/master/examples/pytorch/pytorch-cnn-fashion)
+This tutorial explains how to create sweep jobs from a pre-existing W&B project. We will use the [Fashion MNIST dataset](https://github.com/zalandoresearch/fashion-mnist) to train a PyTorch convolutional neural network how to classify images. The required code an dataset is located in the W&B repo: [https://github.com/wandb/examples/tree/master/examples/pytorch/pytorch-cnn-fashion](https://github.com/wandb/examples/tree/master/examples/pytorch/pytorch-cnn-fashion)
Explore the results in this [W&B Dashboard](https://app.wandb.ai/carey/pytorch-cnn-fashion).
@@ -31,21 +26,21 @@ Optionally explore the example appear in the W&B App UI dashboard.
From your [project page](../app/pages/project-page.md), open the [Sweep tab](./sweeps-ui.md) in the sidebar and select **Create Sweep**.
-![](@site/static/images/sweeps/sweep1.png)
+![](/images/sweeps/sweep1.png)
The auto-generated configuration guesses values to sweep over based on the runs you have completed. Edit the configuration to specify what ranges of hyperparameters you want to try. When you launch the sweep, it starts a new process on the hosted W&B sweep server. This centralized service coordinates the agents— the machines that are running the training jobs.
-![](@site/static/images/sweeps/sweep2.png)
+![](/images/sweeps/sweep2.png)
## 3. Launch agents
Next, launch an agent locally. You can launch up to 20 agents on different machines in parallel if you want to distribute the work and finish the sweep job more quickly. The agent will print out the set of parameters it’s trying next.
-![](@site/static/images/sweeps/sweep3.png)
+![](/images/sweeps/sweep3.png)
Now you're running a sweep. The following image demonstrates what the dashboard looks like as the example sweep job is running. [View an example project page →](https://app.wandb.ai/carey/pytorch-cnn-fashion)
-![](https://paper-attachments.dropbox.com/s\_5D8914551A6C0AABCD5718091305DD3B64FFBA192205DD7B3C90EC93F4002090\_1579066494222\_image.png)
+![](/images/sweeps/sweep4.png)
## Seed a new sweep with existing runs
diff --git a/docs/guides/sweeps/faq.md b/docs/guides/sweeps/faq.md
index e3f46e6c9..17df458f8 100644
--- a/docs/guides/sweeps/faq.md
+++ b/docs/guides/sweeps/faq.md
@@ -1,17 +1,11 @@
---
description: Answers to frequently asked question about W&B Sweeps.
displayed_sidebar: default
+title: Sweeps FAQ
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-
-# FAQ
-
-
- Frequently Asked Questions About Sweeps
-
-
### Do I need to provide values for all hyperparameters as part of the W&B Sweep. Can I set defaults?
The hyperparameter names and values specified as part of the sweep configuration are accessible in `wandb.config`, a dictionary-like object.
diff --git a/docs/guides/sweeps/initialize-sweeps.md b/docs/guides/sweeps/initialize-sweeps.md
index 5f5d16390..14927e836 100644
--- a/docs/guides/sweeps/initialize-sweeps.md
+++ b/docs/guides/sweeps/initialize-sweeps.md
@@ -1,17 +1,11 @@
---
description: Initialize a W&B Sweep
displayed_sidebar: default
+title: Initialize a sweep
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Initialize sweeps
-
-
- Start a W&B Sweep
-
-
W&B uses a _Sweep Controller_ to manage sweeps on the cloud (standard), locally (local) across one or more machines. After a run completes, the sweep controller will issue a new set of instructions describing a new run to execute. These instructions are picked up by _agents_ who actually perform the runs. In a typical W&B Sweep, the controller lives on the W&B server. Agents live on _your_ machine(s).
The following code snippets demonstrate how to initialize sweeps with the CLI and within a Jupyter Notebook or Python script.
diff --git a/docs/guides/sweeps/intro.md b/docs/guides/sweeps/intro.md
index 3fa2f29ae..d4766a039 100644
--- a/docs/guides/sweeps/intro.md
+++ b/docs/guides/sweeps/intro.md
@@ -2,17 +2,12 @@
slug: /guides/sweeps
description: Hyperparameter search and model optimization with W&B Sweeps
displayed_sidebar: default
+title: Sweeps
---
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-
-# Tune Hyperparameters
-
-
- Tune Hyperparameters with Sweeps
-
Use W&B Sweeps to automate hyperparameter search and visualize rich, interactive experiment tracking. Pick from popular search methods such as Bayesian, grid search, and random to search the hyperparameter space. Scale and parallelize sweep across one or more machines.
@@ -44,7 +39,6 @@ The preceding code snippet, and the colab linked on this page, show how to initi
Depending on your use case, explore the following resources to get started with W&B Sweeps:
-* If this is your first time using W&B Sweeps, we recommend you go through the [Sweeps Colab notebook](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/pytorch/Organizing_Hyperparameter_Sweeps_in_PyTorch_with_W%26B.ipynb).
* Read through the [sweeps walkthrough](./walkthrough.md) for a step-by-step outline of the W&B Python SDK commands to use to define a sweep configuration, initialize a sweep, and start a sweep.
* Explore this chapter to learn how to:
* [Add W&B to your code](./add-w-and-b-to-your-code.md)
@@ -54,7 +48,7 @@ Depending on your use case, explore the following resources to get started with
* [Visualize sweep results](./visualize-sweep-results.md)
* Explore a [curated list of Sweep experiments](./useful-resources.md) that explore hyperparameter optimization with W&B Sweeps. Results are stored in W&B Reports.
-For a step-by-step video, see: [Tune Hyperparameters Easily with W&B Sweeps](https://www.youtube.com/watch?v=9zrmUIlScdY\&ab\_channel=Weights%26Biases).
+For a step-by-step video, see: [Tune Hyperparameters Easily with W&B Sweeps](https://www.youtube.com/watch?v=9zrmUIlScdY\&ab_channel=Weights%26Biases).
diff --git a/docs/guides/sweeps/local-controller.md b/docs/guides/sweeps/local-controller.md
index e8372b05b..bb9c94e38 100644
--- a/docs/guides/sweeps/local-controller.md
+++ b/docs/guides/sweeps/local-controller.md
@@ -3,14 +3,9 @@ description: >-
Search and stop algorithms locally instead of using the W&B
cloud-hosted service.
displayed_sidebar: default
+title: Manage algorithms locally
---
-# Search and stop algorithms locally
-
-
- Search and stop algorithms locally with W&B agents
-
-
The hyper-parameter controller is hosted by Weights & Biased as a cloud service by default. W&B agents communicate with the controller to determine the next set of parameters to use for training. The controller is also responsible for running early stopping algorithms to determine which runs can be stopped.
The local controller feature allows the user to commence search and stop algorithms locally. The local controller gives the user the ability to inspect and instrument the code in order to debug issues as well as develop new features which can be incorporated into the cloud service.
diff --git a/docs/guides/sweeps/parallelize-agents.md b/docs/guides/sweeps/parallelize-agents.md
index b61bf1fcb..3bd5df9f5 100644
--- a/docs/guides/sweeps/parallelize-agents.md
+++ b/docs/guides/sweeps/parallelize-agents.md
@@ -1,17 +1,11 @@
---
description: Parallelize W&B Sweep agents on multi-core or multi-GPU machine.
displayed_sidebar: default
+title: Parallelize agents
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Parallelize agents
-
-
- Parallelize agents
-
-
Parallelize your W&B Sweep agents on a multi-core or multi-GPU machine. Before you get started, ensure you have initialized your W&B Sweep. For more information on how to initialize a W&B Sweep, see [Initialize sweeps](./initialize-sweeps.md).
### Parallelize on a multi-CPU machine
diff --git a/docs/guides/sweeps/pause-resume-and-cancel-sweeps.md b/docs/guides/sweeps/pause-resume-and-cancel-sweeps.md
index d4aa94d41..7267b949f 100644
--- a/docs/guides/sweeps/pause-resume-and-cancel-sweeps.md
+++ b/docs/guides/sweeps/pause-resume-and-cancel-sweeps.md
@@ -1,14 +1,9 @@
---
description: Pause, resume, and cancel a W&B Sweep with the CLI.
displayed_sidebar: default
+title: Manage sweeps with the CLI
---
-# Pause, resume, stop and cancel sweeps
-
-
- Pause, resume, stop or cancel W&B Sweeps
-
-
Pause, resume, and cancel a W&B Sweep with the CLI. Pausing a W&B Sweep tells the W&B agent that new W&B Runs should not be executed until the Sweep is resumed. Resuming a Sweep tells the agent to continue executing new W&B Runs. Stopping a W&B Sweep tells the W&B Sweep agent to stop creating or executing new W&B Runs. Cancelling a W&B Sweep tells the Sweep agent to kill currently executing W&B Runs and stop executing new Runs.
In each case, provide the W&B Sweep ID that was generated when you initialized a W&B Sweep. Optionally open a new terminal window to execute the proceeding commands. A new terminal window makes it easier to execute a command if a W&B Sweep is printing output statements to your current terminal window.
diff --git a/docs/guides/sweeps/start-sweep-agents.md b/docs/guides/sweeps/start-sweep-agents.md
index 4ea4b0f3c..d78e14937 100644
--- a/docs/guides/sweeps/start-sweep-agents.md
+++ b/docs/guides/sweeps/start-sweep-agents.md
@@ -1,15 +1,11 @@
---
description: Start or stop a W&B Sweep Agent on one or more machines.
displayed_sidebar: default
+title: Start or stop a sweep agent
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Start sweep agents
-
-
- Start or stop a W&B Sweep
-
Start a W&B Sweep on one or more agents on one or more machines. W&B Sweep agents query the W&B server you launched when you initialized a W&B Sweep (`wandb sweep)` for hyperparameters and use them to run model training.
@@ -23,7 +19,7 @@ Where:
* entity: Your W&B username or team name.
* project: The name of the project where you want the output of the W&B Run to be stored. If the project is not specified, the run is put in an "Uncategorized" project.
-* sweep\_ID: The pseudo random, unique ID generated by W&B.
+* sweep_ID: The pseudo random, unique ID generated by W&B.
Provide the name of the function the W&B Sweep will execute if you start a W&B Sweep agent within a Jupyter Notebook or Python script.
diff --git a/docs/guides/sweeps/sweep-config-keys.md b/docs/guides/sweeps/sweep-config-keys.md
index 0a5a8d343..57d71b8b9 100644
--- a/docs/guides/sweeps/sweep-config-keys.md
+++ b/docs/guides/sweeps/sweep-config-keys.md
@@ -1,29 +1,28 @@
---
displayed_sidebar: default
+title: Sweep configuration options
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Sweep configuration options
-
A sweep configuration consists of nested key-value pairs. Use top-level keys within your sweep configuration to define qualities of your sweep search such as the parameters to search through ([`parameter`](./sweep-config-keys.md#parameters) key), the methodology to search the parameter space ([`method`](./sweep-config-keys.md#method) key), and more.
The proceeding table lists top-level sweep configuration keys and a brief description. See the respective sections for more information about each key.
-| Top-level keys | Description |
-| ----------------- | ----------------------------------------------------------------------------------------------------------------------------- |
-| `program` | (required) Training script to run. |
-| `entity` | Specify the entity for this sweep. |
-| `project` | Specify the project for this sweep. |
-| `description` | Text description of the sweep. |
-| `name` | The name of the sweep, displayed in the W&B UI. |
-| [`method`](#method) | (required) Specify the [search strategy](./define-sweep-configuration.md#configuration-keys). |
-| [`metric`](#metric) | Specify the metric to optimize (only used by certain search strategies and stopping criteria). |
-| [`parameters`](#parameters) | (required) Specify [parameters](define-sweep-configuration.md#parameters) bounds to search. |
-| [`early_terminate`](#early_terminate) | Specify any [early stopping criteria](./define-sweep-configuration.md#early_terminate). |
-| [`command`](#command) | Specify [command structure ](./define-sweep-configuration.md#command)for invoking and passing arguments to the training script. |
-| `run_cap` | Specify a maximum number of runs in a sweep. |
+| Top-level keys | Description |
+| -------------- | ----------- |
+| `program` | (required) Training script to run |
+| `entity` | The entity for this sweep |
+| `project` | The project for this sweep |
+| `description` | Text description of the sweep |
+| `name` | The name of the sweep, displayed in the W&B UI. |
+| [`method`](#method) | (required) The search strategy |
+| [`metric`](#metric) | The metric to optimize (only used by certain search strategies and stopping criteria) |
+| [`parameters`](#parameters) | (required) Parameter bounds to search |
+| [`early_terminate`](#early_terminate) | Any early stopping criteria |
+| [`command`](#command) | Command structure for invoking and passing arguments to the training script |
+| `run_cap` | Maximum number of runs for this sweep |
See the [Sweep configuration](./sweep-config-keys.md) structure for more information on how to structure your sweep configuration.
@@ -90,7 +89,7 @@ Choose a random, uninformed, set of hyperparameter values on each iteration base
Specify the distribution space with the metric key if you choose random (`method: random`) search.
#### Bayesian search
-In contrast to [random](#random-search) and [grid](#grid-search) search, Bayesian models make informed decisions. Bayesian optimization uses a probabilistic model to decide which values to use through an iterative process of testing values on a surrogate function before evaluating the objective function. Bayesian search works well for small numbers of continuous parameters but scales poorly. For more information about Bayesian search, see the [Bayesian Optimization Primer paper](https://static.sigopt.com/b/20a144d208ef255d3b981ce419667ec25d8412e2/static/pdf/SigOpt_Bayesian_Optimization_Primer.pdf).
+In contrast to [random](#random-search) and [grid](#grid-search) search, Bayesian models make informed decisions. Bayesian optimization uses a probabilistic model to decide which values to use through an iterative process of testing values on a surrogate function before evaluating the objective function. Bayesian search works well for small numbers of continuous parameters but scales poorly. For more information about Bayesian search, see the [Bayesian Optimization Primer paper](https://web.archive.org/web/20240209053347/https://static.sigopt.com/b/20a144d208ef255d3b981ce419667ec25d8412e2/static/pdf/SigOpt_Bayesian_Optimization_Primer.pdf).
diff --git a/docs/guides/sweeps/sweeps-ui.md b/docs/guides/sweeps/sweeps-ui.md
index b00a816ef..515e9a676 100644
--- a/docs/guides/sweeps/sweeps-ui.md
+++ b/docs/guides/sweeps/sweeps-ui.md
@@ -1,20 +1,7 @@
---
description: Describes the different components of the Sweeps UI.
displayed_sidebar: default
+title: Sweeps UI
---
-# Sweeps UI
-
-
- Sweeps UI
-
-
-
-
-The state (**State**), creation time (**Created**), the entity that started the sweep (**Creator**), the number of runs completed (**Run count**), and the time it took to compute the sweep (**Compute time**) are displayed in the Sweeps UI. The expected number of runs a sweep will create (**Est. Runs**) is provided when you do a grid search over a discrete search space.
-
-
-
-Click on a sweep to manage the state of the sweep. You can pause, resume, stop, or kill a sweep from the interface.
-
-![](https://downloads.intercomcdn.com/i/o/146037849/aeae7b64ddf7008f48dfb170/sweep+controls.png)
+The state (**State**), creation time (**Created**), the entity that started the sweep (**Creator**), the number of runs completed (**Run count**), and the time it took to compute the sweep (**Compute time**) are displayed in the Sweeps UI. The expected number of runs a sweep will create (**Est. Runs**) is provided when you do a grid search over a discrete search space. You can also click on a sweep to pause, resume, stop, or kill the sweep from the interface.
diff --git a/docs/guides/sweeps/troubleshoot-sweeps.md b/docs/guides/sweeps/troubleshoot-sweeps.md
index 28ff4d0a2..547f312f9 100644
--- a/docs/guides/sweeps/troubleshoot-sweeps.md
+++ b/docs/guides/sweeps/troubleshoot-sweeps.md
@@ -1,14 +1,9 @@
---
description: Troubleshoot common W&B Sweep issues.
displayed_sidebar: default
+title: Sweeps troubleshooting
---
-# Troubleshoot Sweeps
-
-
- Troubleshoot W&B Sweeps
-
-
Troubleshoot common error messages with the guidance suggested.
### `CommError, Run does not exist` and `ERROR Error uploading`
diff --git a/docs/guides/sweeps/useful-resources.md b/docs/guides/sweeps/useful-resources.md
index 0944f2841..ae6e9157d 100644
--- a/docs/guides/sweeps/useful-resources.md
+++ b/docs/guides/sweeps/useful-resources.md
@@ -1,14 +1,9 @@
---
description: Collection of useful sources for Sweeps.
displayed_sidebar: default
+title: Learn more about sweeps
---
-# Useful resources
-
-
- Resources to learn more about W&B Sweeps
-
-
### Academic papers
Li, Lisha, et al. "[Hyperband: A novel bandit-based approach to hyperparameter optimization.](https://arxiv.org/pdf/1603.06560.pdf)" _The Journal of Machine Learning Research_ 18.1 (2017): 6765-6816.
@@ -21,11 +16,11 @@ The following W&B Reports demonstrate examples of projects that explore hyperpar
* Description: Developing the baseline and exploring submissions to the Drought Watch benchmark.
* [Tuning Safety Penalties in Reinforcement Learning](https://wandb.ai/safelife/benchmark-sweeps/reports/Tuning-Safety-Penalties-in-Reinforcement-Learning---VmlldzoyNjQyODM)
* Description: We examine agents trained with different side effect penalties on three different tasks: pattern creation, pattern removal, and navigation.
-* [Meaning and Noise in Hyperparameter Search with W&B](https://wandb.ai/stacey/pytorch\_intro/reports/Meaning-and-Noise-in-Hyperparameter-Search--Vmlldzo0Mzk5MQ) [Stacey Svetlichnaya](https://wandb.ai/stacey)
+* [Meaning and Noise in Hyperparameter Search with W&B](https://wandb.ai/stacey/pytorch_intro/reports/Meaning-and-Noise-in-Hyperparameter-Search--Vmlldzo0Mzk5MQ) [Stacey Svetlichnaya](https://wandb.ai/stacey)
* Description: How do we distinguish signal from pareidolia (imaginary patterns)? This article is showcases what is possible with W&B and aims to inspire further exploration.
* [Who is Them? Text Disambiguation with Transformers](https://wandb.ai/stacey/winograd/reports/Who-is-Them-Text-Disambiguation-with-Transformers--VmlldzoxMDU1NTc)
* Description: Using Hugging Face to explore models for natural language understanding
-* [DeepChem: Molecular Solubility](https://wandb.ai/stacey/deepchem\_molsol/reports/DeepChem-Molecular-Solubility--VmlldzoxMjQxMjM)
+* [DeepChem: Molecular Solubility](https://wandb.ai/stacey/deepchem_molsol/reports/DeepChem-Molecular-Solubility--VmlldzoxMjQxMjM)
* Description: Predict chemical properties from molecular structure with random forests and deep nets.
* [Intro to MLOps: Hyperparameter Tuning](https://wandb.ai/iamleonie/Intro-to-MLOps/reports/Intro-to-MLOps-Hyperparameter-Tuning--VmlldzozMTg2OTk3)
* Description: Explore why hyperparameter optimization matters and look at three algorithms to automate hyperparameter tuning for your machine learning models.
@@ -34,7 +29,7 @@ The following W&B Reports demonstrate examples of projects that explore hyperpar
The following how-to-guide demonstrates how to solve real-world problems with W&B:
-* [Sweeps with XGBoost ](https://github.com/wandb/examples/blob/master/examples/wandb-sweeps/sweeps-xgboost/xgboost\_tune.py)
+* [Sweeps with XGBoost ](https://github.com/wandb/examples/blob/master/examples/wandb-sweeps/sweeps-xgboost/xgboost_tune.py)
* Description: How to use W&B Sweeps for hyperparameter tuning using XGBoost.
### Sweep GitHub repository
diff --git a/docs/guides/sweeps/visualize-sweep-results.md b/docs/guides/sweeps/visualize-sweep-results.md
index 4c56e6615..19f5e92b5 100644
--- a/docs/guides/sweeps/visualize-sweep-results.md
+++ b/docs/guides/sweeps/visualize-sweep-results.md
@@ -1,14 +1,9 @@
---
description: Visualize the results of your W&B Sweeps with the W&B App UI.
displayed_sidebar: default
+title: Visualize sweep results
---
-# Visualize sweep results
-
-
- Visualize Results of W&B Sweeps
-
-
Visualize the results of your W&B Sweeps with the W&B App UI. Navigate to the W&B App UI at [https://wandb.ai/home](https://wandb.ai/home). Choose the project that you specified when you initialized a W&B Sweep. You will be redirected to your project [workspace](../app/pages/workspaces.md). Select the **Sweep icon** on the left panel (broom icon). From the [Sweep UI](./visualize-sweep-results.md), select the name of your Sweep from the list.
By default, W&B will automatically create a parallel coordinates plot, a parameter importance plot, and a scatter plot when you start a W&B Sweep job.
diff --git a/docs/guides/sweeps/walkthrough.md b/docs/guides/sweeps/walkthrough.md
index e0745fdd1..f2a87cf9a 100644
--- a/docs/guides/sweeps/walkthrough.md
+++ b/docs/guides/sweeps/walkthrough.md
@@ -3,14 +3,9 @@ description: >-
Sweeps quickstart shows how to define, initialize, and run a sweep. There are
four main steps
displayed_sidebar: default
+title: "Tutorial: Define, initialize, and run a sweep"
---
-# Walkthrough
-
-
- Sweeps Walkthrough
-
-
This page shows how to define, initialize, and run a sweep. There are four main steps:
1. [Set up your training code](#set-up-your-training-code)
diff --git a/docs/guides/tables/intro.md b/docs/guides/tables/intro.md
index 6c23b216a..d7eec730a 100644
--- a/docs/guides/tables/intro.md
+++ b/docs/guides/tables/intro.md
@@ -2,12 +2,11 @@
slug: /guides/tables
description: Iterate on datasets and understand model predictions
displayed_sidebar: default
+title: Visualize your data with tables
---
import Translate, {translate} from '@docusaurus/Translate';
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# Visualize your data
-
Use W&B Tables to visualize and query tabular data. For example:
diff --git a/docs/guides/tables/tables-download.md b/docs/guides/tables/tables-download.md
index de6357397..8a4114ed4 100644
--- a/docs/guides/tables/tables-download.md
+++ b/docs/guides/tables/tables-download.md
@@ -1,9 +1,9 @@
---
description: How to export data from tables.
displayed_sidebar: default
+title: Export table data
---
-# Export Table Data
Like all W&B Artifacts, Tables can be converted into pandas dataframes for easy data exporting.
## Convert `table` to `artifact`
diff --git a/docs/guides/tables/tables-gallery.md b/docs/guides/tables/tables-gallery.md
index e76b40953..b1c447895 100644
--- a/docs/guides/tables/tables-gallery.md
+++ b/docs/guides/tables/tables-gallery.md
@@ -1,18 +1,18 @@
---
description: Examples of W&B Tables
displayed_sidebar: default
+title: Example tables
---
-# Tables gallery
The following sections highlight some of the ways you can use tables:
### View your data
-Log metrics and rich media during model training or evaluation, then visualize results in a persistent database synced to the cloud, or to your [hosting instance](https://docs.wandb.ai/guides/hosting).
+Log metrics and rich media during model training or evaluation, then visualize results in a persistent database synced to the cloud, or to your [hosting instance](/guides/hosting).
![Browse examples and verify the counts and distribution of your data](/images/data_vis/tables_see_data.png)
-For example, check out this table that shows a [balanced split of a photos dataset](https://wandb.ai/stacey/mendeleev/artifacts/balanced\_data/inat\_80-10-10\_5K/ab79f01e007113280018/files/data\_split.table.json).
+For example, check out this table that shows a [balanced split of a photos dataset](https://wandb.ai/stacey/mendeleev/artifacts/balanced_data/inat_80-10-10_5K/ab79f01e007113280018/files/data_split.table.json).
### Interactively explore your data
@@ -28,7 +28,7 @@ Quickly compare results across different training epochs, datasets, hyperparamet
![See granular differences: the left model detects some red sidewalk, the right does not.](/images/data_vis/compare_model_versions.png)
-For example, see this table that compares [two models on the same test images](https://wandb.ai/stacey/evalserver\_answers\_2/artifacts/results/eval\_Daenerys/c2290abd3d7274f00ad8/files/eval\_results.table.json#b6dae62d4f00d31eeebf$eval\_Bob).
+For example, see this table that compares [two models on the same test images](https://wandb.ai/stacey/evalserver_answers_2/artifacts/results/eval_Daenerys/c2290abd3d7274f00ad8/files/eval_results.table.json#b6dae62d4f00d31eeebf$eval_Bob).
### Track every detail and see the bigger picture
@@ -42,7 +42,7 @@ The following highlight some real W&B Projects that use W&B Tables.
### Image classification
-Read [this report](https://wandb.ai/stacey/mendeleev/reports/Visualize-Data-for-Image-Classification--VmlldzozNjE3NjA), follow [this colab](https://wandb.me/dsviz-nature-colab), or explore this [artifacts context](https://wandb.ai/stacey/mendeleev/artifacts/val\_epoch\_preds/val\_pred\_gawf9z8j/2dcee8fa22863317472b/files/val\_epoch\_res.table.json) to see how a CNN identifies ten types of living things (plants, bird, insects, etc) from [iNaturalist](https://www.inaturalist.org/pages/developers) photos.
+Read [this report](https://wandb.ai/stacey/mendeleev/reports/Visualize-Data-for-Image-Classification--VmlldzozNjE3NjA), follow [this colab](https://wandb.me/dsviz-nature-colab), or explore this [artifacts context](https://wandb.ai/stacey/mendeleev/artifacts/val_epoch_preds/val_pred_gawf9z8j/2dcee8fa22863317472b/files/val_epoch_res.table.json) to see how a CNN identifies ten types of living things (plants, bird, insects, etc) from [iNaturalist](https://www.inaturalist.org/pages/developers) photos.
![Compare the distribution of true labels across two different models' predictions.](/images/data_vis/image_classification.png)
@@ -56,11 +56,11 @@ Interact with audio tables in [this report](https://wandb.ai/stacey/cshanty/repo
Browse text samples from training data or generated output, dynamically group by relevant fields, and align your evaluation across model variants or experiment settings. Render text as Markdown or use visual diff mode to compare texts. Explore a simple character-based RNN for generating Shakespeare in [this report](https://wandb.ai/stacey/nlg/reports/Visualize-Text-Data-Predictions--Vmlldzo1NzcwNzY).
-![Doubling the size of the hidden layer yields some more creative prompt completions.](@site/static/images/data_vis/shakesamples.png)
+![Doubling the size of the hidden layer yields some more creative prompt completions.](/images/data_vis/shakesamples.png)
### Video
-Browse and aggregate over videos logged during training to understand your models. Here is an early example using the [SafeLife benchmark](https://wandb.ai/safelife/v1dot2/benchmark) for RL agents seeking to [minimize side effects ](https://wandb.ai/stacey/saferlife/artifacts/video/videos\_append-spawn/c1f92c6e27fa0725c154/files/video\_examples.table.json)
+Browse and aggregate over videos logged during training to understand your models. Here is an early example using the [SafeLife benchmark](https://wandb.ai/safelife/v1dot2/benchmark) for RL agents seeking to [minimize side effects ](https://wandb.ai/stacey/saferlife/artifacts/video/videos_append-spawn/c1f92c6e27fa0725c154/files/video_examples.table.json)
![Browse easily through the few successful agents](/images/data_vis/video.png)
@@ -68,11 +68,11 @@ Browse and aggregate over videos logged during training to understand your model
View a report on how to [split and preprocess tabular data](https://wandb.ai/dpaiton/splitting-tabular-data/reports/Tabular-Data-Versioning-and-Deduplication-with-Weights-Biases--VmlldzoxNDIzOTA1) with version control and deduplication.
-![Tables and Artifacts work together to version control, label, and deduplicate your dataset iterations](@site/static/images/data_vis/tabs.png)
+![Tables and Artifacts work together to version control, label, and deduplicate your dataset iterations](/images/data_vis/tabs.png)
### Comparing model variants (semantic segmentation)
-An [interactive notebook](https://wandb.me/dsviz-cars-demo) and [live example](https://wandb.ai/stacey/evalserver\_answers\_2/artifacts/results/eval\_Daenerys/c2290abd3d7274f00ad8/files/eval\_results.table.json#a57f8e412329727038c2$eval\_Ada) of logging Tables for semantic segmentation and comparing different models. Try your own queries [in this Table](https://wandb.ai/stacey/evalserver\_answers\_2/artifacts/results/eval\_Daenerys/c2290abd3d7274f00ad8/files/eval\_results.table.json).
+An [interactive notebook](https://wandb.me/dsviz-cars-demo) and [live example](https://wandb.ai/stacey/evalserver_answers_2/artifacts/results/eval_Daenerys/c2290abd3d7274f00ad8/files/eval_results.table.json#a57f8e412329727038c2$eval_Ada) of logging Tables for semantic segmentation and comparing different models. Try your own queries [in this Table](https://wandb.ai/stacey/evalserver_answers_2/artifacts/results/eval_Daenerys/c2290abd3d7274f00ad8/files/eval_results.table.json).
![Find the best predictions across two models on the same test set](/images/data_vis/comparing_model_variants.png)
diff --git a/docs/guides/tables/tables-walkthrough.md b/docs/guides/tables/tables-walkthrough.md
index 49078bddc..20176d25c 100644
--- a/docs/guides/tables/tables-walkthrough.md
+++ b/docs/guides/tables/tables-walkthrough.md
@@ -1,15 +1,13 @@
---
description: Explore how to use W&B Tables with this 5 minute Quickstart.
displayed_sidebar: default
+title: "Tutorial: Log tables, visualize and query data"
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Walkthrough
-
The following Quickstart demonstrates how to log data tables, visualize data, and query data.
-
Select the button below to try a PyTorch Quickstart example project on MNIST data.
## 1. Log a table
diff --git a/docs/guides/tables/visualize-tables.md b/docs/guides/tables/visualize-tables.md
index 19b9d0d53..cdec7a314 100644
--- a/docs/guides/tables/visualize-tables.md
+++ b/docs/guides/tables/visualize-tables.md
@@ -1,12 +1,11 @@
---
description: Visualize and analyze W&B Tables.
displayed_sidebar: default
+title: Visualize and analyze tables
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Visualize & analyze tables
-
Customize your W&B Tables to answer questions about your machine learning model's performance, analyze your data, and more.
Interactively explore your data to:
@@ -38,18 +37,18 @@ Follow these steps to compare two tables:
In the following image we demonstrate a model's predictions on MNIST validation data after each of five epochs ([view interactive example here](https://wandb.ai/stacey/mnist-viz/artifacts/predictions/baseline/d888bc05719667811b23/files/predictions.table.json)).
-![Click on "predictions" to view the Table](@site/static/images/data_vis/preds_mnist.png)
+![Click on "predictions" to view the Table](/images/data_vis/preds_mnist.png)
3. Hover over the second artifact version you want to compare in the sidebar and click **Compare** when it appears. For example, in the image below we select a version labeled as "v4" to compare to MNIST predictions made by the same model after 5 epochs of training.
-![Preparing to compare model predictions after training for 1 epoch (v0, shown here) vs 5 epochs (v4)](@site/static/images/data_vis/preds_2.png)
+![Preparing to compare model predictions after training for 1 epoch (v0, shown here) vs 5 epochs (v4)](/images/data_vis/preds_2.png)
### Merged view
Initially you will see both tables merged together. The first table selected has index 0 and a blue highlight, and the second table has index 1 and a yellow highlight. [View a live example of merged tables here](https://wandb.ai/stacey/mnist-viz/artifacts/predictions/baseline/d888bc05719667811b23/files/predictions.table.json#7dd0cd845c0edb469dec).
-![In the merged view, numerical columns will appear as histograms by default](@site/static/images/data_vis/merged_view.png)
+![In the merged view, numerical columns will appear as histograms by default](/images/data_vis/merged_view.png)
From the merged view, you can
@@ -71,7 +70,7 @@ To view the two tables side-by-side, change the first dropdown from "Merge Table
## Compare artifacts
-You can also [compare tables across time](#compare-across-time) or [model variants](#compare-across-model-variants).
+You can also [compare tables across time](#compare-tables-across-time) or [model variants](#compare-tables-across-model-variants).
### Compare tables across time
@@ -85,7 +84,7 @@ For a more detailed walkthrough of visualizing predictions across training time,
Compare two artifact versions logged at the same step for two different models to analyze model performance across different configurations (hyperparameters, base architectures, and so forth).
-For example, compare predictions between a `baseline` and a new model variant, `2x_layers_2x_lr`, where the first convolutional layer doubles from 32 to 64, the second from 128 to 256, and the learning rate from 0.001 to 0.002. From [this live example](https://wandb.ai/stacey/mnist-viz/artifacts/predictions/baseline/d888bc05719667811b23/files/predictions.table.json#2bb3b1d40aa777496b5d$2x\_layers\_2x\_lr), use the side-by-side view and filter down to the incorrect predictions after 1 (left tab) versus 5 training epochs (right tab).
+For example, compare predictions between a `baseline` and a new model variant, `2x_layers_2x_lr`, where the first convolutional layer doubles from 32 to 64, the second from 128 to 256, and the learning rate from 0.001 to 0.002. From [this live example](https://wandb.ai/stacey/mnist-viz/artifacts/predictions/baseline/d888bc05719667811b23/files/predictions.table.json#2bb3b1d40aa777496b5d$2x_layers_2x_lr), use the side-by-side view and filter down to the incorrect predictions after 1 (left tab) versus 5 training epochs (right tab).
- Organize and visualize results from your machine learning models
-
-
Use the W&B Dashboard as a central place to organize and visualize results from your machine learning models.
diff --git a/docs/guides/track/config.md b/docs/guides/track/config.md
index d7f841b1a..6c90da87a 100644
--- a/docs/guides/track/config.md
+++ b/docs/guides/track/config.md
@@ -1,15 +1,11 @@
---
description: Use a dictionary-like object to save your experiment configuration
displayed_sidebar: default
+title: Configure experiments
---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# Configure experiments
-
-
- Configure a Machine Learning Experiment
-
-
-[**Try in a Colab Notebook here**](http://wandb.me/config-colab)
+
Use the `wandb.config` object to save your training configuration such as:
- hyperparameters
diff --git a/docs/guides/track/environment-variables.md b/docs/guides/track/environment-variables.md
index 3b26b2e9e..56d2352e6 100644
--- a/docs/guides/track/environment-variables.md
+++ b/docs/guides/track/environment-variables.md
@@ -1,14 +1,9 @@
---
description: Set W&B environment variables.
displayed_sidebar: default
+title: Environment variables
---
-# Environment Variables
-
-
- W&B Environment Variables
-
-
When you're running a script in an automated environment, you can control **wandb** with environment variables set before the script runs or within the script.
```bash
@@ -36,50 +31,50 @@ Use these optional environment variables to do things like set up authentication
| Variable name | Usage |
| --------------------------- | ---------- |
-| **WANDB\_ANONYMOUS** | Set this to "allow", "never", or "must" to let users create anonymous runs with secret urls. |
-| **WANDB\_API\_KEY** | Sets the authentication key associated with your account. You can find your key on [your settings page](https://app.wandb.ai/settings). This must be set if `wandb login` hasn't been run on the remote machine. |
-| **WANDB\_BASE\_URL** | If you're using [wandb/local](../hosting/intro.md) you should set this environment variable to `http://YOUR_IP:YOUR_PORT` |
-| **WANDB\_CACHE\_DIR** | This defaults to \~/.cache/wandb, you can override this location with this environment variable |
-| **WANDB\_CONFIG\_DIR** | This defaults to \~/.config/wandb, you can override this location with this environment variable |
-| **WANDB\_CONFIG\_PATHS** | Comma separated list of yaml files to load into wandb.config. See [config](./config.md#file-based-configs). |
-| **WANDB\_CONSOLE** | Set this to "off" to disable stdout / stderr logging. This defaults to "on" in environments that support it. |
-| **WANDB\_DIR** | Set this to an absolute path to store all generated files here instead of the _wandb_ directory relative to your training script. _be sure this directory exists and the user your process runs as can write to it_ |
-| **WANDB\_DISABLE\_GIT** | Prevent wandb from probing for a git repository and capturing the latest commit / diff. |
-| **WANDB\_DISABLE\_CODE** | Set this to true to prevent wandb from saving notebooks or git diffs. We'll still save the current commit if we're in a git repo. |
-| **WANDB\_DOCKER** | Set this to a docker image digest to enable restoring of runs. This is set automatically with the wandb docker command. You can obtain an image digest by running `wandb docker my/image/name:tag --digest` |
-| **WANDB\_ENTITY** | The entity associated with your run. If you have run `wandb init` in the directory of your training script, it will create a directory named _wandb_ and will save a default entity which can be checked into source control. If you don't want to create that file or want to override the file you can use the environmental variable. |
-| **WANDB\_ERROR\_REPORTING** | Set this to false to prevent wandb from logging fatal errors to its error tracking system. |
-| **WANDB\_HOST** | Set this to the hostname you want to see in the wandb interface if you don't want to use the system provided hostname |
-| **WANDB\_IGNORE\_GLOBS** | Set this to a comma separated list of file globs to ignore. These files will not be synced to the cloud. |
-| **WANDB\_JOB\_NAME** | Specify a name for any jobs created by `wandb`. For more information, see [create a job](../launch/create-launch-job.md) |
-| **WANDB\_JOB\_TYPE** | Specify the job type, like "training" or "evaluation" to indicate different types of runs. See [grouping](../runs/grouping.md) for more info. |
-| **WANDB\_MODE** | If you set this to "offline" wandb will save your run metadata locally and not sync to the server. If you set this to "disabled" wandb will turn off completely. |
-| **WANDB\_NAME** | The human-readable name of your run. If not set it will be randomly generated for you |
-| **WANDB\_NOTEBOOK\_NAME** | If you're running in jupyter you can set the name of the notebook with this variable. We attempt to auto detect this. |
-| **WANDB\_NOTES** | Longer notes about your run. Markdown is allowed and you can edit this later in the UI. |
-| **WANDB\_PROJECT** | The project associated with your run. This can also be set with `wandb init`, but the environmental variable will override the value. |
-| **WANDB\_RESUME** | By default this is set to _never_. If set to _auto_ wandb will automatically resume failed runs. If set to _must_ forces the run to exist on startup. If you want to always generate your own unique ids, set this to _allow_ and always set **WANDB\_RUN\_ID**. |
-| **WANDB\_RUN\_GROUP** | Specify the experiment name to automatically group runs together. See [grouping](../runs/grouping.md) for more info. |
-| **WANDB\_RUN\_ID** | Set this to a globally unique string (per project) corresponding to a single run of your script. It must be no longer than 64 characters. All non-word characters will be converted to dashes. This can be used to resume an existing run in cases of failure. |
-| **WANDB\_SILENT** | Set this to **true** to silence wandb log statements. If this is set all logs will be written to **WANDB\_DIR**/debug.log |
-| **WANDB\_SHOW\_RUN** | Set this to **true** to automatically open a browser with the run url if your operating system supports it. |
-| **WANDB\_TAGS** | A comma separated list of tags to be applied to the run. |
-| **WANDB\_USERNAME** | The username of a member of your team associated with the run. This can be used along with a service account API key to enable attribution of automated runs to members of your team. |
-| **WANDB\_USER\_EMAIL** | The email of a member of your team associated with the run. This can be used along with a service account API key to enable attribution of automated runs to members of your team. |
+| **WANDB_ANONYMOUS** | Set this to "allow", "never", or "must" to let users create anonymous runs with secret urls. |
+| **WANDB_API_KEY** | Sets the authentication key associated with your account. You can find your key on [your settings page](https://app.wandb.ai/settings). This must be set if `wandb login` hasn't been run on the remote machine. |
+| **WANDB_BASE_URL** | If you're using [wandb/local](../hosting/intro.md) you should set this environment variable to `http://YOUR_IP:YOUR_PORT` |
+| **WANDB_CACHE_DIR** | This defaults to \~/.cache/wandb, you can override this location with this environment variable |
+| **WANDB_CONFIG_DIR** | This defaults to \~/.config/wandb, you can override this location with this environment variable |
+| **WANDB_CONFIG_PATHS** | Comma separated list of yaml files to load into wandb.config. See [config](./config.md#file-based-configs). |
+| **WANDB_CONSOLE** | Set this to "off" to disable stdout / stderr logging. This defaults to "on" in environments that support it. |
+| **WANDB_DIR** | Set this to an absolute path to store all generated files here instead of the _wandb_ directory relative to your training script. _be sure this directory exists and the user your process runs as can write to it_ |
+| **WANDB_DISABLE_GIT** | Prevent wandb from probing for a git repository and capturing the latest commit / diff. |
+| **WANDB_DISABLE_CODE** | Set this to true to prevent wandb from saving notebooks or git diffs. We'll still save the current commit if we're in a git repo. |
+| **WANDB_DOCKER** | Set this to a docker image digest to enable restoring of runs. This is set automatically with the wandb docker command. You can obtain an image digest by running `wandb docker my/image/name:tag --digest` |
+| **WANDB_ENTITY** | The entity associated with your run. If you have run `wandb init` in the directory of your training script, it will create a directory named _wandb_ and will save a default entity which can be checked into source control. If you don't want to create that file or want to override the file you can use the environmental variable. |
+| **WANDB_ERROR_REPORTING** | Set this to false to prevent wandb from logging fatal errors to its error tracking system. |
+| **WANDB_HOST** | Set this to the hostname you want to see in the wandb interface if you don't want to use the system provided hostname |
+| **WANDB_IGNORE_GLOBS** | Set this to a comma separated list of file globs to ignore. These files will not be synced to the cloud. |
+| **WANDB_JOB_NAME** | Specify a name for any jobs created by `wandb`. For more information, see [create a job](../launch/create-launch-job.md) |
+| **WANDB_JOB_TYPE** | Specify the job type, like "training" or "evaluation" to indicate different types of runs. See [grouping](../runs/grouping.md) for more info. |
+| **WANDB_MODE** | If you set this to "offline" wandb will save your run metadata locally and not sync to the server. If you set this to "disabled" wandb will turn off completely. |
+| **WANDB_NAME** | The human-readable name of your run. If not set it will be randomly generated for you |
+| **WANDB_NOTEBOOK_NAME** | If you're running in jupyter you can set the name of the notebook with this variable. We attempt to auto detect this. |
+| **WANDB_NOTES** | Longer notes about your run. Markdown is allowed and you can edit this later in the UI. |
+| **WANDB_PROJECT** | The project associated with your run. This can also be set with `wandb init`, but the environmental variable will override the value. |
+| **WANDB_RESUME** | By default this is set to _never_. If set to _auto_ wandb will automatically resume failed runs. If set to _must_ forces the run to exist on startup. If you want to always generate your own unique ids, set this to _allow_ and always set **WANDB_RUN_ID**. |
+| **WANDB_RUN_GROUP** | Specify the experiment name to automatically group runs together. See [grouping](../runs/grouping.md) for more info. |
+| **WANDB_RUN_ID** | Set this to a globally unique string (per project) corresponding to a single run of your script. It must be no longer than 64 characters. All non-word characters will be converted to dashes. This can be used to resume an existing run in cases of failure. |
+| **WANDB_SILENT** | Set this to **true** to silence wandb log statements. If this is set all logs will be written to **WANDB_DIR**/debug.log |
+| **WANDB_SHOW_RUN** | Set this to **true** to automatically open a browser with the run url if your operating system supports it. |
+| **WANDB_TAGS** | A comma separated list of tags to be applied to the run. |
+| **WANDB_USERNAME** | The username of a member of your team associated with the run. This can be used along with a service account API key to enable attribution of automated runs to members of your team. |
+| **WANDB_USER_EMAIL** | The email of a member of your team associated with the run. This can be used along with a service account API key to enable attribution of automated runs to members of your team. |
## Singularity environments
-If you're running containers in [Singularity](https://singularity.lbl.gov/index.html) you can pass environment variables by pre-pending the above variables with **SINGULARITYENV\_**. More details about Singularity environment variables can be found [here](https://singularity.lbl.gov/docs-environment-metadata#environment).
+If you're running containers in [Singularity](https://singularity.lbl.gov/index.html) you can pass environment variables by pre-pending the above variables with **SINGULARITYENV_**. More details about Singularity environment variables can be found [here](https://singularity.lbl.gov/docs-environment-metadata#environment).
## Running on AWS
-If you're running batch jobs in AWS, it's easy to authenticate your machines with your W&B credentials. Get your API key from your [settings page](https://app.wandb.ai/settings), and set the WANDB\_API\_KEY environment variable in the [AWS batch job spec](https://docs.aws.amazon.com/batch/latest/userguide/job\_definition\_parameters.html#parameters).
+If you're running batch jobs in AWS, it's easy to authenticate your machines with your W&B credentials. Get your API key from your [settings page](https://app.wandb.ai/settings), and set the WANDB_API_KEY environment variable in the [AWS batch job spec](https://docs.aws.amazon.com/batch/latest/userguide/job_definition_parameters.html#parameters).
## Common Questions
### Automated runs and service accounts
-If you have automated tests or internal tools that launch runs logging to W&B, create a **Service Account** on your team settings page. This will allow you to use a service API key for your automated jobs. If you want to attribute service account jobs to a specific user, you can use the **WANDB\_USERNAME** or **WANDB\_USER\_EMAIL** environment variables.
+If you have automated tests or internal tools that launch runs logging to W&B, create a **Service Account** on your team settings page. This will allow you to use a service API key for your automated jobs. If you want to attribute service account jobs to a specific user, you can use the **WANDB_USERNAME** or **WANDB_USER_EMAIL** environment variables.
![Create a service account on your team settings page for automated jobs](/images/track/common_questions_automate_runs.png)
@@ -104,6 +99,6 @@ logger.setLevel(logging.WARNING)
### Multiple wandb users on shared machines
-If you're using a shared machine and another person is a wandb user, it's easy to make sure your runs are always logged to the proper account. Set the WANDB\_API\_KEY environment variable to authenticate. If you source it in your env, when you log in you'll have the right credentials, or you can set the environment variable from your script.
+If you're using a shared machine and another person is a wandb user, it's easy to make sure your runs are always logged to the proper account. Set the WANDB_API_KEY environment variable to authenticate. If you source it in your env, when you log in you'll have the right credentials, or you can set the environment variable from your script.
Run this command `export WANDB_API_KEY=X` where X is your API key. When you're logged in, you can find your API key at [wandb.ai/authorize](https://app.wandb.ai/authorize).
diff --git a/docs/guides/track/intro.md b/docs/guides/track/intro.md
index dddfdf96a..a16e1b389 100644
--- a/docs/guides/track/intro.md
+++ b/docs/guides/track/intro.md
@@ -2,18 +2,13 @@
description: Track machine learning experiments with W&B.
slug: /guides/track
displayed_sidebar: default
+title: Track experiments
---
import Translate, {translate} from '@docusaurus/Translate';
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# Track Experiments
-
-
- Track Machine Learning and Deep Learning Experiments.
-
-
Track machine learning experiments with a few lines of code. You can then review the results in an [interactive dashboard](app.md) or export your data to Python for programmatic access using our [Public API](../../ref/python/public-api/README.md).
Utilize W&B Integrations if you use use popular frameworks such as [PyTorch](../integrations/pytorch.md), [Keras](../integrations/keras.md), or [Scikit](../integrations/scikit.md). See our [Integration guides](../integrations/intro.md) for a for a full list of integrations and information on how to add W&B to your code.
@@ -55,7 +50,6 @@ wandb.log_artifact(model)
Depending on your use case, explore the following resources to get started with W&B Experiments:
-* If this is your first time using W&B Artifacts, we recommend you go through the [Experiments Colab notebook](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/intro/Intro_to_Weights_%26_Biases.ipynb).
* Read the [W&B Quickstart](../../quickstart.md) for a step-by-step outline of the W&B Python SDK commands you could use to create, track, and use a dataset artifact.
* Explore this chapter to learn how to:
* Create an experiment
diff --git a/docs/guides/track/jupyter.md b/docs/guides/track/jupyter.md
index 5c4e277da..b3bef3713 100644
--- a/docs/guides/track/jupyter.md
+++ b/docs/guides/track/jupyter.md
@@ -3,16 +3,11 @@ description: >-
se W&B with Jupyter to get interactive visualizations without
leaving your notebook.
displayed_sidebar: default
+title: Track Jupyter notebooks
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Track Jupyter Notebooks
-
-
- Track Jupyter Notebooks
-
-
Use W&B with Jupyter to get interactive visualizations without leaving your notebook. Combine custom analysis, experiments, and prototypes, all fully logged!
## Use cases for W&B with Jupyter notebooks
diff --git a/docs/guides/track/launch.md b/docs/guides/track/launch.md
index 78377ea89..860aea948 100644
--- a/docs/guides/track/launch.md
+++ b/docs/guides/track/launch.md
@@ -1,16 +1,11 @@
---
description: Create a W&B Experiment.
displayed_sidebar: default
+title: Create an experiment
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Create an experiment
-
-
- Start a W&B Experiment
-
-
Use the W&B Python SDK to track machine learning experiments. You can then review the results in an interactive dashboard or export your data to Python for programmatic access with the [W&B Public API](../../ref/python/public-api/README.md).
This guide describes how to use W&B building blocks to create a W&B Experiment.
diff --git a/docs/guides/track/limits.md b/docs/guides/track/limits.md
index 096c6db38..21f892163 100644
--- a/docs/guides/track/limits.md
+++ b/docs/guides/track/limits.md
@@ -3,14 +3,9 @@ description: >-
eep your pages in W&B faster and more responsive by logging within these
suggested bounds.
displayed_sidebar: default
+title: Experiments limits and performance
---
-# Limits and performance
-
-
- Experiments Limits and Performance
-
-
Keep your pages in W&B faster and more responsive by logging within the following suggested bounds.
@@ -81,9 +76,9 @@ Data is saved and tracked even if you log values wider than the recommended amou
Pick a logging frequency that is appropriate to the metric you are logging. As a general rule of thumb, the wider the metric the less frequently you should log it. W&B recommends:
-* Scalars: <100,000 logged points per metric
-* Media: <50,000 logged points per metric
-* Histograms: <10,000 logged points per metric
+* Scalars: \<100,000 logged points per metric
+* Media: \<50,000 logged points per metric
+* Histograms: \<10,000 logged points per metric
```python
@@ -159,7 +154,7 @@ with f as open("large_config.json", "r"):
For faster loading times, keep the total number of runs in a single project under 10,000. Large run counts can slow down project workspaces and runs table operations, especially when grouping is enabled or runs have a large count of distinct metrics.
-If you find that you or your team are frequently accessing the same set of runs (for example, recent runs), consider [bulk moving *other* runs](https://docs.wandb.ai/guides/app/features/runs-table#move-runs-between-projects) to a new project used as an archive, leaving a smaller set of runs in your working project.
+If you find that you or your team are frequently accessing the same set of runs (for example, recent runs), consider [bulk moving *other* runs](/guides/app/features/runs-table#move-runs-between-projects) to a new project used as an archive, leaving a smaller set of runs in your working project.
### Section count
diff --git a/docs/guides/track/log/customize-logging-axes.md b/docs/guides/track/log/customize-logging-axes.md
index dd8a065a1..77cf1ea0f 100644
--- a/docs/guides/track/log/customize-logging-axes.md
+++ b/docs/guides/track/log/customize-logging-axes.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Customize log axes
---
-# Customize Log Axes
-
Use `define_metric` to set a **custom x axis**.Custom x-axes are useful in contexts where you need to log to different time steps in the past during training, asynchronously. For example, this can be useful in RL where you may track the per-episode reward and a per-step reward.
[Try `define_metric` live in Google Colab →](http://wandb.me/define-metric-colab)
diff --git a/docs/guides/track/log/distributed-training.md b/docs/guides/track/log/distributed-training.md
index 76ebcf9ce..44548ed6b 100644
--- a/docs/guides/track/log/distributed-training.md
+++ b/docs/guides/track/log/distributed-training.md
@@ -1,21 +1,15 @@
---
description: Use W&B to log distributed training experiments with multiple GPUs.
displayed_sidebar: default
+title: Log distributed training experiments
---
-# Log distributed training experiments
-
-
- Log distributed training experiments
-
-
-
In distributed training, models are trained using multiple GPUs in parallel. W&B supports two patterns to track distributed training experiments:
1. **One process**: Initialize W&B ([`wandb.init`](../../../ref//python/init.md)) and log experiments ([`wandb.log`](../../../ref//python/log.md)) from a single process. This is a common solution for logging distributed training experiments with the [PyTorch Distributed Data Parallel](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel) (DDP) Class. In some cases, users funnel data over from other processes using a multiprocessing queue (or another communication primitive) to the main logging process.
2. **Many processes**: Initialize W&B ([`wandb.init`](../../../ref//python/init.md)) and log experiments ([`wandb.log`](../../../ref//python/log.md)) in every process. Each process is effectively a separate experiment. Use the `group` parameter when you initialize W&B (`wandb.init(group='group-name')`) to define a shared experiment and group the logged values together in the W&B App UI.
-The proceeding examples demonstrate how to track metrics with W&B using PyTorch DDP on two GPUs on a single machine. [PyTorch DDP](https://pytorch.org/tutorials/intermediate/ddp\_tutorial.html) (`DistributedDataParallel` in`torch.nn`) is a popular library for distributed training. The basic principles apply to any distributed training setup, but the details of implementation may differ.
+The proceeding examples demonstrate how to track metrics with W&B using PyTorch DDP on two GPUs on a single machine. [PyTorch DDP](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) (`DistributedDataParallel` in`torch.nn`) is a popular library for distributed training. The basic principles apply to any distributed training setup, but the details of implementation may differ.
:::info
Explore the code behind these examples in the W&B GitHub examples repository [here](https://github.com/wandb/examples/tree/master/examples/pytorch/pytorch-ddp). Specifically, see the [`log-dpp.py`](https://github.com/wandb/examples/blob/master/examples/pytorch/pytorch-ddp/log-ddp.py) Python script for information on how to implement one process and many process methods.
diff --git a/docs/guides/track/log/intro.md b/docs/guides/track/log/intro.md
index 86eca6643..096fa94a8 100644
--- a/docs/guides/track/log/intro.md
+++ b/docs/guides/track/log/intro.md
@@ -2,14 +2,9 @@
slug: /guides/track/log
description: Keep track of metrics, videos, custom plots, and more
displayed_sidebar: default
+title: Log media and objects in experiments
---
-# Log media and objects in experiments
-
-
- Log Media and Objects in Experiments
-
-
Log a dictionary of metrics, media, or custom objects to a step with the W&B Python SDK. W&B collects the key-value pairs during each step and stores them in one unified dictionary each time you log data with `wandb.log()`. Data logged from your script is saved locally to your machine in a directory called `wandb`, then synced to the W&B cloud or your [private server](../../hosting/intro.md).
:::info
@@ -78,5 +73,5 @@ wandb.log({"loss": 0.314, "epoch": 5,
1. **Compare the best accuracy**: To compare the best value of a metric across runs, set the summary value for that metric. By default, summary is set to the last value you logged for each key. This is useful in the table in the UI, where you can sort and filter runs based on their summary metrics — so you could compare runs in a table or bar chart based on their _best_ accuracy, instead of final accuracy. For example, you could set summary like so: `wandb.run.summary["best_accuracy"] = best_accuracy`
2. **Multiple metrics on one chart**: Log multiple metrics in the same call to `wandb.log`, like this: `wandb.log({"acc'": 0.9, "loss": 0.1})` and they will both be available to plot against in the UI
-3. **Custom x-axis**: Add a custom x-axis to the same log call to visualize your metrics against a different axis in the W&B dashboard. For example: `wandb.log({'acc': 0.9, 'epoch': 3, 'batch': 117})`. To set the default x-axis for a given metric use [Run.define\_metric()](../../../ref/python/run.md#define_metric)
+3. **Custom x-axis**: Add a custom x-axis to the same log call to visualize your metrics against a different axis in the W&B dashboard. For example: `wandb.log({'acc': 0.9, 'epoch': 3, 'batch': 117})`. To set the default x-axis for a given metric use [Run.define_metric()](../../../ref/python/run.md#define_metric)
4. **Log rich media and charts**: `wandb.log` supports the logging of a wide variety of data types, from [media like images and videos](./media.md) to [tables](./log-tables.md) and [charts](../../app/features/custom-charts/intro.md).
diff --git a/docs/guides/track/log/log-models.md b/docs/guides/track/log/log-models.md
index 5959c1505..87806130d 100644
--- a/docs/guides/track/log/log-models.md
+++ b/docs/guides/track/log/log-models.md
@@ -1,6 +1,10 @@
---
displayed_sidebar: default
+title: Log models
---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
+
+
# Log models
@@ -17,10 +21,6 @@ W&B suggests that you use [W&B Artifacts](../../artifacts/intro.md) if you want
For more information on W&B Artifacts and advanced versioning use cases, see the [Artifacts](../../artifacts/intro.md) documentation.
:::
-:::info
-See this [Colab notebook](https://colab.research.google.com/github/wandb/examples/blob/ken-add-new-model-reg-api/colabs/wandb-model-registry/New_Model_Logging_in_W&B.ipynb) for an end-to-end example of how to use the APIs described on this page.
-:::
-
## Log a model to a run
Use the [`log_model`](../../../ref/python/run.md#log_model) to log a model artifact that contains content within a directory you specify. The [`log_model`](../../../ref/python/run.md#log_model) method also marks the resulting model artifact as an output of the W&B run.
@@ -43,7 +43,7 @@ run.log_model(path="", name="")
Optionally provide a name for the model artifact for the `name` parameter. If `name` is not specified, W&B will use the basename of the input path prepended with the run ID as the name.
:::tip
-Keep track of the `name` that you, or W&B assigns, to the model. You will need the name of the model to retrieve the model path with the [`use_model`](https://docs.wandb.ai/ref/python/run#use_model) method.
+Keep track of the `name` that you, or W&B assigns, to the model. You will need the name of the model to retrieve the model path with the [`use_model`](/ref/python/run#use_model) method.
:::
See [`log_model`](../../../ref/python/run.md#log_model) in the API Reference guide for more information on possible parameters.
diff --git a/docs/guides/track/log/log-summary.md b/docs/guides/track/log/log-summary.md
index a50fcf6c8..14cf3a037 100644
--- a/docs/guides/track/log/log-summary.md
+++ b/docs/guides/track/log/log-summary.md
@@ -1,9 +1,8 @@
---
displayed_sidebar: default
+title: Log summary metrics
---
-# Log summary metrics
-
In addition to values that change over time during training, it is often important to track a single value that summarizes a model or a preprocessing step. Log this information in a W&B Run's `summary` dictionary. A Run's summary dictionary can handle numpy arrays, PyTorch tensors or TensorFlow tensors. When a value is one of these types we persist the entire tensor in a binary file and store high level metrics in the summary object such as min, mean, variance, 95th percentile, etc.
The last value logged with `wandb.log` is automatically set as the summary dictionary in a W&B Run. If a summary metric dictionary is modified, the previous value is lost.
diff --git a/docs/guides/track/log/log-tables.md b/docs/guides/track/log/log-tables.md
index c623f886e..59061b0d5 100644
--- a/docs/guides/track/log/log-tables.md
+++ b/docs/guides/track/log/log-tables.md
@@ -1,9 +1,11 @@
---
description: Log tables with W&B.
displayed_sidebar: default
+title: Log tables
---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-# Log tables
+
Use `wandb.Table` to log data to visualize and query with W&B. In this guide, learn how to:
@@ -174,4 +176,4 @@ join_at.add(join_table, "synth_explore")
run.log_artifact(join_at)
```
-Explore this [Colab notebook](https://wandb.ai/stacey/cshanty/reports/Whale2Song-W-B-Tables-for-Audio--Vmlldzo4NDI3NzM) for an example on how to combine two previously stored tables stored in different Artifact objects.
+[Read this tutorial](https://wandb.ai/stacey/cshanty/reports/Whale2Song-W-B-Tables-for-Audio--Vmlldzo4NDI3NzM) for an example on how to combine two previously stored tables stored in different Artifact objects.
diff --git a/docs/guides/track/log/logging-faqs.md b/docs/guides/track/log/logging-faqs.md
index bec9d4a04..e2136c63d 100644
--- a/docs/guides/track/log/logging-faqs.md
+++ b/docs/guides/track/log/logging-faqs.md
@@ -3,16 +3,11 @@ description: >-
Answers to frequently asked questions about tracking data from machine
learning experiments with W&B Experiments.
displayed_sidebar: default
+title: Logging FAQ
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Logging FAQs
-
-
- Frequently Asked Questions About Logging Data from Experiments
-
-
### How can I organize my logged charts and media in the W&B UI?
We treat `/` as a separator for organizing logged panels in the W&B UI. By default, the component of the logged item's name before a `/` is used to define a group of panel called a "Panel Section".
diff --git a/docs/guides/track/log/media.md b/docs/guides/track/log/media.md
index d5cb8461d..54afbaca4 100644
--- a/docs/guides/track/log/media.md
+++ b/docs/guides/track/log/media.md
@@ -1,12 +1,13 @@
---
description: Log rich media, from 3D point clouds and molecules to HTML and histograms
displayed_sidebar: default
+title: Log media and objects
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-
-# Log media and objects
+
We support images, video, audio, and more. Log rich media to explore your results and visually compare your runs, models, and datasets. Read on for examples and how-to guides.
@@ -14,10 +15,8 @@ We support images, video, audio, and more. Log rich media to explore your result
Looking for reference docs for our media types? You want [this page](../../../ref/python/data-types/README.md).
:::
-
-
:::info
-You can see working code to log all of these media objects in [this Colab Notebook](http://wandb.me/media-colab), check out what the results look like on wandb.ai [here](https://wandb.ai/lavanyashukla/visualize-predictions/reports/Visualize-Model-Predictions--Vmlldzo1NjM4OA), and follow along with a video tutorial, linked above.
+You can [see what the results look like on wandb.ai](https://wandb.ai/lavanyashukla/visualize-predictions/reports/Visualize-Model-Predictions--Vmlldzo1NjM4OA), and [follow along with a video tutorial](https://www.youtube.com/watch?v=96MxRvx15Ts).
:::
## Images
@@ -41,7 +40,7 @@ It's recommended to log fewer than 50 images per step to prevent logging from be
]}>
-Provide arrays directly when constructing images manually, e.g. using [`make_grid` from `torchvision`](https://pytorch.org/vision/stable/utils.html#torchvision.utils.make\_grid).
+Provide arrays directly when constructing images manually, e.g. using [`make_grid` from `torchvision`](https://pytorch.org/vision/stable/utils.html#torchvision.utils.make_grid).
Arrays are converted to png using [Pillow](https://pillow.readthedocs.io/en/stable/index.html).
@@ -101,7 +100,7 @@ To log multiple masks, log a mask dictionary with multiple keys, as in the code
[See a live example](https://app.wandb.ai/stacey/deep-drive/reports/Image-Masks-for-Semantic-Segmentation--Vmlldzo4MTUwMw)
-[Sample code](https://colab.research.google.com/drive/1SOVl3EvW82Q4QKJXX6JtHye4wFix\_P4J)
+[Sample code](https://colab.research.google.com/drive/1SOVl3EvW82Q4QKJXX6JtHye4wFix_P4J)
```python
mask_data = np.array([[1, 2, 2, ..., 2, 2, 1], ...])
@@ -123,7 +122,7 @@ mask_img = wandb.Image(
Log bounding boxes with images, and use filters and toggles to dynamically visualize different sets of boxes in the UI.
-![](@site/static/images/track/bb-docs.jpeg)
+![](/images/track/bb-docs.jpeg)
[See a live example](https://app.wandb.ai/stacey/yolo-drive/reports/Bounding-Boxes-for-Object-Detection--Vmlldzo4Nzg4MQ)
@@ -327,7 +326,7 @@ wandb.log(
![Ground truth and prediction of a headphones point cloud](/images/track/ground_truth_prediction_of_3d_point_clouds.png)
-[See a live example](https://app.wandb.ai/nbaryd/SparseConvNet-examples\_3d\_segmentation/reports/Point-Clouds--Vmlldzo4ODcyMA)
+[See a live example](https://app.wandb.ai/nbaryd/SparseConvNet-examples_3d_segmentation/reports/Point-Clouds--Vmlldzo4ODcyMA)
@@ -428,7 +427,7 @@ When your run finishes, you'll be able to interact with 3D visualizations of you
[See a live example using AlphaFold](http://wandb.me/alphafold-workspace)
-![](@site/static/images/track/docs-molecule.png)
+![](/images/track/docs-molecule.png)
diff --git a/docs/guides/track/log/plots.md b/docs/guides/track/log/plots.md
index a189947ab..eeefbdccb 100644
--- a/docs/guides/track/log/plots.md
+++ b/docs/guides/track/log/plots.md
@@ -1,16 +1,11 @@
---
description: Create and track plots from machine learning experiments.
displayed_sidebar: default
+title: Create and track plots from experiments
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Log plots
-
-
- Log and Track Plots from W&B Experiments.
-
-
Using the methods in `wandb.plot`, you can track charts with `wandb.log`, including charts that change over time during training. To learn more about our custom charting framework, check out [this guide](../../app/features/custom-charts/walkthrough.md).
### Basic charts
@@ -161,7 +156,7 @@ These preset charts have built-in `wandb.plot` methods that make it quick and ea
`wandb.plot.pr_curve()`
-Create a [Precision-Recall curve](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision\_recall\_curve.html#sklearn.metrics.precision\_recall\_curve) in one line:
+Create a [Precision-Recall curve](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve) in one line:
```python
wandb.log({"pr": wandb.plot.pr_curve(ground_truth, predictions)})
@@ -184,7 +179,7 @@ You can log this whenever your code has access to:
`wandb.plot.roc_curve()`
-Create an [ROC curve](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc\_curve.html#sklearn.metrics.roc\_curve) in one line:
+Create an [ROC curve](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve) in one line:
```python
wandb.log({"roc": wandb.plot.roc_curve(ground_truth, predictions)})
@@ -201,13 +196,13 @@ You can log this whenever your code has access to:
[See in the app](https://wandb.ai/wandb/plots/reports/Plot-ROC-Curves--VmlldzoyNjk3MDE)
-[Run the code](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/wandb-log/Plot\_ROC\_Curves\_with\_W%26B.ipynb)
+[Run the code](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/wandb-log/Plot_ROC_Curves_with_W%26B.ipynb)
`wandb.plot.confusion_matrix()`
-Create a multi-class [confusion matrix](https://scikit-learn.org/stable/auto\_examples/model\_selection/plot\_confusion\_matrix.html) in one line:
+Create a multi-class [confusion matrix](https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html) in one line:
```python
cm = wandb.plot.confusion_matrix(
@@ -227,7 +222,7 @@ You can log this wherever your code has access to:
[See in the app](https://wandb.ai/wandb/plots/reports/Confusion-Matrix--VmlldzozMDg1NTM)
-[Run the code](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/wandb-log/Log\_a\_Confusion\_Matrix\_with\_W%26B.ipynb)
+[Run the code](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/wandb-log/Log_a_Confusion_Matrix_with_W%26B.ipynb)
diff --git a/docs/guides/track/log/working-with-csv.md b/docs/guides/track/log/working-with-csv.md
index b93e1cf39..481925b56 100644
--- a/docs/guides/track/log/working-with-csv.md
+++ b/docs/guides/track/log/working-with-csv.md
@@ -1,14 +1,9 @@
---
description: Importing and logging data into W&B
displayed_sidebar: default
+title: Track CSV files with experiments
---
-# Log data and experiments from a CSV file
-
-
- Track CSV files with W&B Experiments
-
-
Use the W&B Python Library to log a CSV file and visualize it in a [W&B Dashboard](../app.md). W&B Dashboard are the central place to organize and visualize results from your machine learning models. This is particularly useful if you have a [CSV file that contains information of previous machine learning experiments](#import-and-log-your-csv-of-experiments) that are not logged in W&B or if you have [CSV file that contains a dataset](#import-and-log-your-dataset-csv-file).
## Import and log your dataset CSV file
diff --git a/docs/guides/track/public-api-guide.md b/docs/guides/track/public-api-guide.md
index 161b06d30..9581ef857 100644
--- a/docs/guides/track/public-api-guide.md
+++ b/docs/guides/track/public-api-guide.md
@@ -1,17 +1,12 @@
---
description: Import data from MLFlow, export or update data that you have saved to W&B
displayed_sidebar: default
+title: Import and export data
---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Import and export data
-
-
- Import & Export Data to W&B
-
-
Export data or import data from MLFlow or between W&B instances with W&B Public APIs.
:::info
@@ -243,7 +238,7 @@ these are the different outputs for the above run object attributes
### Sampling
-The default history method samples the metrics to a fixed number of samples (the default is 500, you can change this with the `samples` \_\_ argument). If you want to export all of the data on a large run, you can use the `run.scan_history()` method. For more details see the [API Reference](https://docs.wandb.ai/ref/python/public-api).
+The default history method samples the metrics to a fixed number of samples (the default is 500, you can change this with the `samples` __ argument). If you want to export all of the data on a large run, you can use the `run.scan_history()` method. For more details see the [API Reference](/ref/python/public-api).
### Querying Multiple Runs
diff --git a/docs/guides/track/save-restore.md b/docs/guides/track/save-restore.md
index dda983803..dddb7531a 100644
--- a/docs/guides/track/save-restore.md
+++ b/docs/guides/track/save-restore.md
@@ -1,14 +1,9 @@
---
description: Save files to the cloud and restore them locally later
displayed_sidebar: default
+title: Save & restore files to the cloud
---
-# Save & Restore Files
-
-
- Save and Restore Files to the Cloud
-
-
This guide first demonstrates how to save files to the cloud with `wandb.save`, then demonstrates how they can be re-created locally with `wandb.restore`.
## Saving Files
@@ -28,7 +23,7 @@ If you want to sync files as they're being written, you can specify a filename o
### Examples of `wandb.save`
-See [this report](https://app.wandb.ai/lavanyashukla/save\_and\_restore/reports/Saving-and-Restoring-Models-with-W%26B--Vmlldzo3MDQ3Mw) for a complete working example.
+See [this report](https://app.wandb.ai/lavanyashukla/save_and_restore/reports/Saving-and-Restoring-Models-with-W%26B--Vmlldzo3MDQ3Mw) for a complete working example.
```python
# Save a model file from the current directory
@@ -53,7 +48,7 @@ W&B's local run directories are by default inside the `./wandb` directory relati
* **now** - sync this file to a wandb server immediately, don't continue syncing if it changes
* **end** - only sync the file when the run finishes
-You can also specify the **base\_path** argument to `wandb.save`. This would allow you to maintain a directory hierarchy, for example:
+You can also specify the **base_path** argument to `wandb.save`. This would allow you to maintain a directory hierarchy, for example:
```python
wandb.save(path="./results/eval/*", base_path="./results", policy="now")
@@ -100,7 +95,7 @@ Common use cases:
### Examples of `wandb.restore`
-See [this report](https://app.wandb.ai/lavanyashukla/save\_and\_restore/reports/Saving-and-Restoring-Models-with-W%26B--Vmlldzo3MDQ3Mw) for a complete working example.
+See [this report](https://app.wandb.ai/lavanyashukla/save_and_restore/reports/Saving-and-Restoring-Models-with-W%26B--Vmlldzo3MDQ3Mw) for a complete working example.
```python
# restore a model file from a specific run by user "vanpelt" in "my-project"
@@ -120,7 +115,7 @@ my_predefined_model.load_weights(weights_file.name)
### How do I ignore files?
-You can edit the `wandb/settings` file and set `ignore_globs` equal to a comma separated list of [globs](https://en.wikipedia.org/wiki/Glob\_\(programming\)). You can also set the `WANDB_IGNORE_GLOBS` [environment variable](./environment-variables.md). A common use case is to prevent the git patch that we automatically create from being uploaded i.e. `WANDB_IGNORE_GLOBS=*.patch`.
+You can edit the `wandb/settings` file and set `ignore_globs` equal to a comma separated list of [globs](https://en.wikipedia.org/wiki/Glob_\(programming\)). You can also set the `WANDB_IGNORE_GLOBS` [environment variable](./environment-variables.md). A common use case is to prevent the git patch that we automatically create from being uploaded i.e. `WANDB_IGNORE_GLOBS=*.patch`.
### Change directory for saving files
diff --git a/docs/guides/track/tracking-faq.md b/docs/guides/track/tracking-faq.md
index 00e4b32a6..278214b97 100644
--- a/docs/guides/track/tracking-faq.md
+++ b/docs/guides/track/tracking-faq.md
@@ -1,13 +1,10 @@
---
description: Answers to frequently asked question about W&B Experiments.
displayed_sidebar: default
+title: Experiments FAQ
---
-
-# Experiments FAQ
-
-
- Frequently Asked Questions About Experiments
-
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
The proceeding questions are commonly asked questions about W&B Artifacts.
diff --git a/docs/guides/weave_platform.md b/docs/guides/weave_platform.md
index 01dae8fd2..532792c94 100644
--- a/docs/guides/weave_platform.md
+++ b/docs/guides/weave_platform.md
@@ -1,11 +1,10 @@
---
slug: /guides/weave
displayed_sidebar: default
+title: W&B Weave
---
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# W&B Weave
-
Weave is a lightweight toolkit for tracking and evaluating LLM applications. Use W&B Weave to visualize and inspect the execution flow of your LLMs, analyze the inputs and outputs of your LLMs, view the intermediate results and securely store and manage your prompts and LLM chain configurations.
@@ -27,7 +26,3 @@ Depending on your use case, explore the following resources to get started with
* [Quickstart: Track inputs and outputs of LLM calls](https://wandb.github.io/weave/quickstart)
* [Build an Evaluation pipeline tutorial](https://wandb.github.io/weave/tutorial-eval)
* [Model-Based Evaluation of RAG applications tutorial](https://wandb.github.io/weave/tutorial-rag)
-
-
\ No newline at end of file
diff --git a/docs/quickstart.md b/docs/quickstart.md
index 7189e01bf..be90c4cd1 100644
--- a/docs/quickstart.md
+++ b/docs/quickstart.md
@@ -1,13 +1,11 @@
---
-description: W&B Quickstart.
+description: W&B Quickstart
displayed_sidebar: default
+title: W&B Quickstart
---
-
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Quickstart
-
Install W&B and start tracking your machine learning experiments in minutes.
## 1. Create an account and install W&B
diff --git a/docs/ref/cli/wandb-launch-agent.md b/docs/ref/cli/wandb-launch-agent.md
index 27714e8cc..019aa5035 100644
--- a/docs/ref/cli/wandb-launch-agent.md
+++ b/docs/ref/cli/wandb-launch-agent.md
@@ -12,7 +12,7 @@ Run a W&B launch agent.
| **Option** | **Description** |
| :--- | :--- |
-| -q, --queue | The name of a queue for the agent to watch. Multiple -q flags supported. |
+| -q, --queue \ | The name of a queue for the agent to watch. Multiple -q flags supported. |
| -e, --entity | The entity to use. Defaults to current logged-in user |
| -l, --log-file | Destination for internal agent logs. Use - for stdout. By default all agents logs will go to debug.log in your wandb/ subdirectory or WANDB_DIR if set. |
| -j, --max-jobs | The maximum number of launch jobs this agent can run in parallel. Defaults to 1. Set to -1 for no upper limit |
diff --git a/docs/ref/js/README.md b/docs/ref/js/README.md
index 0391a6f2d..11c1835fd 100644
--- a/docs/ref/js/README.md
+++ b/docs/ref/js/README.md
@@ -53,7 +53,7 @@ Node/CommonJS:
const wandb = require('@wandb/sdk').default;
```
-We're currently missing a lot of the functionality found in our Python SDK, but basic logging functionality is available. We'll be adding additional features like [Tables](https://docs.wandb.ai/guides/tables?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=readme) soon.
+We're currently missing a lot of the functionality found in our Python SDK, but basic logging functionality is available. We'll be adding additional features like [Tables](/guides/tables?utm_source=github&utm_medium=code&utm_campaign=wandb&utm_content=readme) soon.
### Authentication and Settings
@@ -61,7 +61,7 @@ In node environments we look for `process.env.WANDB_API_KEY` and prompt for it's
# Integrations
-Our [Python integrations](https://docs.wandb.ai/guides/integrations) are widely used by our community, and we hope to build out more JavaScript integrations to help LLM app builders leverage whatever tool they want.
+Our [Python integrations](/guides/integrations) are widely used by our community, and we hope to build out more JavaScript integrations to help LLM app builders leverage whatever tool they want.
If you have any requests for additional integrations, we'd love you to open an issue with details about the request.
diff --git a/docs/ref/python/README.md b/docs/ref/python/README.md
index 4b5ce7fb8..fb21fb9ba 100644
--- a/docs/ref/python/README.md
+++ b/docs/ref/python/README.md
@@ -14,7 +14,7 @@ For guides and examples, see https://docs.wandb.ai.
For scripts and interactive notebooks, see https://github.com/wandb/examples.
-For reference documentation, see https://docs.wandb.com/ref/python.
+For reference documentation, see /ref/python.
## Classes
diff --git a/docs/ref/python/artifact.md b/docs/ref/python/artifact.md
index 4ce85e2ab..ffcb2852f 100644
--- a/docs/ref/python/artifact.md
+++ b/docs/ref/python/artifact.md
@@ -33,7 +33,7 @@ begin with `add`. Once the artifact has all the desired files, you can call
| Attributes | |
| :--- | :--- |
-| `aliases` | List of one or more semantically-friendly references or identifying "nicknames" assigned to an artifact version. Aliases are mutable references that you can programmatically reference. Change an artifact's alias with the W&B App UI or programmatically. See [Create new artifact versions](https://docs.wandb.ai/guides/artifacts/create-a-new-artifact-version) for more information. |
+| `aliases` | List of one or more semantically-friendly references or identifying "nicknames" assigned to an artifact version. Aliases are mutable references that you can programmatically reference. Change an artifact's alias with the W&B App UI or programmatically. See [Create new artifact versions](/guides/artifacts/create-a-new-artifact-version) for more information. |
| `collection` | The collection this artifact was retrieved from. A collection is an ordered group of artifact versions. If this artifact was retrieved from a portfolio / linked collection, that collection will be returned rather than the collection that an artifact version originated from. The collection that an artifact originates from is known as the source sequence. |
| `commit_hash` | The hash returned when this artifact was committed. |
| `created_at` | Timestamp when the artifact was created. |
@@ -44,16 +44,16 @@ begin with `add`. Once the artifact has all the desired files, you can call
| `id` | The artifact's ID. |
| `manifest` | The artifact's manifest. The manifest lists all of its contents, and can't be changed once the artifact has been logged. |
| `metadata` | User-defined artifact metadata. Structured data associated with the artifact. |
-| `name` | The artifact name and version in its secondary (portfolio) collection. A string with the format {collection}:{alias}. Before the artifact is saved, contains only the name since the version is not yet known. |
+| `name` | The artifact name and version in its secondary (portfolio) collection. A string with the format `{collection}:{alias}`. Before the artifact is saved, contains only the name since the version is not yet known. |
| `project` | The name of the project of the secondary (portfolio) artifact collection. |
| `qualified_name` | The entity/project/name of the secondary (portfolio) collection. |
| `size` | The total size of the artifact in bytes. Includes any references tracked by this artifact. |
| `source_collection` | The artifact's primary (sequence) collection. |
| `source_entity` | The name of the entity of the primary (sequence) artifact collection. |
-| `source_name` | The artifact name and version in its primary (sequence) collection. A string with the format {collection}:{alias}. Before the artifact is saved, contains only the name since the version is not yet known. |
+| `source_name` | The artifact name and version in its primary (sequence) collection. A string with the format `{collection}:{alias}`. Before the artifact is saved, contains only the name since the version is not yet known. |
| `source_project` | The name of the project of the primary (sequence) artifact collection. |
| `source_qualified_name` | The entity/project/name of the primary (sequence) collection. |
-| `source_version` | The artifact's version in its primary (sequence) collection. A string with the format "v{number}". |
+| `source_version` | The artifact's version in its primary (sequence) collection. A string with the format `v{number}`. |
| `state` | The status of the artifact. One of: "PENDING", "COMMITTED", or "DELETED". |
| `tags` | List of one or more tags assigned to this artifact version. |
| `ttl` | The time-to-live (TTL) policy of an artifact. Artifacts are deleted shortly after a TTL policy's duration passes. If set to `None`, the artifact deactivates TTL policies and will be not scheduled for deletion, even if there is a team default TTL. An artifact inherits a TTL policy from the team default if the team administrator defines a default TTL and there is no custom policy set on an artifact. |
@@ -166,7 +166,7 @@ Add a reference denoted by a URI to the artifact.
Unlike files or directories that you add to an artifact, references are not
uploaded to W&B. For more information,
-see [Track external files](https://docs.wandb.ai/guides/artifacts/track-external-files).
+see [Track external files](/guides/artifacts/track-external-files).
By default, the following schemes are supported:
diff --git a/docs/ref/python/data-types/table.md b/docs/ref/python/data-types/table.md
index fb5c68e43..8c1b519eb 100644
--- a/docs/ref/python/data-types/table.md
+++ b/docs/ref/python/data-types/table.md
@@ -18,7 +18,7 @@ This means you can embed `Images`, `Video`, `Audio`, and other sorts of rich, an
directly in Tables, alongside other traditional scalar values.
This class is the primary class used to generate the Table Visualizer
-in the UI: https://docs.wandb.ai/guides/data-vis/tables.
+in the UI: /guides/data-vis/tables.
| Arguments | |
| :--- | :--- |
diff --git a/docs/ref/python/init.md b/docs/ref/python/init.md
index b29dadd31..afb584a33 100644
--- a/docs/ref/python/init.md
+++ b/docs/ref/python/init.md
@@ -76,7 +76,7 @@ child process, you must explicitly call `wandb.finish` at the end of the
child process.
For more on using `wandb.init()`, including detailed examples, check out our
-[guide and FAQs](https://docs.wandb.ai/guides/track/launch).
+[guide and FAQs](/guides/track/launch).
| Arguments | |
| :--- | :--- |
@@ -84,13 +84,13 @@ For more on using `wandb.init()`, including detailed examples, check out our
| `entity` | (str, optional) An entity is a username or team name where you're sending runs. This entity must exist before you can send runs there, so make sure to create your account or team in the UI before starting to log runs. If you don't specify an entity, the run will be sent to your default entity. Change your default entity in [your settings](https://wandb.ai/settings) under "default location to create new projects". |
| `config` | (dict, argparse, absl.flags, str, optional) This sets `wandb.config`, a dictionary-like object for saving inputs to your job, like hyperparameters for a model or settings for a data preprocessing job. The config will show up in a table in the UI that you can use to group, filter, and sort runs. Keys should not contain `.` in their names, and values should be under 10 MB. If dict, argparse or absl.flags: will load the key value pairs into the `wandb.config` object. If str: will look for a yaml file by that name, and load config from that file into the `wandb.config` object. |
| `save_code` | (bool, optional) Turn this on to save the main script or notebook to W&B. This is valuable for improving experiment reproducibility and to diff code across experiments in the UI. By default this is off, but you can flip the default behavior to on in [your settings page](https://wandb.ai/settings). |
-| `group` | (str, optional) Specify a group to organize individual runs into a larger experiment. For example, you might be doing cross validation, or you might have multiple jobs that train and evaluate a model against different test sets. Group gives you a way to organize runs together into a larger whole, and you can toggle this on and off in the UI. For more details, see our [guide to grouping runs](https://docs.wandb.com/guides/runs/grouping). |
+| `group` | (str, optional) Specify a group to organize individual runs into a larger experiment. For example, you might be doing cross validation, or you might have multiple jobs that train and evaluate a model against different test sets. Group gives you a way to organize runs together into a larger whole, and you can toggle this on and off in the UI. For more details, see our [guide to grouping runs](/guides/runs/grouping). |
| `job_type` | (str, optional) Specify the type of run, which is useful when you're grouping runs together into larger experiments using group. For example, you might have multiple jobs in a group, with job types like train and eval. Setting this makes it easy to filter and group similar runs together in the UI so you can compare apples to apples. |
| `tags` | (list, optional) A list of strings, which will populate the list of tags on this run in the UI. Tags are useful for organizing runs together, or applying temporary labels like "baseline" or "production". It's easy to add and remove tags in the UI, or filter down to just runs with a specific tag. If you are resuming a run, its tags will be overwritten by the tags you pass to `wandb.init()`. If you want to add tags to a resumed run without overwriting its existing tags, use `run.tags += ["new_tag"]` after `wandb.init()`. |
| `name` | (str, optional) A short display name for this run, which is how you'll identify this run in the UI. By default, we generate a random two-word name that lets you easily cross-reference runs from the table to charts. Keeping these run names short makes the chart legends and tables easier to read. If you're looking for a place to save your hyperparameters, we recommend saving those in config. |
| `notes` | (str, optional) A longer description of the run, like a `-m` commit message in git. This helps you remember what you were doing when you ran this run. |
| `dir` | (str or pathlib.Path, optional) An absolute path to a directory where metadata will be stored. When you call `download()` on an artifact, this is the directory where downloaded files will be saved. By default, this is the `./wandb` directory. |
-| `resume` | (bool, str, optional) Sets the resuming behavior. Options: `"allow"`, `"must"`, `"never"`, `"auto"` or `None`. Defaults to `None`. Cases: - `None` (default): If the new run has the same ID as a previous run, this run overwrites that data. - `"auto"` (or `True`): if the previous run on this machine crashed, automatically resume it. Otherwise, start a new run. - `"allow"`: if id is set with `init(id="UNIQUE_ID")` or `WANDB_RUN_ID="UNIQUE_ID"` and it is identical to a previous run, wandb will automatically resume the run with that id. Otherwise, wandb will start a new run. - `"never"`: if id is set with `init(id="UNIQUE_ID")` or `WANDB_RUN_ID="UNIQUE_ID"` and it is identical to a previous run, wandb will crash. - `"must"`: if id is set with `init(id="UNIQUE_ID")` or `WANDB_RUN_ID="UNIQUE_ID"` and it is identical to a previous run, wandb will automatically resume the run with the id. Otherwise, wandb will crash. See [our guide to resuming runs](https://docs.wandb.com/guides/runs/resuming) for more. |
+| `resume` | (bool, str, optional) Sets the resuming behavior. Options: `"allow"`, `"must"`, `"never"`, `"auto"` or `None`. Defaults to `None`. Cases: - `None` (default): If the new run has the same ID as a previous run, this run overwrites that data. - `"auto"` (or `True`): if the previous run on this machine crashed, automatically resume it. Otherwise, start a new run. - `"allow"`: if id is set with `init(id="UNIQUE_ID")` or `WANDB_RUN_ID="UNIQUE_ID"` and it is identical to a previous run, wandb will automatically resume the run with that id. Otherwise, wandb will start a new run. - `"never"`: if id is set with `init(id="UNIQUE_ID")` or `WANDB_RUN_ID="UNIQUE_ID"` and it is identical to a previous run, wandb will crash. - `"must"`: if id is set with `init(id="UNIQUE_ID")` or `WANDB_RUN_ID="UNIQUE_ID"` and it is identical to a previous run, wandb will automatically resume the run with the id. Otherwise, wandb will crash. See [our guide to resuming runs](/guides/runs/resuming) for more. |
| `reinit` | (bool, optional) Allow multiple `wandb.init()` calls in the same process. (default: `False`) |
| `magic` | (bool, dict, or str, optional) The bool controls whether we try to auto-instrument your script, capturing basic details of your run without you having to add more wandb code. (default: `False`) You can also pass a dict, json string, or yaml filename. |
| `config_exclude_keys` | (list, optional) string keys to exclude from `wandb.config`. |
@@ -101,11 +101,11 @@ For more on using `wandb.init()`, including detailed examples, check out our
| `force` | (bool, optional) If `True`, this crashes the script if a user isn't logged in to W&B. If `False`, this will let the script run in offline mode if a user isn't logged in to W&B. (default: `False`) |
| `sync_tensorboard` | (bool, optional) Synchronize wandb logs from tensorboard or tensorboardX and save the relevant events file. (default: `False`) |
| `tensorboard` | (bool, optional) Alias for `sync_tensorboard`, deprecated. |
-| `monitor_gym` | (bool, optional) Automatically log videos of environment when using OpenAI Gym. (default: `False`) See [our guide to this integration](https://docs.wandb.com/guides/integrations/openai-gym). |
-| `id` | (str, optional) A unique ID for this run, used for resuming. It must be unique in the project, and if you delete a run you can't reuse the ID. Use the `name` field for a short descriptive name, or `config` for saving hyperparameters to compare across runs. The ID cannot contain the following special characters: `/\#?%:`. See [our guide to resuming runs](https://docs.wandb.com/guides/runs/resuming). |
-| `fork_from` | (str, optional) A string with the format {run_id}?_step={step} describing a moment in a previous run to fork a new run from. Creates a new run that picks up logging history from the specified run at the specified moment. The target run must be in the current project. Example: `fork_from="my-run-id?_step=1234"`. |
-| `resume_from` | (str, optional) A string with the format {run_id}?_step={step} describing a moment in a previous run to resume a run from. This allows users to truncate the history logged to a run at an intermediate step and resume logging from that step. It uses run forking under the hood. The target run must be in the current project. Example: `resume_from="my-run-id?_step=1234"`. |
-| `settings` | (dict, wandb.Settings, optional) Settings to use for this run. (default: None) |
+| `monitor_gym` | (bool, optional) Automatically log videos of environment when using OpenAI Gym. (default: `False`) See [our guide to this integration](/guides/integrations/openai-gym). |
+| `id` | (str, optional) A unique ID for this run, used for resuming. It must be unique in the project, and if you delete a run you can't reuse the ID. Use the `name` field for a short descriptive name, or `config` for saving hyperparameters to compare across runs. The ID cannot contain the following special characters: `/\#?%:`. See [our guide to resuming runs](/guides/runs/resuming). |
+| `fork_from` | (str, optional) A string with the format `{run_id}?_step={step}` describing a moment in a previous run to fork a new run from. Creates a new run that picks up logging history from the specified run at the specified moment. The target run must be in the current project. Example: `fork_from="my-run-id?_step=1234"`. |
+| `resume_from` | (str, optional) A string with the format `{run_id}?_step={step}` describing a moment in a previous run to resume a run from. This allows users to truncate the history logged to a run at an intermediate step and resume logging from that step. It uses run forking under the hood. The target run must be in the current project. Example: `resume_from="my-run-id?_step=1234"`. |
+| `settings` | `(dict, wandb.Settings, optional)` Settings to use for this run. (default: None) |
#### Examples:
diff --git a/docs/ref/python/integrations/keras/wandbmodelcheckpoint.md b/docs/ref/python/integrations/keras/wandbmodelcheckpoint.md
index 52321f254..6f4fbc811 100644
--- a/docs/ref/python/integrations/keras/wandbmodelcheckpoint.md
+++ b/docs/ref/python/integrations/keras/wandbmodelcheckpoint.md
@@ -28,7 +28,7 @@ here: https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpo
This callback is to be used in conjunction with training using `model.fit()` to save
a model or weights (in a checkpoint file) at some interval. The model checkpoints
will be logged as W&B Artifacts. You can learn more here:
-https://docs.wandb.ai/guides/artifacts
+/guides/artifacts
This callback provides the following features:
- Save the model that has achieved "best performance" based on "monitor".
diff --git a/docs/ref/python/integrations/wandbtracer.md b/docs/ref/python/integrations/wandbtracer.md
index 2baeb02aa..d57981b49 100644
--- a/docs/ref/python/integrations/wandbtracer.md
+++ b/docs/ref/python/integrations/wandbtracer.md
@@ -1,10 +1,8 @@
-# WandbTracer
-
-
-
-[![](https://www.tensorflow.org/images/GitHub-Mark-32px.png)View source on GitHub](https://www.github.com/wandb/client/tree/c4726707ed83ebb270a2cf84c4fd17b8684ff699/wandb/integration/langchain/wandb_tracer.py#L99-L281)
-
+---
+title: WandbTracer
+---
+[View source on GitHub](https://www.github.com/wandb/client/tree/c4726707ed83ebb270a2cf84c4fd17b8684ff699/wandb/integration/langchain/wandb_tracer.py#L99-L281)
Callback Handler that logs to Weights and Biases.
@@ -12,13 +10,8 @@ Callback Handler that logs to Weights and Biases.
WandbTracer() -> Any
```
-
-
-
This handler will log the model architecture and run traces to Weights and Biases. This will ensure that all LangChain activity is logged to W&B.
-
-
| Attributes | |
| :--- | :--- |
| `always_verbose` | Whether to call verbose callbacks even if verbose is False. |
@@ -26,14 +19,10 @@ This handler will log the model architecture and run traces to Weights and Biase
| `ignore_chain` | Whether to ignore chain callbacks. |
| `ignore_llm` | Whether to ignore LLM callbacks. |
-
-
## Methods
### `finish`
-
-
[View source](https://www.github.com/wandb/client/tree/c4726707ed83ebb270a2cf84c4fd17b8684ff699/wandb/integration/langchain/wandb_tracer.py#L152-L162)
```python
@@ -49,8 +38,6 @@ python script. -->
### `finish_run`
-
-
[View source](https://www.github.com/wandb/client/tree/c4726707ed83ebb270a2cf84c4fd17b8684ff699/wandb/integration/langchain/wandb_tracer.py#L202-L211)
```python
@@ -59,11 +46,8 @@ finish_run() -> None
Waits for W&B data to upload.
-
### `init`
-
-
[View source](https://www.github.com/wandb/client/tree/c4726707ed83ebb270a2cf84c4fd17b8684ff699/wandb/integration/langchain/wandb_tracer.py#L111-L150)
```python
@@ -77,10 +61,8 @@ init(
Sets up a WandbTracer and makes it the default handler.
-
#### Parameters:
-
* **`run_args`**: (dict, optional) Arguments to pass to `wandb.init()`. If not provided, `wandb.init()` will be
called with no arguments. Please refer to the `wandb.init` for more details.
* **`include_stdout`**: (bool, optional) If True, the `StdOutCallbackHandler` will be added to the list of
@@ -103,8 +85,6 @@ notebook), as it will only create a new run if the run_args differ.
### `init_run`
-
-
[View source](https://www.github.com/wandb/client/tree/c4726707ed83ebb270a2cf84c4fd17b8684ff699/wandb/integration/langchain/wandb_tracer.py#L164-L200)
```python
@@ -115,10 +95,8 @@ init_run(
Initialize wandb if it has not been initialized.
-
#### Parameters:
-
* **`run_args`**: (dict, optional) Arguments to pass to `wandb.init()`. If not provided, `wandb.init()` will be
called with no arguments. Please refer to the `wandb.init` for more details.
@@ -130,8 +108,6 @@ want to manually initialize the tracer and add it to the list of handlers.
### `load_default_session`
-
-
[View source](https://www.github.com/wandb/client/tree/c4726707ed83ebb270a2cf84c4fd17b8684ff699/wandb/integration/langchain/wandb_tracer.py#L264-L267)
```python
@@ -140,11 +116,8 @@ load_default_session() -> "TracerSession"
Load the default tracing session and set it as the Tracer's session.
-
### `load_session`
-
-
[View source](https://www.github.com/wandb/client/tree/c4726707ed83ebb270a2cf84c4fd17b8684ff699/wandb/integration/langchain/wandb_tracer.py#L259-L262)
```python
@@ -155,11 +128,8 @@ load_session(
Load a session from the tracer.
-
### `new_session`
-
-
```python
new_session(
name: Optional[str] = None,
@@ -169,11 +139,8 @@ new_session(
NOT thread safe, do not call this method from multiple threads.
-
### `on_agent_action`
-
-
```python
on_agent_action(
action: AgentAction,
@@ -183,11 +150,8 @@ on_agent_action(
Do nothing.
-
### `on_agent_finish`
-
-
```python
on_agent_finish(
finish: AgentFinish,
@@ -197,11 +161,8 @@ on_agent_finish(
Handle an agent finish message.
-
### `on_chain_end`
-
-
```python
on_chain_end(
outputs: Dict[str, Any],
@@ -211,11 +172,8 @@ on_chain_end(
End a trace for a chain run.
-
### `on_chain_error`
-
-
```python
on_chain_error(
error: Union[Exception, KeyboardInterrupt],
@@ -225,11 +183,8 @@ on_chain_error(
Handle an error for a chain run.
-
### `on_chain_start`
-
-
```python
on_chain_start(
serialized: Dict[str, Any],
@@ -240,11 +195,8 @@ on_chain_start(
Start a trace for a chain run.
-
### `on_llm_end`
-
-
```python
on_llm_end(
response: LLMResult,
@@ -254,11 +206,8 @@ on_llm_end(
End a trace for an LLM run.
-
### `on_llm_error`
-
-
```python
on_llm_error(
error: Union[Exception, KeyboardInterrupt],
@@ -268,11 +217,8 @@ on_llm_error(
Handle an error for an LLM run.
-
### `on_llm_new_token`
-
-
```python
on_llm_new_token(
token: str,
@@ -282,11 +228,8 @@ on_llm_new_token(
Handle a new token for an LLM run.
-
### `on_llm_start`
-
-
```python
on_llm_start(
serialized: Dict[str, Any],
@@ -297,11 +240,8 @@ on_llm_start(
Start a trace for an LLM run.
-
### `on_text`
-
-
```python
on_text(
text: str,
@@ -311,11 +251,8 @@ on_text(
Handle a text message.
-
### `on_tool_end`
-
-
```python
on_tool_end(
output: str,
@@ -325,11 +262,8 @@ on_tool_end(
End a trace for a tool run.
-
### `on_tool_error`
-
-
```python
on_tool_error(
error: Union[Exception, KeyboardInterrupt],
@@ -339,11 +273,8 @@ on_tool_error(
Handle an error for a tool run.
-
### `on_tool_start`
-
-
```python
on_tool_start(
serialized: Dict[str, Any],
@@ -352,8 +283,4 @@ on_tool_start(
) -> None
```
-Start a trace for a tool run.
-
-
-
-
+Start a trace for a tool run.
\ No newline at end of file
diff --git a/docs/ref/python/launch-library/launch_add.md b/docs/ref/python/launch-library/launch_add.md
index 10ea1e395..e40cd1164 100644
--- a/docs/ref/python/launch-library/launch_add.md
+++ b/docs/ref/python/launch-library/launch_add.md
@@ -35,7 +35,7 @@ launch_add(
| `uri` | URI of experiment to run. A wandb run uri or a Git repository URI. |
| `job` | string reference to a wandb.Job eg: wandb/test/my-job:latest |
| `config` | A dictionary containing the configuration for the run. May also contain resource specific arguments under the key "resource_args" |
-| `template_variables` | A dictionary containing values of template variables for a run queue. Expected format of {"VAR_NAME": VAR_VALUE} |
+| `template_variables` | A dictionary containing values of template variables for a run queue. Expected format of `{"VAR_NAME": VAR_VALUE}` |
| `project` | Target project to send launched run to |
| `entity` | Target entity to send launched run to |
| `queue` | the name of the queue to enqueue the run to |
diff --git a/docs/ref/python/public-api/api.md b/docs/ref/python/public-api/api.md
index cda135006..b8eb303c3 100644
--- a/docs/ref/python/public-api/api.md
+++ b/docs/ref/python/public-api/api.md
@@ -285,8 +285,8 @@ Create a new run queue (launch).
| `type` | (str) Type of resource to be used for the queue. One of "local-container", "local-process", "kubernetes", "sagemaker", or "gcp-vertex". |
| `entity` | (str) Optional name of the entity to create the queue. If None, will use the configured or default entity. |
| `prioritization_mode` | (str) Optional version of prioritization to use. Either "V0" or None |
-| `config` | (dict) Optional default resource configuration to be used for the queue. Use handlebars (eg. "{{var}}") to specify template variables. |
-| `template_variables` | (dict) A dictionary of template variable schemas to be used with the config. Expected format of: { "var-name": { "schema": { "type": ("string", "number", or "integer"), "default": (optional value), "minimum": (optional minimum), "maximum": (optional maximum), "enum": [..."(options)"] } } } |
+| `config` | (dict) Optional default resource configuration to be used for the queue. Use handlebars (eg. `{{var}}`) to specify template variables. |
+| `template_variables` | (dict) A dictionary of template variable schemas to be used with the config. Expected format of: `{ "var-name": { "schema": { "type": ("string", "number", or "integer"), "default": (optional value), "minimum": (optional minimum), "maximum": (optional maximum), "enum": [..."(options)"] }}}` |
| Returns | |
| :--- | :--- |
@@ -611,7 +611,7 @@ api.runs(path="my_entity/my_project", order="+summary_metrics.loss")
| Arguments | |
| :--- | :--- |
| `path` | (str) path to project, should be in the form: "entity/project" |
-| `filters` | (dict) queries for specific runs using the MongoDB query language. You can filter by run properties such as config.key, summary_metrics.key, state, entity, createdAt, etc. For example: {"config.experiment_name": "foo"} would find runs with a config entry of experiment name set to "foo" You can compose operations to make more complicated queries, see Reference for the language is at https://docs.mongodb.com/manual/reference/operator/query |
+| `filters` | (dict) queries for specific runs using the MongoDB query language. You can filter by run properties such as config.key, summary_metrics.key, state, entity, createdAt, etc. For example: \{"config.experiment_name": "foo"\} would find runs with a config entry of experiment name set to "foo" You can compose operations to make more complicated queries, see Reference for the language is at https://docs.mongodb.com/manual/reference/operator/query |
| `order` | (str) Order can be `created_at`, `heartbeat_at`, `config.*.value`, or `summary_metrics.*`. If you prepend order with a + order is ascending. If you prepend order with a - order is descending (default). The default order is run.created_at from oldest to newest. |
| `per_page` | (int) Sets the page size for query pagination. |
| `include_sweeps` | (bool) Whether to include the sweep runs in the results. |
diff --git a/docs/ref/python/run.md b/docs/ref/python/run.md
index e85dadb69..8eb410b7a 100644
--- a/docs/ref/python/run.md
+++ b/docs/ref/python/run.md
@@ -291,7 +291,7 @@ The linked artifact will be visible in the UI for the specified portfolio.
| Arguments | |
| :--- | :--- |
| `artifact` | the (public or local) artifact which will be linked |
-| `target_path` | `str` - takes the following forms: {portfolio}, {project}/{portfolio}, or {entity}/{project}/{portfolio} |
+| `target_path` | `str` - takes the following forms: `{portfolio}`, `{project}/{portfolio}`, or `{entity}/{project}/{portfolio}` |
| `aliases` | `List[str]` - optional alias(es) that will only be applied on this linked artifact inside the portfolio. The alias "latest" will always be applied to the latest version of an artifact that is linked. |
| Returns | |
diff --git a/docs/ref/python/sweep.md b/docs/ref/python/sweep.md
index 4b8cbdd51..4cf929020 100644
--- a/docs/ref/python/sweep.md
+++ b/docs/ref/python/sweep.md
@@ -22,7 +22,7 @@ At a later step provide the `sweep_id` to a sweep agent.
| Args | |
| :--- | :--- |
-| `sweep` | The configuration of a hyperparameter search. (or configuration generator). See [Sweep configuration structure](https://docs.wandb.ai/guides/sweeps/define-sweep-configuration) for information on how to define your sweep. If you provide a callable, ensure that the callable does not take arguments and that it returns a dictionary that conforms to the W&B sweep config spec. |
+| `sweep` | The configuration of a hyperparameter search. (or configuration generator). See [Sweep configuration structure](/guides/sweeps/define-sweep-configuration) for information on how to define your sweep. If you provide a callable, ensure that the callable does not take arguments and that it returns a dictionary that conforms to the W&B sweep config spec. |
| `entity` | The username or team name where you want to send W&B runs created by the sweep to. Ensure that the entity you specify already exists. If you don't specify an entity, the run will be sent to your default entity, which is usually your username. |
| `project` | The name of the project where W&B runs created from the sweep are sent to. If the project is not specified, the run is sent to a project labeled 'Uncategorized'. |
| `prior_runs` | The run IDs of existing runs to add to this sweep. |
diff --git a/docs/tutorials/artifacts.md b/docs/tutorials/artifacts.md
index 03282d20d..7362639d9 100644
--- a/docs/tutorials/artifacts.md
+++ b/docs/tutorials/artifacts.md
@@ -1,10 +1,12 @@
-# Track models and datasets
+---
+title: Track models and datasets
+---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-[**Try in a Colab Notebook here →**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/wandb-artifacts/Pipeline_Versioning_with_W&B_Artifacts.ipynb)
+
In this notebook, we'll show you how to track your ML experiment pipelines using W&B Artifacts.
-
### Follow along with a [video tutorial](http://tiny.cc/wb-artifacts-video)!
### 🤔 What are Artifacts and Why Should I Care?
@@ -21,7 +23,7 @@ After all, those who don't track training are doomed to repeat it.
Using our Artifacts API, you can log `Artifact`s as outputs of W&B `Run`s or use `Artifact`s as input to `Run`s, as in this diagram,
where a training run takes in a dataset and produces a model.
- ![](https://gblobscdn.gitbook.com/assets%2F-Lqya5RvLedGEWPhtkjU%2F-M94QAXA-oJmE6q07_iT%2F-M94QJCXLeePzH1p_fW1%2Fsimple%20artifact%20diagram%202.png?alt=media&token=94bc438a-bd3b-414d-a4e4-aa4f6f359f21)
+ ![](/images/tutorials/artifacts-diagram.png)
Since one run can use another's output as an input, Artifacts and Runs together form a directed graph -- actually, a bipartite [DAG](https://en.wikipedia.org/wiki/Directed_acyclic_graph)! -- with nodes for `Artifact`s and `Run`s
and arrows connecting `Run`s to the `Artifact`s they consume or produce.
diff --git a/docs/tutorials/experiments.md b/docs/tutorials/experiments.md
index a35c4a9aa..0f4be7f8c 100644
--- a/docs/tutorials/experiments.md
+++ b/docs/tutorials/experiments.md
@@ -1,14 +1,11 @@
+---
+title: Track experiments
+---
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-# Track experiments
-
-
-
-
-
-Use [W&B](https://wandb.ai/site?utm_source=intro_colab&utm_medium=code&utm_campaign=intro) for machine learning experiment tracking, model checkpointing, collaboration with your team and more. See the full W&B Documentation [here](https://docs.wandb.ai/).
+Use [W&B](https://wandb.ai/site?utm_source=intro_colab&utm_medium=code&utm_campaign=intro) for machine learning experiment tracking, model checkpointing, collaboration with your team and more. See the full W&B Documentation [here](/).
In this notebook, you will create and track a machine learning experiment using a simple PyTorch model. By the end of the notebook, you will have an interactive project dashboard that you can share and customize with other members of your team. [View an example dashboard here](https://wandb.ai/wandb/wandb_example).
@@ -17,12 +14,12 @@ In this notebook, you will create and track a machine learning experiment using
Install the W&B Python SDK and log in:
-```
+```shell
!pip install wandb -qU
```
-```
+```python
# Log in to your W&B account
import wandb
import random
@@ -33,7 +30,7 @@ wandb.require("core")
```
-```
+```python
wandb.login()
```
@@ -41,7 +38,7 @@ wandb.login()
Create, track, and visualize a machine learning experiment. To do this:
-1. Initialize a [W&B run](https://docs.wandb.ai/guides/runs) and pass in the hyperparameters you want to track.
+1. Initialize a [W&B run](/guides/runs) and pass in the hyperparameters you want to track.
2. Within your training loop, log metrics such as the accuruacy and loss.
@@ -84,7 +81,7 @@ View how your machine learning peformed in your W&B project. Copy and paste the
The following image shows what a dashboard can look like:
-![](https://i.imgur.com/Pell4Oo.png)
+![](/images/tutorials/experiments-1.png)
Now that we know how to integrate W&B into a psuedo machine learning training loop, let's track a machine learning experiment using a basic PyTorch neural network. The following code will also upload model checkpoints to W&B that you can then share with other teams in in your organization.
@@ -92,18 +89,18 @@ Now that we know how to integrate W&B into a psuedo machine learning training lo
The following code cell defines and trains a simple MNIST classifier. During training, you will see W&B prints out URLs. Click on the project page link to see your results stream in live to a W&B project.
-W&B runs automatically log [metrics](https://docs.wandb.ai/ref/app/pages/run-page#charts-tab),
-[system information](https://docs.wandb.ai/ref/app/pages/run-page#system-tab),
-[hyperparameters](https://docs.wandb.ai/ref/app/pages/run-page#overview-tab),
-[terminal output](https://docs.wandb.ai/ref/app/pages/run-page#logs-tab) and
-you'll see an [interactive table](https://docs.wandb.ai/guides/data-vis)
+W&B runs automatically log [metrics](/guides/app/pages/run-page/#workspace-tab),
+[system information](/guides/app/pages/run-page/#system-tab),
+[hyperparameters](/guides/app/pages/run-page/#overview-tab),
+[terminal output](/guides/app/pages/run-page/#logs-tab) and
+you'll see an [interactive table](/guides/tables)
with model inputs and outputs.
### Set up PyTorch Dataloader
The following cell defines some useful functions that we will need to train our machine learning model. The functions themselves are not unique to W&B so we'll not cover them in detail here. See the PyTorch documentation for more information on how to define [forward and backward training loop](https://pytorch.org/tutorials/beginner/nn_tutorial.html), how to use [PyTorch DataLoaders](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html) to load data in for training, and how define PyTorch models using the [`torch.nn.Sequential` Class](https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html).
-```
+```python
#@title
import torch, torchvision
import torch.nn as nn
@@ -166,7 +163,7 @@ In the cell we define a function called `log_image_table`. Though technically, o
More specifically, each row will conists of the image fed to the model, along with predicted value and the actual value (label).
-```
+```python
def log_image_table(images, predicted, labels, probs):
"Log a wandb.Table with (img, pred, target, scores)"
# Create a wandb Table to log images, labels and predictions to
@@ -180,10 +177,10 @@ def log_image_table(images, predicted, labels, probs):
The following code trains and saves model checkpoints to your project. Use model checkpoints like you normally would to assess how the model performed during training.
-W&B also makes it easy to share your saved models and model checkpoints with other members of your team or organization. To learn how to share your model and model checkpoints with members outside of your team, see [W&B Registry](https://docs.wandb.ai/guides/registry).
+W&B also makes it easy to share your saved models and model checkpoints with other members of your team or organization. To learn how to share your model and model checkpoints with members outside of your team, see [W&B Registry](/guides/registry).
-```
+```python
# Launch 3 experiments, trying different dropout rates
for _ in range(3):
# initialise a wandb run
@@ -260,7 +257,7 @@ You have now trained your first model using W&B. Click on one of the links above
## (Optional) Set up a W&B Alert
-Create a [W&B Alerts](https://docs.wandb.ai/guides/track/alert) to send alerts to your Slack or email from your Python code.
+Create a [W&B Alerts](/guides/runs/alert/) to send alerts to your Slack or email from your Python code.
There are 2 steps to follow the first time you'd like to send a Slack or email alert, triggered from your code:
@@ -277,7 +274,7 @@ wandb.alert(
The following cell shows a minimal example below to see how to use `wandb.alert`
-```
+```python
# Start a wandb run
wandb.init(project="pytorch-intro")
@@ -306,7 +303,7 @@ for training_step in range(1000):
wandb.finish()
```
-You can find the full docs for [W&B Alerts here](https://docs.wandb.ai/guides/track/alert).
+You can find the full docs for [W&B Alerts here](/guides/runs/alert).
## Next steps
The next tutorial you will learn how to do hyperparameter optimization using W&B Sweeps:
diff --git a/docs/tutorials/huggingface.md b/docs/tutorials/huggingface.md
index 21a3c09b2..ad32de041 100644
--- a/docs/tutorials/huggingface.md
+++ b/docs/tutorials/huggingface.md
@@ -1,18 +1,19 @@
-# Hugging Face
+---
+title: Hugging Face
+---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-
+![](/images/tutorials/huggingface.png)
-[**Try in a Colab Notebook here →**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/huggingface/Huggingface_wandb.ipynb)
+
Visualize your [Hugging Face](https://github.com/huggingface/transformers) model's performance quickly with a seamless [W&B](https://wandb.ai/site) integration.
Compare hyperparameters, output metrics, and system stats like GPU utilization across your models.
+## Why should I use W&B?
-
-## 🤔 Why should I use W&B?
-
-
+![](/images/tutorials/huggingface-why.png)
- **Unified dashboard**: Central repository for all your model metrics and predictions
- **Lightweight**: No code changes required to integrate with Hugging Face
@@ -34,7 +35,7 @@ Here's an in depth look at how the integration works: [Hugging Face + W&B Report
Install the Hugging Face and Weights & Biases libraries, and the GLUE dataset and training script for this tutorial.
- [Hugging Face Transformers](https://github.com/huggingface/transformers): Natural language models and datasets
-- [Weights & Biases](https://docs.wandb.com/): Experiment tracking and visualization
+- [Weights & Biases](/): Experiment tracking and visualization
- [GLUE dataset](https://gluebenchmark.com/): A language understanding benchmark dataset
- [GLUE script](https://github.com/huggingface/transformers/blob/master/examples/run_glue.py): Model training script for sequence classification
@@ -61,7 +62,7 @@ import wandb
wandb.login()
```
-Optionally, we can set environment variables to customize W&B logging. See [documentation](https://docs.wandb.com/library/integrations/huggingface).
+Optionally, we can set environment variables to customize W&B logging. See [documentation](/guides/integrations/huggingface).
```python
@@ -97,13 +98,12 @@ Click the link printed out above, or go to [wandb.ai](https://app.wandb.ai) to s
**Visualize Model Performance**
It's easy to look across dozens of experiments, zoom in on interesting findings, and visualize highly dimensional data.
-![](https://gblobscdn.gitbook.com/assets%2F-Lqya5RvLedGEWPhtkjU%2F-M79Y5aLAFsMEcybMZcC%2F-M79YL90K1jiq-3jeQK-%2Fhf%20gif%2015.gif?alt=media&token=523d73f4-3f6c-499c-b7e8-ef5be0c10c2a)
+![](/images/tutorials/huggingface-visualize.gif)
**Compare Architectures**
Here's an example comparing [BERT vs DistilBERT](https://app.wandb.ai/jack-morris/david-vs-goliath/reports/Does-model-size-matter%3F-Comparing-BERT-and-DistilBERT-using-Sweeps--VmlldzoxMDUxNzU) — it's easy to see how different architectures effect the evaluation accuracy throughout training with automatic line plot visualizations.
-![](https://gblobscdn.gitbook.com/assets%2F-Lqya5RvLedGEWPhtkjU%2F-M79Y5aLAFsMEcybMZcC%2F-M79Ytpj6q6Jlv9RKZGT%2Fgif%20for%20comparing%20bert.gif?alt=media&token=e3dee5de-d120-4330-b4bd-2e2ddbb8315e)
-
+![](/images/tutorials/huggingface-comparearchitectures.gif)
### 📈 Track key information effortlessly by default
Weights & Biases saves a new run for each experiment. Here's the information that gets saved by default:
@@ -112,8 +112,7 @@ Weights & Biases saves a new run for each experiment. Here's the information tha
- **Terminal Logs**: Command line outputs are saved and available in a tab
- **System Metrics**: GPU and CPU utilization, memory, temperature etc.
-
## 🤓 Learn more!
-- [Documentation](https://docs.wandb.com/huggingface): docs on the Weights & Biases and Hugging Face integration
+- [Documentation](/guides/integrations/huggingface): docs on the Weights & Biases and Hugging Face integration
- [Videos](http://wandb.me/youtube): tutorials, interviews with practitioners, and more on our YouTube channel
- Contact: Message us at contact@wandb.com with questions
diff --git a/docs/tutorials/intro_to_tutorials.md b/docs/tutorials/intro_to_tutorials.md
index b9133fc3d..44109226f 100644
--- a/docs/tutorials/intro_to_tutorials.md
+++ b/docs/tutorials/intro_to_tutorials.md
@@ -1,10 +1,9 @@
---
description: Get started using Weights & Biases with interactive tutorials.
slug: /tutorials
+title: W&B Tutorials
---
-# W&B Tutorials
-
The following tutorials take you through the fundamentals of W&B for machine learning experiment tracking, model evaluation, hyperparameter tuning, model and dataset versioning, and more.
1. [Track experiments](/tutorials/experiments)
diff --git a/docs/tutorials/keras.md b/docs/tutorials/keras.md
index 788cbc46a..454c79be3 100644
--- a/docs/tutorials/keras.md
+++ b/docs/tutorials/keras.md
@@ -1,22 +1,24 @@
-# Keras
+---
+title: Keras
+---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-[**Try in a Colab Notebook here →**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/keras/Use_WandbMetricLogger_in_your_Keras_workflow.ipynb)
+
Use Weights & Biases for machine learning experiment tracking, dataset versioning, and project collaboration.
-
+![](/images/tutorials/huggingface-why.png)
+This Colab notebook introduces the `WandbMetricsLogger` callback. Use this callback for [Experiment Tracking](/guides/track). It will log your training and validation metrics along with system metrics to Weights and Biases.
-This colab notebook introduces the `WandbMetricsLogger` callback. Use this callback for [Experiment Tracking](https://docs.wandb.ai/guides/track). It will log your training and validation metrics along with system metrics to Weights and Biases.
-
-# 🌴 Setup and Installation
+## 🌴 Setup and Installation
First, let us install the latest version of Weights and Biases. We will then authenticate this colab instance to use W&B.
-```python
-!pip install -qq -U wandb
+```shell
+pip install -qq -U wandb
```
@@ -39,7 +41,7 @@ If this is your first time using W&B or you are not logged in, the link that app
wandb.login()
```
-# 🌳 Hyperparameters
+## 🌳 Hyperparameters
Use of proper config system is a recommended best practice for reproducible machine learning. We can track the hyperparameters for every experiment using W&B. In this colab we will be using simple Python `dict` as our config system.
@@ -57,7 +59,7 @@ configs = dict(
)
```
-# 🍁 Dataset
+## 🍁 Dataset
In this colab, we will be using [CIFAR100](https://www.tensorflow.org/datasets/catalog/cifar100) dataset from TensorFlow Dataset catalog. We aim to build a simple image classification pipeline using TensorFlow/Keras.
@@ -130,7 +132,7 @@ model = get_model(configs)
model.summary()
```
-# 🌿 Compile Model
+## 🌿 Compile Model
```python
@@ -141,7 +143,7 @@ model.compile(
)
```
-# 🌻 Train
+## 🌻 Train
```python
diff --git a/docs/tutorials/keras_models.md b/docs/tutorials/keras_models.md
index d6679f0a5..7ebe2bc5e 100644
--- a/docs/tutorials/keras_models.md
+++ b/docs/tutorials/keras_models.md
@@ -1,14 +1,17 @@
-# Keras Models
+---
+title: Keras models
+---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-[**Try in a Colab Notebook here →**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/keras/Use_WandbModelCheckpoint_in_your_Keras_workflow.ipynb)
+
Use Weights & Biases for machine learning experiment tracking, dataset versioning, and project collaboration.
-
+![](/images/tutorials/huggingface-why.png)
-This colab notebook introduces the `WandbModelCheckpoint` callback. Use this callback to log your model checkpoints to Weight and Biases [Artifacts](https://docs.wandb.ai/guides/data-and-model-versioning).
+This Colab notebook introduces the `WandbModelCheckpoint` callback. Use this callback to log your model checkpoints to Weight and Biases [Artifacts](/guides/artifacts).
-# 🌴 Setup and Installation
+## 🌴 Setup and Installation
First, let us install the latest version of Weights and Biases. We will then authenticate this colab instance to use W&B.
@@ -38,7 +41,7 @@ If this is your first time using W&B or you are not logged in, the link that app
wandb.login()
```
-# 🌳 Hyperparameters
+## 🌳 Hyperparameters
Use of proper config system is a recommended best practice for reproducible machine learning. We can track the hyperparameters for every experiment using W&B. In this colab we will be using simple Python `dict` as our config system.
@@ -56,7 +59,7 @@ configs = dict(
)
```
-# 🍁 Dataset
+## 🍁 Dataset
In this colab, we will be using [CIFAR100](https://www.tensorflow.org/datasets/catalog/cifar100) dataset from TensorFlow Dataset catalog. We aim to build a simple image classification pipeline using TensorFlow/Keras.
@@ -103,7 +106,7 @@ trainloader = get_dataloader(train_ds, configs)
validloader = get_dataloader(valid_ds, configs, dataloader_type="valid")
```
-# 🎄 Model
+## 🎄 Model
```python
@@ -129,7 +132,7 @@ model = get_model(configs)
model.summary()
```
-# 🌿 Compile Model
+## 🌿 Compile Model
```python
@@ -140,7 +143,7 @@ model.compile(
)
```
-# 🌻 Train
+## 🌻 Train
```python
diff --git a/docs/tutorials/keras_tables.md b/docs/tutorials/keras_tables.md
index 3da69093e..98c436ee6 100644
--- a/docs/tutorials/keras_tables.md
+++ b/docs/tutorials/keras_tables.md
@@ -1,14 +1,17 @@
-# Keras Tables
+---
+title: Keras tables
+---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-[**Try in a Colab Notebook here →**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/keras/Use_WandbEvalCallback_in_your_Keras_workflow.ipynb)
+
Use Weights & Biases for machine learning experiment tracking, dataset versioning, and project collaboration.
-
+![](/images/tutorials/huggingface-why.png)
-This colab notebook introduces the `WandbEvalCallback` which is an abstract callback that be inherited to build useful callbacks for model prediction visualization and dataset visualization. Refer to the [💫 `WandbEvalCallback`](https://colab.research.google.com/drive/107uB39vBulCflqmOWolu38noWLxAT6Be#scrollTo=u50GwKJ70WeJ&line=1&uniqifier=1) section for more details.
+This Colab notebook introduces the `WandbEvalCallback` which is an abstract callback that be inherited to build useful callbacks for model prediction visualization and dataset visualization.
-# 🌴 Setup and Installation
+## 🌴 Setup and Installation
First, let us install the latest version of Weights and Biases. We will then authenticate this colab instance to use W&B.
@@ -40,7 +43,7 @@ If this is your first time using W&B or you are not logged in, the link that app
wandb.login()
```
-# 🌳 Hyperparameters
+## 🌳 Hyperparameters
Use of proper config system is a recommended best practice for reproducible machine learning. We can track the hyperparameters for every experiment using W&B. In this colab we will be using simple Python `dict` as our config system.
@@ -58,7 +61,7 @@ configs = dict(
)
```
-# 🍁 Dataset
+## 🍁 Dataset
In this colab, we will be using [CIFAR100](https://www.tensorflow.org/datasets/catalog/cifar100) dataset from TensorFlow Dataset catalog. We aim to build a simple image classification pipeline using TensorFlow/Keras.
@@ -105,7 +108,7 @@ trainloader = get_dataloader(train_ds, configs)
validloader = get_dataloader(valid_ds, configs, dataloader_type="valid")
```
-# 🎄 Model
+## 🎄 Model
```python
@@ -135,7 +138,7 @@ model = get_model(configs)
model.summary()
```
-# 🌿 Compile Model
+## 🌿 Compile Model
```python
@@ -149,7 +152,7 @@ model.compile(
)
```
-# 💫 `WandbEvalCallback`
+## 💫 `WandbEvalCallback`
The `WandbEvalCallback` is an abstract base class to build Keras callbacks for primarily model prediction visualization and secondarily dataset visualization.
@@ -167,7 +170,7 @@ As an example, we have implemented `WandbClfEvalCallback` below for an image cla
- performs inference and logs the prediction (`pred_table`) to W&B on every epoch end.
-## How the memory footprint is reduced?
+## ✨ How the memory footprint is reduced?
We log the `data_table` to W&B when the `on_train_begin` method is ivoked. Once it's uploaded as a W&B Artifact, we get a reference to this table which can be accessed using `data_table_ref` class variable. The `data_table_ref` is a 2D list that can be indexed like `self.data_table_ref[idx][n]` where `idx` is the row number while `n` is the column number. Let's see the usage in the example below.
@@ -209,7 +212,7 @@ class WandbClfEvalCallback(WandbEvalCallback):
return preds
```
-# 🌻 Train
+## 🌻 Train
```python
diff --git a/docs/tutorials/lightgbm.md b/docs/tutorials/lightgbm.md
deleted file mode 100644
index 74b615f4d..000000000
--- a/docs/tutorials/lightgbm.md
+++ /dev/null
@@ -1,198 +0,0 @@
-# LightGBM
-
-[**Try in a Colab Notebook here →**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/boosting/Simple_LightGBM_Integration.ipynb)
-
-Use Weights & Biases for machine learning experiment tracking, dataset versioning, and project collaboration.
-
-Gradient boosting decision trees are the state of the art when it comes to building predictive models for structured data.
-
-[LigthGBM](https://github.com/microsoft/LightGBM), a gradient boosting framework by Microsoft, has dethroned xgboost and become the go to GBDT algorithm (along with catboost). It outperforms xgboost in training speeds, memory usage and the size of datasets it can handle. LightGBM does so by using histogram-based algorithms to bucket continuous features into discrete bins during training.
-
-You can find the **[W&B + LightGBM documentation here](https://docs.wandb.ai/guides/integrations/boosting)**
-
-
-## What this notebook covers
-* Easy integration of Weights and Biases with LightGBM.
-* `wandb_callback()` callback for metrics logging
-* `log_summary()` function to log a feature importance plot and enable model saving to W&B
-
-We want to make it incredible easy for people to look under the hood of their models, so we built a callback that helps you visualize your LightGBM’s performance in just one line of code.
-
-**Note**: Sections starting with _Step_ is all you need to integrate W&B.
-
-# Install, Import, and Log in
-
-## The Usual Suspects
-
-
-```ipython
-!pip install -Uq 'lightgbm>=3.3.1'
-```
-
-
-```python
-import pandas as pd
-import lightgbm as lgb
-from sklearn.metrics import mean_squared_error
-```
-
-## Step 0: Install W&B
-
-
-```ipython
-!pip install -qU wandb
-```
-
-## Step 1: Import W&B and Login
-
-
-```python
-import wandb
-from wandb.integration.lightgbm import wandb_callback, log_summary
-
-wandb.login()
-```
-
-# Download and Prepare Dataset
-
-
-
-```ipython
-!wget https://raw.githubusercontent.com/microsoft/LightGBM/master/examples/regression/regression.train -qq
-!wget https://raw.githubusercontent.com/microsoft/LightGBM/master/examples/regression/regression.test -qq
-```
-
-
-```python
-# load or create your dataset
-df_train = pd.read_csv("regression.train", header=None, sep="\t")
-df_test = pd.read_csv("regression.test", header=None, sep="\t")
-
-y_train = df_train[0]
-y_test = df_test[0]
-X_train = df_train.drop(0, axis=1)
-X_test = df_test.drop(0, axis=1)
-
-# create dataset for lightgbm
-lgb_train = lgb.Dataset(X_train, y_train)
-lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
-```
-
-# Train
-
-### Step 2: Initialize your wandb run.
-
-Using `wandb.init()` initialize your W&B run. You can also pass a dictionary of configs. [Check out the official documentation here $\rightarrow$](https://docs.wandb.com/library/init)
-
-You can't deny the importance of configs in your ML/DL workflow. W&B makes sure that you have access to the right config to reproduce your model.
-
-[Learn more about configs in this colab notebook $\rightarrow$](http://wandb.me/config-colab)
-
-
-```python
-# specify your configurations as a dict
-params = {
- "boosting_type": "gbdt",
- "objective": "regression",
- "metric": ["rmse", "l2", "l1", "huber"],
- "num_leaves": 31,
- "learning_rate": 0.05,
- "feature_fraction": 0.9,
- "bagging_fraction": 0.8,
- "bagging_freq": 5,
- "verbosity": 0,
-}
-
-wandb.init(project="my-lightgbm-project", config=params)
-```
-
-> Once you have trained your model come back and click on the **Project page**.
-
-### Step 3: Train with `wandb_callback`
-
-
-```python
-# train
-# add lightgbm callback
-gbm = lgb.train(
- params,
- lgb_train,
- num_boost_round=30,
- valid_sets=lgb_eval,
- valid_names=("validation"),
- callbacks=[wandb_callback()],
- early_stopping_rounds=5,
-)
-```
-
-### Step 4: Log Feature Importance and Upload Model with `log_summary`
-`log_summary` will upload calculate and upload the feature importance import and (optionally) upload your trained model to W&B Artifacts so you can use it later
-
-
-```python
-log_summary(gbm, save_model_checkpoint=True)
-```
-
-# Evaluate
-
-
-```python
-# predict
-y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)
-
-# eval
-print("The rmse of prediction is:", mean_squared_error(y_test, y_pred) ** 0.5)
-wandb.log({"rmse_prediction": mean_squared_error(y_test, y_pred) ** 0.5})
-```
-
-When you are finished logging for a particular W&B run its a good idea to call `wandb.finish()` to tidy up the wandb process (only necessary when using notebooks/colabs)
-
-
-```python
-wandb.finish()
-```
-
-# Visualize Results
-
-Click on the **project page** link above to see your results automatically visualized.
-
-
-
-
-# Sweep 101
-
-Use Weights & Biases Sweeps to automate hyperparameter optimization and explore the space of possible models.
-
-## [Check out Hyperparameter Optimization with XGBoost using W&B Sweep $\rightarrow$](http://wandb.me/xgb-colab)
-
-Running a hyperparameter sweep with Weights & Biases is very easy. There are just 3 simple steps:
-
-1. **Define the sweep:** We do this by creating a dictionary or a [YAML file](https://docs.wandb.com/library/sweeps/configuration) that specifies the parameters to search through, the search strategy, the optimization metric et all.
-
-2. **Initialize the sweep:**
-`sweep_id = wandb.sweep(sweep_config)`
-
-3. **Run the sweep agent:**
-`wandb.agent(sweep_id, function=train)`
-
-And voila! That's all there is to running a hyperparameter sweep!
-
-
-
-
-# Example Gallery
-
-See examples of projects tracked and visualized with W&B in our [Gallery →](https://app.wandb.ai/gallery)
-
-# Basic Setup
-1. **Projects**: Log multiple runs to a project to compare them. `wandb.init(project="project-name")`
-2. **Groups**: For multiple processes or cross validation folds, log each process as a runs and group them together. `wandb.init(group='experiment-1')`
-3. **Tags**: Add tags to track your current baseline or production model.
-4. **Notes**: Type notes in the table to track the changes between runs.
-5. **Reports**: Take quick notes on progress to share with colleagues and make dashboards and snapshots of your ML projects.
-
-# Advanced Setup
-1. [Environment variables](https://docs.wandb.com/library/environment-variables): Set API keys in environment variables so you can run training on a managed cluster.
-2. [Offline mode](https://docs.wandb.com/library/technical-faq#can-i-run-wandb-offline): Use `dryrun` mode to train offline and sync results later.
-3. [On-prem](https://docs.wandb.com/self-hosted): Install W&B in a private cloud or air-gapped servers in your own infrastructure. We have local installations for everyone from academics to enterprise teams.
-4. [Sweeps](https://docs.wandb.com/sweeps): Set up hyperparameter search quickly with our lightweight tool for tuning.
diff --git a/docs/tutorials/lightning.md b/docs/tutorials/lightning.md
index a7dbc43bf..7eeae71aa 100644
--- a/docs/tutorials/lightning.md
+++ b/docs/tutorials/lightning.md
@@ -1,25 +1,22 @@
-# PyTorch Lightning
+---
+title: PyTorch Lightning
+---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-[**Try in a Colab Notebook here →**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/pytorch-lightning/Image_Classification_using_PyTorch_Lightning.ipynb)
+
We will build an image classification pipeline using PyTorch Lightning. We will follow this [style guide](https://lightning.ai/docs/pytorch/stable/starter/style_guide.html) to increase the readability and reproducibility of our code. A cool explanation of this available [here](https://wandb.ai/wandb/wandb-lightning/reports/Image-Classification-using-PyTorch-Lightning--VmlldzoyODk1NzY).
## Setting up PyTorch Lightning and W&B
-For this tutorial, we need PyTorch Lightning(ain't that obvious!) and Weights and Biases.
+For this tutorial, we need PyTorch Lightning and Weights and Biases.
-
-```
-!pip install lightning -q
-# install weights and biases
-!pip install wandb -qU
+```shell
+pip install lightning -q
+pip install wandb -qU
```
-You're gonna need these imports.
-
-
-
-```
+```python
import lightning.pytorch as pl
# your favorite machine learning tracking tool
from lightning.pytorch.loggers import WandbLogger
@@ -37,8 +34,7 @@ from torchvision.datasets import CIFAR10
import wandb
```
-Now you'll need to log in to you wandb account.
-
+Now you'll need to log in to your wandb account.
```
wandb.login()
diff --git a/docs/tutorials/minikube_gpu.md b/docs/tutorials/minikube_gpu.md
index de9cdffbe..2535a35fb 100644
--- a/docs/tutorials/minikube_gpu.md
+++ b/docs/tutorials/minikube_gpu.md
@@ -1,8 +1,9 @@
+---
+title: Spin up a single node GPU cluster with Minikube
+---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Spin up a single node GPU cluster with Minikube
-
Set up W&B Launch on a Minikube cluster that can schedule and run GPU workloads.
:::info
diff --git a/docs/tutorials/monai_3d_segmentation.md b/docs/tutorials/monai_3d_segmentation.md
index 0fc681b60..44a3ead2a 100644
--- a/docs/tutorials/monai_3d_segmentation.md
+++ b/docs/tutorials/monai_3d_segmentation.md
@@ -1,9 +1,10 @@
+---
+title: 3D brain tumor segmentation with MONAI
+---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-# 3D brain tumor segmentation with MONAI
-
This tutorial demonstrates how to construct a training workflow of multi-labels 3D brain tumor segmentation task using [MONAI](https://github.com/Project-MONAI/MONAI) and use experiment tracking and data visualization features of [Weights & Biases](https://wandb.ai/site). The tutorial contains the following features:
@@ -229,7 +230,7 @@ val_dataset = DecathlonDataset(
### 📸 Visualizing the Dataset
-Weights & Biases supports images, video, audio, and more. You can log rich media to explore your results and visually compare our runs, models, and datasets. Use the [segmentation mask overlay system](https://docs.wandb.ai/guides/track/log/media#image-overlays-in-tables) to visualize our data volumes. To log segmentation masks in [tables](https://docs.wandb.ai/guides/tables), you must provide a `wandb.Image` object for each row in the table.
+Weights & Biases supports images, video, audio, and more. You can log rich media to explore your results and visually compare our runs, models, and datasets. Use the [segmentation mask overlay system](/guides/track/log/media#image-overlays-in-tables) to visualize our data volumes. To log segmentation masks in [tables](/guides/tables), you must provide a `wandb.Image` object for each row in the table.
An example is provided in the pseudocode below:
@@ -357,15 +358,15 @@ for data_idx, sample in progress_bar:
wandb.log({"Tumor-Segmentation-Data": table})
```
-The data appears on the W&B dashboard in an interactive tabular format. We can see each channel of a particular slice from a data volume overlaid with the respective segmentation mask in each row. You can write [Weave queries](https://docs.wandb.ai/guides/weave) to filter the data on the table and focus on one particular row.
+The data appears on the W&B dashboard in an interactive tabular format. We can see each channel of a particular slice from a data volume overlaid with the respective segmentation mask in each row. You can write [Weave queries](/guides/weave) to filter the data on the table and focus on one particular row.
-| ![An example of logged table data.](@site/static/images/tutorials/monai/viz-1.gif) |
+| ![An example of logged table data.](/images/tutorials/monai/viz-1.gif) |
|:--:|
| **An example of logged table data.** |
Open an image and see how you can interact with each of the segmentation masks using the interactive overlay.
-| ![An example of visualized segmentation maps.](@site/static/images/tutorials/monai/viz-2.gif) |
+| ![An example of visualized segmentation maps.](/images/tutorials/monai/viz-2.gif) |
|:--:|
| **An example of visualized segmentation maps.* |
@@ -581,13 +582,13 @@ artifact.wait()
Instrumenting the code with `wandb.log` not only enables tracking all metrics associated with the training and validation process, but also the all system metrics (our CPU and GPU in this case) on the W&B dashboard.
-| ![An example of training and validation process tracking on W&B.](@site/static/images/tutorials/monai/viz-3.gif) |
+| ![An example of training and validation process tracking on W&B.](/images/tutorials/monai/viz-3.gif) |
|:--:|
| **An example of training and validation process tracking on W&B.** |
Navigate to the artifacts tab in the W&B run dashboard to access the different versions of model checkpoint artifacts logged during training.
-| ![An example of model checkpoints logging and versioning on W&B.](@site/static/images/tutorials/monai/viz-4.gif) |
+| ![An example of model checkpoints logging and versioning on W&B.](/images/tutorials/monai/viz-4.gif) |
|:--:|
| **An example of model checkpoints logging and versioning on W&B.** |
@@ -595,7 +596,7 @@ Navigate to the artifacts tab in the W&B run dashboard to access the different v
Using the artifacts interface, you can select which version of the artifact is the best model checkpoint, in this case, the mean epoch-wise training loss. You can also explore the entire lineage of the artifact and use the version that you need.
-| ![An example of model artifact tracking on W&B.](@site/static/images/tutorials/monai/viz-5.gif) |
+| ![An example of model artifact tracking on W&B.](/images/tutorials/monai/viz-5.gif) |
|:--:|
| **An example of model artifact tracking on W&B.** |
@@ -734,7 +735,7 @@ wandb.finish()
Use the interactive segmentation mask overlay to analyze and compare the predicted segmentation masks and the ground-truth labels for each class.
-| ![An example of predictions and ground-truth visualization on W&B.](@site/static/images/tutorials/monai/viz-6.gif) |
+| ![An example of predictions and ground-truth visualization on W&B.](/images/tutorials/monai/viz-6.gif) |
|:--:|
| **An example of predictions and ground-truth visualization on W&B.** |
diff --git a/docs/tutorials/prompts.md b/docs/tutorials/prompts.md
index 8dd0668ac..518a5251c 100644
--- a/docs/tutorials/prompts.md
+++ b/docs/tutorials/prompts.md
@@ -1,6 +1,10 @@
-# Iterate on LLMs
+---
+displayed_sidebar: tutorials
+title: Iterate on LLMs
+---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-[**Try in a Colab Notebook here →**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/prompts/WandB_Prompts_Quickstart.ipynb)
+
**Weights & Biases Prompts** is a suite of LLMOps tools built for the development of LLM-powered applications.
diff --git a/docs/tutorials/pytorch.md b/docs/tutorials/pytorch.md
index 4015e3f3c..3d5b53607 100644
--- a/docs/tutorials/pytorch.md
+++ b/docs/tutorials/pytorch.md
@@ -1,23 +1,24 @@
-# PyTorch
-Use [Weights & Biases](https://wandb.com) for machine learning experiment tracking, dataset versioning, and project collaboration.
-
-[**Try in a Colab Notebook here →**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/pytorch/Simple_PyTorch_Integration.ipynb)
+---
+title: PyTorch
+---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-
+
-
-
-
+Use [Weights & Biases](https://wandb.com) for machine learning experiment tracking, dataset versioning, and project collaboration.
+![](/images/tutorials/huggingface-why.png)
## What this notebook covers:
We show you how to integrate Weights & Biases with your PyTorch code to add experiment tracking to your pipeline.
## The resulting interactive W&B dashboard will look like:
-![](https://i.imgur.com/z8TK2Et.png)
+
+![](/images/tutorials/pytorch.png)
## In pseudocode, what we'll do is:
+
```python
# import the library
import wandb
@@ -432,11 +433,11 @@ a number of hyperparameters.
You can use Weights & Biases Sweeps to automate hyperparameter testing and explore the space of possible models and optimization strategies.
-## [Check out Hyperparameter Optimization in PyTorch using W&B Sweeps $\rightarrow$](http://wandb.me/sweeps-colab)
+## [Check out Hyperparameter Optimization in PyTorch using W&B Sweeps](http://wandb.me/sweeps-colab)
Running a hyperparameter sweep with Weights & Biases is very easy. There are just 3 simple steps:
-1. **Define the sweep:** We do this by creating a dictionary or a [YAML file](https://docs.wandb.com/library/sweeps/configuration) that specifies the parameters to search through, the search strategy, the optimization metric et all.
+1. **Define the sweep:** We do this by creating a dictionary or a [YAML file](/guides/sweeps/define-sweep-configuration) that specifies the parameters to search through, the search strategy, the optimization metric et all.
2. **Initialize the sweep:**
`sweep_id = wandb.sweep(sweep_config)`
@@ -445,7 +446,8 @@ Running a hyperparameter sweep with Weights & Biases is very easy. There are jus
`wandb.agent(sweep_id, function=train)`
And voila! That's all there is to running a hyperparameter sweep!
-
+
+![](/images/tutorials/pytorch-2.png)
# 🖼️ Example Gallery
@@ -453,7 +455,7 @@ And voila! That's all there is to running a hyperparameter sweep!
See examples of projects tracked and visualized with W&B in our [Gallery →](https://app.wandb.ai/gallery)
# 🤓 Advanced Setup
-1. [Environment variables](https://docs.wandb.com/library/environment-variables): Set API keys in environment variables so you can run training on a managed cluster.
-2. [Offline mode](https://docs.wandb.com/library/technical-faq#can-i-run-wandb-offline): Use `dryrun` mode to train offline and sync results later.
-3. [On-prem](https://docs.wandb.com/self-hosted): Install W&B in a private cloud or air-gapped servers in your own infrastructure. We have local installations for everyone from academics to enterprise teams.
-4. [Sweeps](https://docs.wandb.com/sweeps): Set up hyperparameter search quickly with our lightweight tool for tuning.
+1. [Environment variables](/guides/hosting/env-vars): Set API keys in environment variables so you can run training on a managed cluster.
+2. [Offline mode](/guides/technical-faq/setup/#can-i-run-wandb-offline): Use `dryrun` mode to train offline and sync results later.
+3. [On-prem](/guides/hosting/hosting-options/self-managed): Install W&B in a private cloud or air-gapped servers in your own infrastructure. We have local installations for everyone from academics to enterprise teams.
+4. [Sweeps](/guides/sweeps): Set up hyperparameter search quickly with our lightweight tool for tuning.
diff --git a/docs/tutorials/sweeps.md b/docs/tutorials/sweeps.md
index 605145d92..35ea8b028 100644
--- a/docs/tutorials/sweeps.md
+++ b/docs/tutorials/sweeps.md
@@ -1,33 +1,23 @@
+---
+title: Tune hyperparameters with sweeps
+---
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-# Tune hyperparameters
-
-
-
-
-
-
-
-
-
-
-
-
Finding a machine learning model that meets your desired metric (such as model accuracy) is normally a redundant task that can take multiple iterations. To make matters worse, it might be unclear which hyperparameter combinations to use for a given training run.
Use W&B Sweeps to create an organized and efficient way to automatically search through combinations of hyperparameter values such as the learning rate, batch size, number of hidden layers, optimizer type and more to find values that optimize your model based on your desired metric.
In this tutorial you will create a hyperparameter search with W&B PyTorch integration. Follow along with a [video tutorial](http://wandb.me/sweeps-video)!
-![](https://i.imgur.com/WVKkMWw.png)
+![](/images/tutorials/sweeps-1.png)
## Sweeps: An Overview
Running a hyperparameter sweep with Weights & Biases is very easy. There are just 3 simple steps:
-1. **Define the sweep:** we do this by creating a dictionary or a [YAML file](https://docs.wandb.com/library/sweeps/configuration) that specifies the parameters to search through, the search strategy, the optimization metric et all.
+1. **Define the sweep:** we do this by creating a dictionary or a [YAML file](/guides/sweeps/define-sweep-configuration) that specifies the parameters to search through, the search strategy, the optimization metric et all.
2. **Initialize the sweep:** with one line of code we initialize the sweep and pass in the dictionary of sweep configurations:
`sweep_id = wandb.sweep(sweep_config)`
@@ -70,12 +60,12 @@ Before you start a sweep, you must define your sweep strategy with a _sweep conf
:::info
The sweep configuration you create for a sweep must be in a nested dictionary if you start a sweep in a Jupyter Notebook.
-If you run a sweep within the command line, you must specify your sweep config with a [YAML file](https://docs.wandb.ai/guides/sweeps/define-sweep-configuration).
+If you run a sweep within the command line, you must specify your sweep config with a [YAML file](/guides/sweeps/define-sweep-configuration).
:::
### Pick a search method
-First, specify a hyperparameter search method within your configuration dictionary. [There are three hyperparameter search strategies to choose from: grid, random, and Bayesian search](https://docs.wandb.ai/guides/sweeps/sweep-config-keys#method).
+First, specify a hyperparameter search method within your configuration dictionary. [There are three hyperparameter search strategies to choose from: grid, random, and Bayesian search](/guides/sweeps/sweep-config-keys#method).
For this tutorial, you will use a random search. Within your notebook, create a dictionary and specify `random` for the `method` key.
@@ -175,7 +165,7 @@ import pprint
pprint.pprint(sweep_config)
```
-For a full list of configuration options, see [Sweep configuration options](https://docs.wandb.ai/guides/sweeps/sweep-config-keys).
+For a full list of configuration options, see [Sweep configuration options](/guides/sweeps/sweep-config-keys).
:::tip
For hyperparameters that have potentially infinite options,
@@ -213,7 +203,7 @@ wandb sweep config.yaml
```
:::
-For more information on how to create W&B Sweeps in a terminal, see the [W&B Sweep walkthrough](https://docs.wandb.com/sweeps/walkthrough).
+For more information on how to create W&B Sweeps in a terminal, see the [W&B Sweep walkthrough](/guides/sweeps/walkthrough).
## Step 3: Define your machine learning code
@@ -226,7 +216,7 @@ In the proceeding code example, the helper functions `build_dataset`, `build_net
Run the proceeding machine learning training code in your notebook. The functions define a basic fully-connected neural network in PyTorch.
-```
+```python
import torch
import torch.optim as optim
import torch.nn.functional as F
@@ -252,9 +242,9 @@ def train(config=None):
```
Within the `train` function, you will notice the following W&B Python SDK methods:
-* [`wandb.init()`](https://docs.wandb.com/library/init) – Initialize a new W&B run. Each run is a single execution of the training function.
-* [`wandb.config`](https://docs.wandb.com/library/config) – Pass sweep configuration with the hyperparameters you want to experiment with.
-* [`wandb.log()`](https://docs.wandb.com/library/log) – Log the training loss for each epoch.
+* [`wandb.init()`](/ref/python/init) – Initialize a new W&B run. Each run is a single execution of the training function.
+* [`wandb.config`](/guides/track/config) – Pass sweep configuration with the hyperparameters you want to experiment with.
+* [`wandb.log()`](/ref/python/log) – Log the training loss for each epoch.
The proceeding cell defines four functions:
@@ -263,7 +253,7 @@ These functions are a standard part of a basic PyTorch pipeline,
and their implementation is unaffected by the use of W&B.
-```
+```python
def build_dataset(batch_size):
transform = transforms.Compose(
@@ -338,7 +328,7 @@ according to the sweep configuration you defined.
The proceeding cell activates a sweep agent that runs the training function (`train`) 5 times:
-```
+```python
wandb.agent(sweep_id, train, count=5)
```
@@ -346,7 +336,7 @@ wandb.agent(sweep_id, train, count=5)
Since the `random` search method was specified in the sweep configuration, the sweep controller provides randomly-generated hyperparameter values.
:::
-For more information on how to create W&B Sweeps in a terminal, see the [W&B Sweep walkthrough](https://docs.wandb.com/sweeps/walkthrough).
+For more information on how to create W&B Sweeps in a terminal, see the [W&B Sweep walkthrough](/guides/sweeps/walkthrough).
## Visualize Sweep Results
@@ -355,14 +345,14 @@ For more information on how to create W&B Sweeps in a terminal, see the [W&B Swe
### Parallel Coordinates Plot
This plot maps hyperparameter values to model metrics. It’s useful for honing in on combinations of hyperparameters that led to the best model performance.
-![](https://assets.website-files.com/5ac6b7f2924c652fd013a891/5e190366778ad831455f9af2_s_194708415DEC35F74A7691FF6810D3B14703D1EFE1672ED29000BA98171242A5_1578695138341_image.png)
+![](/images/tutorials/sweeps-2.png)
### Hyperparameter Importance Plot
The hyperparameter importance plot surfaces which hyperparameters were the best predictors of your metrics.
We report feature importance (from a random forest model) and correlation (implicitly a linear model).
-![](https://assets.website-files.com/5ac6b7f2924c652fd013a891/5e190367778ad820b35f9af5_s_194708415DEC35F74A7691FF6810D3B14703D1EFE1672ED29000BA98171242A5_1578695757573_image.png)
+![](/images/tutorials/sweeps-3.png)
These visualizations can help you save both time and resources running expensive hyperparameter optimizations by honing in on the parameters (and value ranges) that are the most important, and thereby worthy of further exploration.
diff --git a/docs/tutorials/tables.md b/docs/tutorials/tables.md
index 1b15085c9..d414a51f2 100644
--- a/docs/tutorials/tables.md
+++ b/docs/tutorials/tables.md
@@ -1,6 +1,9 @@
-# Visualize predictions
+---
+title: Visualize predictions with tables
+---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-[**Try in a Colab Notebook here →**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/datasets-predictions/W&B_Tables_Quickstart.ipynb)
+
This covers how to track, visualize, and compare model predictions over the course of training, using PyTorch on MNIST data.
@@ -9,28 +12,31 @@ You will learn how to:
2. View, sort, filter, group, join, interactively query, and explore these tables
3. Compare model predictions or results: dynamically across specific images, hyperparameters/model versions, or time steps.
-# Examples
-## Compare predicted scores for specific images
+## Examples
+### Compare predicted scores for specific images
[Live example: compare predictions after 1 vs 5 epochs of training →](https://wandb.ai/stacey/table-quickstart/reports/CNN-2-Progress-over-Training-Time--Vmlldzo3NDY5ODU#compare-predictions-after-1-vs-5-epochs)
-
+
+![1 epoch vs 5 epochs of training](/images/tutorials/tables-1.png)
+
The histograms compare per-class scores between the two models. The top green bar in each histogram represents model "CNN-2, 1 epoch" (id 0), which only trained for 1 epoch. The bottom purple bar represents model "CNN-2, 5 epochs" (id 1), which trained for 5 epochs. The images are filtered to cases where the models disagree. For example, in the first row, the "4" gets high scores across all the possible digits after 1 epoch, but after 5 epochs it scores highest on the correct label and very low on the rest.
-## Focus on top errors over time
+### Focus on top errors over time
[Live example →](https://wandb.ai/stacey/table-quickstart/reports/CNN-2-Progress-over-Training-Time--Vmlldzo3NDY5ODU#top-errors-over-time)
See incorrect predictions (filter to rows where "guess" != "truth") on the full test data. Note that there are 229 wrong guesses after 1 training epoch, but only 98 after 5 epochs.
-
-## Compare model performance and find patterns
+![side by side, 1 vs 5 epochs of training](/images/tutorials/tables-2.png)
+
+### Compare model performance and find patterns
[See full detail in a live example →](https://wandb.ai/stacey/table-quickstart/reports/CNN-2-Progress-over-Training-Time--Vmlldzo3NDY5ODU#false-positives-grouped-by-guess)
Filter out correct answers, then group by the guess to see examples of misclassified images and the underlying distribution of true labels—for two models side-by-side. A model variant with 2X the layer sizes and learning rate is on the left, and the baseline is on the right. Note that the baseline makes slightly more mistakes for each guessed class.
-
+![grouped errors for baseline vs double variant](/images/tutorials/tables-3.png)
-# Sign up or login
+## Sign up or login
[Sign up or login](https://wandb.ai/login) to W&B to see and interact with your experiments in the browser.
@@ -52,7 +58,7 @@ wandb.login()
WANDB_PROJECT = "mnist-viz"
```
-# 0. Setup
+## 0. Setup
Install dependencies, download MNIST, and create train and test datasets using PyTorch.
@@ -78,7 +84,7 @@ def get_dataloader(is_train, batch_size, slice=5):
return loader
```
-# 1. Define the model and training schedule
+## 1. Define the model and training schedule
* Set the number of epochs to run, where each epoch consists of a training step and a validation (test) step. Optionally configure the amount of data to log per test step. Here the number of batches and number of images per batch to visualize are set low to simplify the demo.
* Define a simple convolutional neural net (following [pytorch-tutorial](https://github.com/yunjey/pytorch-tutorial) code).
@@ -141,7 +147,7 @@ test_loader = get_dataloader(is_train=False, batch_size=2*BATCH_SIZE)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
```
-# 2. Run training and log test predictions
+## 2. Run training and log test predictions
For every epoch, run a training step and a test step. For each test step, create a wandb.Table() in which to store test predictions. These can be visualized, dynamically queried, and compared side by side in your browser.
@@ -238,6 +244,6 @@ for epoch in range(EPOCHS):
wandb.finish()
```
-# What's next?
+## What's next?
The next tutorial, you will learn how to optimize hyperparameters using W&B Sweeps:
## 👉 [Optimize Hyperparameters](sweeps)
diff --git a/docs/tutorials/tensorflow.md b/docs/tutorials/tensorflow.md
index acb4ace46..8870c8c34 100644
--- a/docs/tutorials/tensorflow.md
+++ b/docs/tutorials/tensorflow.md
@@ -1,17 +1,13 @@
-# TensorFlow
+---
+title: TensorFlow
+---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-[**Try in a Colab Notebook here →**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/tensorflow/Simple_TensorFlow_Integration.ipynb)
+
Use Weights & Biases for machine learning experiment tracking, dataset versioning, and project collaboration.
-
-
-
-
-
-
-
-
+![](/images/tutorials/huggingface-why.png)
## What this notebook covers
@@ -19,17 +15,12 @@ Use Weights & Biases for machine learning experiment tracking, dataset versionin
* Computing metrics with `keras.metrics`
* Using `wandb.log` to log those metrics in your custom training loop.
-
## The interactive W&B dashboard will look like this:
-
![dashboard](/images/tutorials/tensorflow/dashboard.png)
**Note**: Sections starting with _Step_ are all you need to integrate W&B into existing code. The rest is just a standard MNIST example.
-
-
-
```python
import tensorflow as tf
from tensorflow import keras
@@ -173,9 +164,7 @@ def train(train_dataset, val_dataset, model, optimizer,
This lets us know you're launching an experiment,
so we can give it a unique ID and a dashboard.
-[Check out the official documentation here $\rightarrow$](https://docs.wandb.com/library/init)
-
-
+[Check out the official documentation](/ref/python/init)
```python
# initialize wandb with your project name and optionally with configutations.
@@ -220,14 +209,13 @@ run.finish() # In Jupyter/Colab, let us know you're finished!
# 👀 Visualize Results
-Click on the [**run page**](https://docs.wandb.ai/ref/app/pages/run-page)
-link above to see your live results.
+Click on the [**run page**](/guides/app/pages/run-page) link above to see your live results.
# 🧹 Sweep 101
Use Weights & Biases Sweeps to automate hyperparameter optimization and explore the space of possible models.
-## [Check out Hyperparameter Optimization in TensorFlow using W&B Sweeps $\rightarrow$](http://wandb.me/tf-sweeps-colab)
+## [Check out Hyperparameter Optimization in TensorFlow using W&B Sweeps](http://wandb.me/tf-sweeps-colab)
### Benefits of using W&B Sweeps
@@ -235,10 +223,7 @@ Use Weights & Biases Sweeps to automate hyperparameter optimization and explore
* **Transparent**: We cite all the algorithms we're using, and [our code is open source](https://github.com/wandb/client/tree/master/wandb/sweeps).
* **Powerful**: Our sweeps are completely customizable and configurable. You can launch a sweep across dozens of machines, and it's just as easy as starting a sweep on your laptop.
-
-
-
-
+![Sweep result](/images/tutorials/tensorflow/sweeps.png)
# 🎨 Example Gallery
@@ -252,7 +237,7 @@ See examples of projects tracked and visualized with W&B in our gallery of examp
5. **Reports**: Take quick notes on progress to share with colleagues and make dashboards and snapshots of your ML projects.
## 🤓 Advanced Setup
-1. [Environment variables](https://docs.wandb.com/library/environment-variables): Set API keys in environment variables so you can run training on a managed cluster.
-2. [Offline mode](https://docs.wandb.com/library/technical-faq#can-i-run-wandb-offline): Use `dryrun` mode to train offline and sync results later.
-3. [On-prem](https://docs.wandb.com/self-hosted): Install W&B in a private cloud or air-gapped servers in your own infrastructure. We have local installations for everyone from academics to enterprise teams.
-4. [Artifacts](http://wandb.me/artifacts-colab): Track and version models and datasets in a streamlined way that automatically picks up your pipeline steps as you train models.
+1. [Environment variables](/guides/hosting/env-vars): Set API keys in environment variables so you can run training on a managed cluster.
+2. [Offline mode](/guides/technical-faq/setup/#can-i-run-wandb-offline)
+3. [On-prem](/guides/hosting/hosting-options/self-managed): Install W&B in a private cloud or air-gapped servers in your own infrastructure. We have local installations for everyone from academics to enterprise teams.
+4. [Artifacts](/guides/artifacts): Track and version models and datasets in a streamlined way that automatically picks up your pipeline steps as you train models.
diff --git a/docs/tutorials/tensorflow_sweeps.md b/docs/tutorials/tensorflow_sweeps.md
index 643760b2b..b7824a7a2 100644
--- a/docs/tutorials/tensorflow_sweeps.md
+++ b/docs/tutorials/tensorflow_sweeps.md
@@ -1,14 +1,17 @@
-# TensorFlow Sweeps
+---
+title: TensorFlow Sweeps
+---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-[**Try in a Colab Notebook here →**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/tensorflow/Hyperparameter_Optimization_in_TensorFlow_using_W&B_Sweeps.ipynb)
+
Use Weights & Biases for machine learning experiment tracking, dataset versioning, and project collaboration.
-
+![](/images/tutorials/huggingface-why.png)
Use Weights & Biases Sweeps to automate hyperparameter optimization and explore the space of possible models, complete with interactive dashboards like this:
-![](https://i.imgur.com/AN0qnpC.png)
+![](/images/tutorials/tensorflow/sweeps.png)
## 🤔 Why Should I Use Sweeps?
@@ -17,7 +20,7 @@ Use Weights & Biases Sweeps to automate hyperparameter optimization and explore
* **Transparent**: We cite all the algorithms we're using, and [our code is open source](https://github.com/wandb/client/tree/master/wandb/sweeps).
* **Powerful**: Our sweeps are completely customizable and configurable. You can launch a sweep across dozens of machines, and it's just as easy as starting a sweep on your laptop.
-**[Check out the official documentation $\rightarrow$](https://docs.wandb.com/sweeps)**
+**[Check out the official documentation](/guides/sweeps)**
## What this notebook covers
@@ -34,7 +37,7 @@ The rest of the code is there to set up a simple example.
-# 🚀 Install, Import, and Log in
+## 🚀 Install, Import, and Log in
### Step 0️⃣: Install W&B
@@ -69,7 +72,7 @@ wandb.login()
> Side note: If this is your first time using W&B or you are not logged in, the link that appears after running `wandb.login()` will take you to sign-up/login page. Signing up is as easy as a few clicks.
-# 👩🍳 Prepare Dataset
+## 👩🍳 Prepare Dataset
```python
@@ -82,7 +85,7 @@ x_train = np.reshape(x_train, (-1, 784))
x_test = np.reshape(x_test, (-1, 784))
```
-# 🧠 Define the Model and Training Loop
+## 🧠 Define the Model and Training Loop
## 🏗️ Build a Simple Classifier MLP
@@ -174,7 +177,7 @@ def train(train_dataset,
'val_acc':float(val_acc)})
```
-# Step 4️⃣: Configure the Sweep
+### Step 4️⃣: Configure the Sweep
This is where you will:
* Define the hyperparameters you're sweeping over
@@ -182,7 +185,7 @@ This is where you will:
* Provide an objective and a `metric` if using `bayes`, for example to `minimize` the `val_loss`.
* Use `hyperband` for early termination of poorly-performing runs
-#### [Check out more on Sweep Configs $\rightarrow$](https://docs.wandb.com/sweeps/configuration)
+#### [Check out more on Sweep Configs](/guides/sweeps/define-sweep-configuration)
```python
@@ -207,7 +210,7 @@ sweep_config = {
}
```
-# Step 5️⃣: Wrap the Training Loop
+### Step 5️⃣: Wrap the Training Loop
You'll need a function, like `sweep_train` below,
that uses `wandb.config` to set the hyperparameters
@@ -265,7 +268,7 @@ def sweep_train(config_defaults=None):
val_log_step=wandb.config.val_log_step)
```
-# Step 6️⃣: Initialize Sweep and Run Agent
+### Step 6️⃣: Initialize Sweep and Run Agent
```python
@@ -279,23 +282,23 @@ You can limit the number of total runs with the `count` parameter, we will limit
wandb.agent(sweep_id, function=sweep_train, count=10)
```
-# 👀 Visualize Results
+## 👀 Visualize Results
Click on the **Sweep URL** link above to see your live results.
-# 🎨 Example Gallery
+## 🎨 Example Gallery
See examples of projects tracked and visualized with W&B in our [Gallery →](https://app.wandb.ai/gallery)
-# 📏 Best Practices
+## 📏 Best Practices
1. **Projects**: Log multiple runs to a project to compare them. `wandb.init(project="project-name")`
2. **Groups**: For multiple processes or cross validation folds, log each process as a runs and group them together. `wandb.init(group='experiment-1')`
3. **Tags**: Add tags to track your current baseline or production model.
4. **Notes**: Type notes in the table to track the changes between runs.
5. **Reports**: Take quick notes on progress to share with colleagues and make dashboards and snapshots of your ML projects.
-# 🤓 Advanced Setup
-1. [Environment variables](https://docs.wandb.com/library/environment-variables): Set API keys in environment variables so you can run training on a managed cluster.
-2. [Offline mode](https://docs.wandb.com/library/technical-faq#can-i-run-wandb-offline): Use `dryrun` mode to train offline and sync results later.
-3. [On-prem](https://docs.wandb.com/self-hosted): Install W&B in a private cloud or air-gapped servers in your own infrastructure. We have local installations for everyone from academics to enterprise teams.
+## 🤓 Advanced Setup
+1. [Environment variables](/guides/hosting/env-vars): Set API keys in environment variables so you can run training on a managed cluster.
+2. [Offline mode](/guides/technical-faq/setup/#can-i-run-wandb-offline)
+3. [On-prem](/guides/hosting/hosting-options/self-managed): Install W&B in a private cloud or air-gapped servers in your own infrastructure. We have local installations for everyone from academics to enterprise teams.
diff --git a/docs/tutorials/volcano.md b/docs/tutorials/volcano.md
index b2813d737..dc5b5da9b 100644
--- a/docs/tutorials/volcano.md
+++ b/docs/tutorials/volcano.md
@@ -1,8 +1,9 @@
+---
+title: Launch multinode jobs with Volcano
+---
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
-# Launch multinode jobs with Volcano
-
This tutorial will guide you through the process of launching multinode training jobs with W&B and Volcano on Kubernetes.
## Overview
diff --git a/docs/tutorials/workspaces.md b/docs/tutorials/workspaces.md
index 3e0139d8b..51a1e75cd 100644
--- a/docs/tutorials/workspaces.md
+++ b/docs/tutorials/workspaces.md
@@ -1,14 +1,13 @@
+---
+title: Programmatic Workspaces
+---
import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-# Programmatic Workspaces
-
-
-
Organize and visualize your machine learning experiments more effectively by programmatically create, manage, and customize workspaces by defining configurations, setting panel layouts, and organizing sections with the [`wandb-workspaces`](https://github.com/wandb/wandb-workspaces/tree/main) W&B library. Load and modify workspaces with URLs, filter and group runs using expressions, and customize run appearances.
-`wandb-workspaces` is a Python library for programmatically creating and customizing W&B [Workspaces](https://docs.wandb.ai/guides/app/pages/workspaces) and [Reports](https://docs.wandb.ai/guides/reports).
+`wandb-workspaces` is a Python library for programmatically creating and customizing W&B [Workspaces](/guides/app/pages/workspaces) and [Reports](/guides/reports).
In this tutorial you will see how to use `wandb-workspaces` to create and customize workspaces by defining configurations, set panel layouts, and organize sections.
@@ -19,7 +18,7 @@ In this tutorial you will see how to use `wandb-workspaces` to create and custom
:::info
-Programmatic interaction with workspaces is currently supported for [**Saved workspaces views**](https://docs.wandb.ai/guides/app/pages/workspaces#saved-workspace-views). Saved workspaces views are collaborative snapshots of a workspace. Anyone on your team can view, edit, and save changes to saved workspace views.
+Programmatic interaction with workspaces is currently supported for [**Saved workspaces views**](/guides/app/pages/workspaces#saved-workspace-views). Saved workspaces views are collaborative snapshots of a workspace. Anyone on your team can view, edit, and save changes to saved workspace views.
:::
## 1. Install and import dependencies
diff --git a/docs/tutorials/xgboost.md b/docs/tutorials/xgboost.md
index 677365d3b..7ff26603b 100644
--- a/docs/tutorials/xgboost.md
+++ b/docs/tutorials/xgboost.md
@@ -1,6 +1,9 @@
-# XGBoost
+---
+title: XGBoost
+---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
-[**Try in a Colab Notebook here →**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/boosting/Credit_Scorecards_with_XGBoost_and_W&B.ipynb)
+
In this notebook we'll train a XGBoost model to classify whether submitted loan applications will default or not. Using boosting algorithms such as XGBoost increases the performance of a loan assesment, whilst retaining interpretability for internal Risk Management functions as well as external regulators.
@@ -24,7 +27,7 @@ We will track all of the training hyperparameters and output metrics in order to
**Run a Hyperparameter Sweep to Find the Best HyperParameters**
-Weights and Biases also enables you to do hyperparameter sweeps, either with our own [Sweeps functionality](https://docs.wandb.ai/guides/sweeps) or with our [Ray Tune integration](https://docs.wandb.ai/guides/sweeps/advanced-sweeps/ray-tune). See our docs for a full guide of how to use more advanced hyperparameter sweeps options.
+Weights and Biases also enables you to do hyperparameter sweeps, either with our own [Sweeps functionality](/guides/sweeps) or with our [Ray Tune integration](/guides/integrations/ray-tune). See our docs for a full guide of how to use more advanced hyperparameter sweeps options.
![credit_scorecard_2](/images/tutorials/credit_scorecard/credit_scorecard_2.png)
@@ -91,7 +94,7 @@ artifact = run.use_artifact("mnist:latest", type="dataset")
artifact_dir = artifact.download()
```
-See [Artifact References](https://docs.wandb.ai/guides/artifacts/references) for more on how to use Artifacts by reference, credentials setup etc.
+See [reference artifacts](/guides/artifacts/track-external-files) for more on how to use Artifacts by reference, credentials setup etc.
## Log in to W&B
Log in to Weights and Biases
@@ -266,7 +269,7 @@ trndat.head()
### Log Dataset with W&B Tables
-With W&B Tables you can log, query, and analyze tabular data that contains rich media such as images, video, audio and more. With it you can understand your datasets, visualize model predictions, and share insights, for more see more in our [W&B Tables Guide](https://docs.wandb.ai/guides/tables)
+With W&B Tables you can log, query, and analyze tabular data that contains rich media such as images, video, audio and more. With it you can understand your datasets, visualize model predictions, and share insights, for more see more in our [W&B Tables Guide](/guides/tables)
```python
@@ -356,7 +359,7 @@ y_val = valdat.loc[:, targ_var].astype(int)
#### 4) Fit the model, log results to W&B and save model to W&B Artifacts
-To log all our xgboost model parameters we used the `WandbCallback`. This will . See the [W&B docs](https://docs.wandb.ai/guides/integrations), including documentation for other libraries that have integrated W&B including LightGBM and more.
+To log all our xgboost model parameters we used the `WandbCallback`. This will . See the [W&B docs](/guides/integrations), including documentation for other libraries that have integrated W&B including LightGBM and more.
```python
@@ -442,7 +445,7 @@ Now that we've trained a single model, lets try and optimize its performance by
# HyperParameter Sweep
-Weights and Biases also enables you to do hyperparameter sweeps, either with our own [Sweeps functionality](https://docs.wandb.ai/guides/sweeps/python-api) or with our [Ray Tune integration](https://docs.wandb.ai/guides/sweeps/advanced-sweeps/ray-tune). See [our docs](https://docs.wandb.ai/guides/sweeps/python-api) for a full guide of how to use more advanced hyperparameter sweeps options.
+Weights and Biases also enables you to do hyperparameter sweeps, either with our own [Sweeps functionality](/guides/sweeps) or with our [Ray Tune integration](/guides/integrations/ray-tune).
**[Click Here](https://wandb.ai/morgan/credit_score_sweeps/sweeps/iuppbs45)** to check out the results of a 1000 run sweep generated using this notebook
@@ -551,4 +554,4 @@ Weights and Biases has integrations in all of your favourite ML and Deep Learnin
- Sci-Kit Learn
- LightGBM
-**See [W&B integrations for details](https://docs.wandb.ai/guides/integrations)**
+**See [W&B integrations for details](/guides/integrations)**
diff --git a/docs/tutorials/xgboost_sweeps.md b/docs/tutorials/xgboost_sweeps.md
index 4ff798d56..52efa939a 100644
--- a/docs/tutorials/xgboost_sweeps.md
+++ b/docs/tutorials/xgboost_sweeps.md
@@ -1,13 +1,13 @@
-# XGBoost Sweeps
+---
+title: XGBoost Sweeps
+---
+import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx'
-
-[**Try in a Colab Notebook here →**](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/boosting/Using_W&B_Sweeps_with_XGBoost.ipynb)
+
Use Weights & Biases for machine learning experiment tracking, dataset versioning, and project collaboration.
-
-
-
+![](/images/tutorials/huggingface-why.png)
Squeezing the best performance out of tree-based models requires
[selecting the right hyperparameters](https://blog.cambridgespark.com/hyperparameter-tuning-in-xgboost-4ff9100a3b2f).
@@ -42,7 +42,7 @@ In the notebook below, we'll walk through these 3 steps in more detail.
We highly encourage you to fork this notebook, tweak the parameters, or try the model with your own dataset!
### Resources
-- [Sweeps docs →](https://docs.wandb.com/library/sweeps)
+- [Sweeps docs →](/guides/sweeps)
- [Launching from the command line →](https://www.wandb.com/articles/hyperparameter-tuning-as-easy-as-1-2-3)
@@ -61,7 +61,7 @@ wandb.login()
## 1. Define the Sweep
Weights & Biases sweeps give you powerful levers to configure your sweeps exactly how you want them, with just a few lines of code. The sweeps config can be defined as
-[a dictionary or a YAML file](https://docs.wandb.ai/guides/sweeps/configuration).
+[a dictionary or a YAML file](/guides/sweeps/define-sweep-configuration).
Let's walk through some of them together:
* **Metric** – This is the metric the sweeps are attempting to optimize. Metrics can take a `name` (this metric should be logged by your training script) and a `goal` (`maximize` or `minimize`).
@@ -71,7 +71,7 @@ Let's walk through some of them together:
* **Bayesian Search** – Creates a probabilistic model that maps hyperparameters to probability of a metric score, and chooses parameters with high probability of improving the metric. The objective of Bayesian optimization is to spend more time in picking the hyperparameter values, but in doing so trying out fewer hyperparameter values.
* **Parameters** – A dictionary containing the hyperparameter names, and discrete values, a range, or distributions from which to pull their values on each iteration.
-You can find a list of all configuration options [here](https://docs.wandb.com/library/sweeps/configuration).
+For details, see the [list of all sweep configuration options](/guides/sweeps/define-sweep-configuration).
```python
@@ -185,7 +185,7 @@ and that machine will join the sweep!
> _Note_: a `random` sweep will by defauly run forever,
trying new parameter combinations until the cows come home --
-or until you [turn the sweep off from the app UI](https://docs.wandb.ai/ref/app/features/sweeps).
+or until you [turn the sweep off from the app UI](/guides/sweeps/sweeps-ui).
You can prevent this by providing the total `count` of runs you'd like the `agent` to complete.
diff --git a/package-lock.json b/package-lock.json
deleted file mode 100644
index 42f878ee0..000000000
--- a/package-lock.json
+++ /dev/null
@@ -1,16265 +0,0 @@
-{
- "name": "docodile",
- "version": "0.0.0",
- "lockfileVersion": 3,
- "requires": true,
- "packages": {
- "": {
- "name": "docodile",
- "version": "0.0.0",
- "dependencies": {
- "@docusaurus/core": "^2.3.1",
- "@docusaurus/plugin-google-gtag": "^2.3.1",
- "@docusaurus/plugin-google-tag-manager": "^2.3.1",
- "@docusaurus/preset-classic": "^2.3.1",
- "@laxels/docusaurus-plugin-segment": "^1.0.6",
- "@laxels/serve-static": "^1.15.1",
- "@mdx-js/react": "^1.6.22",
- "clsx": "^1.2.1",
- "docusaurus-lunr-search": "^2.3.2",
- "docusaurus-plugin-image-zoom": "^0.1.4",
- "express": "^4.18.2",
- "helmet": "^6.0.1",
- "plugin-image-zoom": "^1.1.0",
- "prism-react-renderer": "^1.3.5",
- "react": "^17.0.2",
- "react-dom": "^17.0.2"
- },
- "devDependencies": {
- "@docusaurus/module-type-aliases": "^2.3.1",
- "@google-cloud/bigquery": "^6.1.0",
- "@jest/globals": "^29.5.0",
- "@tsconfig/docusaurus": "^1.0.5",
- "@tsconfig/node-lts": "^18.12.1",
- "@types/js-yaml": "^4.0.5",
- "@types/lodash": "^4.14.191",
- "@types/node": "^18.15.5",
- "@types/yargs": "^17.0.23",
- "axios": "^1.3.5",
- "fast-xml-parser": "^4.1.2",
- "immer": "^9.0.19",
- "jest": "^29.5.0",
- "js-yaml": "^4.1.0",
- "lodash": "^4.17.21",
- "ts-jest": "^29.0.5",
- "ts-node": "^10.9.1",
- "typescript": "^4.7.4",
- "yargs": "^17.7.1"
- },
- "engines": {
- "node": ">=16.14"
- }
- },
- "node_modules/@algolia/autocomplete-core": {
- "version": "1.7.4",
- "resolved": "https://registry.npmjs.org/@algolia/autocomplete-core/-/autocomplete-core-1.7.4.tgz",
- "integrity": "sha512-daoLpQ3ps/VTMRZDEBfU8ixXd+amZcNJ4QSP3IERGyzqnL5Ch8uSRFt/4G8pUvW9c3o6GA4vtVv4I4lmnkdXyg==",
- "license": "MIT",
- "dependencies": {
- "@algolia/autocomplete-shared": "1.7.4"
- }
- },
- "node_modules/@algolia/autocomplete-preset-algolia": {
- "version": "1.7.4",
- "resolved": "https://registry.npmjs.org/@algolia/autocomplete-preset-algolia/-/autocomplete-preset-algolia-1.7.4.tgz",
- "integrity": "sha512-s37hrvLEIfcmKY8VU9LsAXgm2yfmkdHT3DnA3SgHaY93yjZ2qL57wzb5QweVkYuEBZkT2PIREvRoLXC2sxTbpQ==",
- "license": "MIT",
- "dependencies": {
- "@algolia/autocomplete-shared": "1.7.4"
- },
- "peerDependencies": {
- "@algolia/client-search": ">= 4.9.1 < 6",
- "algoliasearch": ">= 4.9.1 < 6"
- }
- },
- "node_modules/@algolia/autocomplete-shared": {
- "version": "1.7.4",
- "resolved": "https://registry.npmjs.org/@algolia/autocomplete-shared/-/autocomplete-shared-1.7.4.tgz",
- "integrity": "sha512-2VGCk7I9tA9Ge73Km99+Qg87w0wzW4tgUruvWAn/gfey1ZXgmxZtyIRBebk35R1O8TbK77wujVtCnpsGpRy1kg==",
- "license": "MIT"
- },
- "node_modules/@algolia/cache-browser-local-storage": {
- "version": "4.14.3",
- "resolved": "https://registry.npmjs.org/@algolia/cache-browser-local-storage/-/cache-browser-local-storage-4.14.3.tgz",
- "integrity": "sha512-hWH1yCxgG3+R/xZIscmUrWAIBnmBFHH5j30fY/+aPkEZWt90wYILfAHIOZ1/Wxhho5SkPfwFmT7ooX2d9JeQBw==",
- "license": "MIT",
- "dependencies": {
- "@algolia/cache-common": "4.14.3"
- }
- },
- "node_modules/@algolia/cache-common": {
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- "license": "MIT"
- },
- "node_modules/@algolia/cache-in-memory": {
- "version": "4.14.3",
- "resolved": "https://registry.npmjs.org/@algolia/cache-in-memory/-/cache-in-memory-4.14.3.tgz",
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- "license": "MIT",
- "dependencies": {
- "@algolia/cache-common": "4.14.3"
- }
- },
- "node_modules/@algolia/client-account": {
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- "resolved": "https://registry.npmjs.org/@algolia/client-account/-/client-account-4.14.3.tgz",
- "integrity": "sha512-PBcPb0+f5Xbh5UfLZNx2Ow589OdP8WYjB4CnvupfYBrl9JyC1sdH4jcq/ri8osO/mCZYjZrQsKAPIqW/gQmizQ==",
- "license": "MIT",
- "dependencies": {
- "@algolia/client-common": "4.14.3",
- "@algolia/client-search": "4.14.3",
- "@algolia/transporter": "4.14.3"
- }
- },
- "node_modules/@algolia/client-analytics": {
- "version": "4.14.3",
- "resolved": "https://registry.npmjs.org/@algolia/client-analytics/-/client-analytics-4.14.3.tgz",
- "integrity": "sha512-eAwQq0Hb/aauv9NhCH5Dp3Nm29oFx28sayFN2fdOWemwSeJHIl7TmcsxVlRsO50fsD8CtPcDhtGeD3AIFLNvqw==",
- "license": "MIT",
- "dependencies": {
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- "@algolia/client-search": "4.14.3",
- "@algolia/requester-common": "4.14.3",
- "@algolia/transporter": "4.14.3"
- }
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- "resolved": "https://registry.npmjs.org/xtend/-/xtend-4.0.2.tgz",
- "integrity": "sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==",
- "license": "MIT",
- "engines": {
- "node": ">=0.4"
- }
- },
- "node_modules/y18n": {
- "version": "5.0.8",
- "resolved": "https://registry.npmjs.org/y18n/-/y18n-5.0.8.tgz",
- "integrity": "sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==",
- "dev": true,
- "license": "ISC",
- "engines": {
- "node": ">=10"
- }
- },
- "node_modules/yallist": {
- "version": "4.0.0",
- "resolved": "https://registry.npmjs.org/yallist/-/yallist-4.0.0.tgz",
- "integrity": "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==",
- "license": "ISC"
- },
- "node_modules/yaml": {
- "version": "1.10.2",
- "resolved": "https://registry.npmjs.org/yaml/-/yaml-1.10.2.tgz",
- "integrity": "sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==",
- "license": "ISC",
- "engines": {
- "node": ">= 6"
- }
- },
- "node_modules/yargs": {
- "version": "17.7.1",
- "resolved": "https://registry.npmjs.org/yargs/-/yargs-17.7.1.tgz",
- "integrity": "sha512-cwiTb08Xuv5fqF4AovYacTFNxk62th7LKJ6BL9IGUpTJrWoU7/7WdQGTP2SjKf1dUNBGzDd28p/Yfs/GI6JrLw==",
- "dev": true,
- "license": "MIT",
- "dependencies": {
- "cliui": "^8.0.1",
- "escalade": "^3.1.1",
- "get-caller-file": "^2.0.5",
- "require-directory": "^2.1.1",
- "string-width": "^4.2.3",
- "y18n": "^5.0.5",
- "yargs-parser": "^21.1.1"
- },
- "engines": {
- "node": ">=12"
- }
- },
- "node_modules/yargs-parser": {
- "version": "21.1.1",
- "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-21.1.1.tgz",
- "integrity": "sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==",
- "dev": true,
- "license": "ISC",
- "engines": {
- "node": ">=12"
- }
- },
- "node_modules/yn": {
- "version": "3.1.1",
- "resolved": "https://registry.npmjs.org/yn/-/yn-3.1.1.tgz",
- "integrity": "sha512-Ux4ygGWsu2c7isFWe8Yu1YluJmqVhxqK2cLXNQA5AcC3QfbGNpM7fu0Y8b/z16pXLnFxZYvWhd3fhBY9DLmC6Q==",
- "dev": true,
- "license": "MIT",
- "engines": {
- "node": ">=6"
- }
- },
- "node_modules/yocto-queue": {
- "version": "0.1.0",
- "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
- "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
- "license": "MIT",
- "engines": {
- "node": ">=10"
- },
- "funding": {
- "url": "https://github.com/sponsors/sindresorhus"
- }
- },
- "node_modules/zwitch": {
- "version": "1.0.5",
- "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-1.0.5.tgz",
- "integrity": "sha512-V50KMwwzqJV0NpZIZFwfOD5/lyny3WlSzRiXgA0G7VUnRlqttta1L6UQIHzd6EuBY/cHGfwTIck7w1yH6Q5zUw==",
- "license": "MIT",
- "funding": {
- "type": "github",
- "url": "https://github.com/sponsors/wooorm"
- }
- }
- }
-}
diff --git a/package.json b/package.json
index c356d2798..481ab45f9 100644
--- a/package.json
+++ b/package.json
@@ -8,10 +8,6 @@
"build:prod": "docusaurus build --config docusaurus.config.js",
"build:beta": "docusaurus build --config docusaurus-beta.config.js",
"swizzle": "docusaurus swizzle",
- "deploy:prod": "yarn build:prod && yarn deploy-gae:prod",
- "deploy:beta": "yarn build:beta && yarn deploy-gae:beta",
- "deploy-gae:prod": "gcloud config set project wandb-docs && gcloud app deploy app.yaml",
- "deploy-gae:beta": "gcloud config set project wandb-docs && gcloud app deploy app-beta.yaml",
"rebuild-search-index:prod": "docker run -it --env-file=.env.local -e \"CONFIG=$(cat docSearchConfig.json | jq -r tostring)\" algolia/docsearch-scraper",
"rebuild-search-index:beta": "docker run -it --env-file=.env.local -e \"CONFIG=$(cat docSearchConfig-beta.json | jq -r tostring)\" algolia/docsearch-scraper",
"clear": "docusaurus clear",
@@ -23,27 +19,31 @@
"redirect-404s": "ts-node scripts/getRedirectsFrom404s"
},
"dependencies": {
- "@docusaurus/core": "^2.3.1",
- "@docusaurus/plugin-google-gtag": "^2.3.1",
- "@docusaurus/plugin-google-tag-manager": "^2.3.1",
- "@docusaurus/preset-classic": "^2.3.1",
+ "@algolia/client-search": "^5.6.1",
+ "@docusaurus/core": "^3.5.2",
+ "@docusaurus/plugin-google-gtag": "^3.5.2",
+ "@docusaurus/plugin-google-tag-manager": "^3.5.2",
+ "@docusaurus/preset-classic": "^3.5.2",
"@laxels/docusaurus-plugin-segment": "^1.0.6",
"@laxels/serve-static": "^1.15.1",
- "@mdx-js/react": "^1.6.22",
- "clsx": "^1.2.1",
- "docusaurus-lunr-search": "^2.3.2",
+ "@mdx-js/react": "^3.0.1",
+ "@types/react": "^18.3.9",
+ "classnames": "^2.5.1",
+ "clsx": "^2.0.0",
+ "docusaurus-lunr-search": "^3.5.0",
"docusaurus-plugin-image-zoom": "^0.1.4",
"express": "^4.18.2",
"helmet": "^6.0.1",
"plugin-image-zoom": "^1.1.0",
"prism-react-renderer": "^1.3.5",
- "react": "^17.0.2",
- "react-dom": "^17.0.2",
- "styled-components": "^6.0.1"
+ "react": "^18.0.0",
+ "react-dom": "^18.0.0",
+ "react-loadable": "^5.5.0",
+ "search-insights": "^2.17.2",
+ "styled-components": "^6.1.8"
},
"devDependencies": {
- "@docusaurus/module-type-aliases": "^2.3.1",
- "@google-cloud/bigquery": "^6.1.0",
+ "@docusaurus/module-type-aliases": "^3.5.2",
"@jest/globals": "^29.5.0",
"@tsconfig/docusaurus": "^1.0.5",
"@tsconfig/node-lts": "^18.12.1",
@@ -59,7 +59,7 @@
"lodash": "^4.17.21",
"ts-jest": "^29.0.5",
"ts-node": "^10.9.1",
- "typescript": "^4.7.4",
+ "typescript": "^5.0.0",
"yargs": "^17.7.1"
},
"browserslist": {
@@ -76,5 +76,6 @@
},
"engines": {
"node": ">=16.14"
- }
+ },
+ "packageManager": "yarn@1.22.22"
}
diff --git a/scripts/production.sh b/scripts/production.sh
index c0c76e4f3..c66f77c79 100644
--- a/scripts/production.sh
+++ b/scripts/production.sh
@@ -11,17 +11,18 @@ git remote set-branches origin '*'
git fetch -v --depth=1
# The JA and KO builds use special scripts for the baseURL mod
-# JA
+# JA (requires node 18.0.0)
git checkout japanese_docs
yarn install
sh scripts/build-prod-docs.sh
mv build/ja ../../build
+git stash
# KO
git checkout korean_docs
yarn install
sh scripts/build-prod-docs.sh
mv build/ko ../../build
-
+git stash
# Cleanup
cd ../..
rm -rf scripts/docodile
\ No newline at end of file
diff --git a/sidebars.js b/sidebars.js
index f2adb1c51..a526a53d1 100644
--- a/sidebars.js
+++ b/sidebars.js
@@ -1,18 +1,4 @@
-/**
- * Creating a sidebar enables you to:
- - create an ordered group of docs
- - render a sidebar for each doc of that group
- - provide next/previous navigation
-
- The sidebars can be generated from the filesystem, or explicitly defined here.
-
- Create as many sidebars as you want.
- */
-
-// @ts-check
-
-/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */
-const sidebars = {
+export default {
default: [
'guides/intro',
'quickstart',
@@ -72,7 +58,7 @@ const sidebars = {
'guides/track/environment-variables',
],
},
- {
+ {
type: 'category',
label: 'Sweeps',
link: {type: 'doc', id: 'guides/sweeps/intro'},
@@ -99,7 +85,7 @@ const sidebars = {
'guides/sweeps/useful-resources',
'guides/sweeps/existing-project',
],
- },
+ },
{
type: 'category',
label: 'Registry',
@@ -133,14 +119,14 @@ const sidebars = {
],
},
],
- },
+ },
{
type: 'category',
label: 'Automations',
items: [
'guides/model_registry/model-registry-automations',
'guides/artifacts/project-scoped-automations',
- ]
+ ],
},
{
type: 'category',
@@ -190,65 +176,57 @@ const sidebars = {
items: [
{
type: 'category',
- label: 'Features',
- link: {type: 'doc', id: 'guides/app/features/intro'},
+ label: 'Panels',
+ link: {type: 'doc', id: 'guides/app/features/panels/intro'},
items: [
{
type: 'category',
- label: 'Panels',
- link: {type: 'doc', id: 'guides/app/features/panels/intro'},
+ label: 'Line Plot',
+ link: {
+ type: 'doc',
+ id: 'guides/app/features/panels/line-plot/intro',
+ },
items: [
- {
- type: 'category',
- label: 'Line Plot',
- link: {
- type: 'doc',
- id: 'guides/app/features/panels/line-plot/intro',
- },
- items: [
- 'guides/app/features/panels/line-plot/reference',
- 'guides/app/features/panels/line-plot/sampling',
- 'guides/app/features/panels/line-plot/smoothing',
- ],
- },
- 'guides/app/features/panels/bar-plot',
- 'guides/app/features/panels/run-colors',
- 'guides/app/features/panels/parallel-coordinates',
- 'guides/app/features/panels/scatter-plot',
- 'guides/app/features/panels/code',
- 'guides/app/features/panels/parameter-importance',
- 'guides/app/features/panels/run-comparer',
-
- {
- type: 'category',
- label: 'Query panels',
- link: {
- type: 'doc',
- id: 'guides/app/features/panels/query-panel/intro',
- },
- items: [
- 'guides/app/features/panels/query-panel/embedding-projector',
- ],
- },
+ 'guides/app/features/panels/line-plot/reference',
+ 'guides/app/features/panels/line-plot/sampling',
+ 'guides/app/features/panels/line-plot/smoothing',
],
},
+ 'guides/app/features/panels/bar-plot',
+ 'guides/app/features/panels/run-colors',
+ 'guides/app/features/panels/parallel-coordinates',
+ 'guides/app/features/panels/scatter-plot',
+ 'guides/app/features/panels/code',
+ 'guides/app/features/panels/parameter-importance',
+ 'guides/app/features/panels/run-comparer',
{
type: 'category',
- label: 'Custom Charts',
+ label: 'Query panels',
link: {
type: 'doc',
- id: 'guides/app/features/custom-charts/intro',
+ id: 'guides/app/features/panels/query-panel/intro',
},
items: [
- // 'guides/app/features/custom-charts/intro',
- 'guides/app/features/custom-charts/walkthrough',
+ 'guides/app/features/panels/query-panel/embedding-projector',
],
},
- 'guides/app/features/runs-table',
- 'guides/app/features/tags',
- 'guides/app/features/notes',
],
},
+ {
+ type: 'category',
+ label: 'Custom Charts',
+ link: {
+ type: 'doc',
+ id: 'guides/app/features/custom-charts/intro',
+ },
+ items: [
+ // 'guides/app/features/custom-charts/intro',
+ 'guides/app/features/custom-charts/walkthrough',
+ ],
+ },
+ 'guides/app/features/runs-table',
+ 'guides/app/features/tags',
+ 'guides/app/features/notes',
{
type: 'category',
label: 'Settings',
@@ -271,7 +249,7 @@ const sidebars = {
{
type: 'category',
label: 'W&B Weave',
- link: { type: 'doc', id: 'guides/weave_platform'},
+ link: {type: 'doc', id: 'guides/weave_platform'},
items: [],
},
{
@@ -338,7 +316,7 @@ const sidebars = {
{
type: 'category',
label: 'W&B Platform',
- link: { type: 'doc', id: 'guides/hosting/intro' },
+ link: {type: 'doc', id: 'guides/hosting/intro'},
items: [
{
type: 'category',
@@ -348,7 +326,10 @@ const sidebars = {
{
type: 'category',
label: 'Dedicated Cloud',
- link: { type: 'doc', id: 'guides/hosting/hosting-options/dedicated_cloud' },
+ link: {
+ type: 'doc',
+ id: 'guides/hosting/hosting-options/dedicated_cloud',
+ },
items: [
'guides/hosting/hosting-options/dedicated_regions',
'guides/hosting/export-data-from-dedicated-cloud',
@@ -357,10 +338,20 @@ const sidebars = {
{
type: 'category',
label: 'Self Managed',
- link: { type: 'doc', id: 'guides/hosting/hosting-options/self-managed' },
+ link: {
+ type: 'doc',
+ id: 'guides/hosting/hosting-options/self-managed',
+ },
items: [
'guides/hosting/self-managed/basic-setup',
- 'guides/hosting/operator',
+ {
+ type: 'category',
+ label: 'W&B Operator',
+ items: [
+ 'guides/hosting/operator',
+ 'guides/hosting/operator-airgapped',
+ ],
+ },
{
type: 'category',
label: 'Install on public cloud',
@@ -375,11 +366,11 @@ const sidebars = {
],
},
],
- },
+ },
{
type: 'category',
label: 'Identity and access management (IAM)',
- link: { type: 'doc', id: 'guides/hosting/iam/org_team_struct'},
+ link: {type: 'doc', id: 'guides/hosting/iam/org_team_struct'},
items: [
{
type: 'category',
@@ -387,7 +378,7 @@ const sidebars = {
items: [
'guides/hosting/iam/sso',
'guides/hosting/iam/ldap',
- 'guides/hosting/iam/identity_federation'
+ 'guides/hosting/iam/identity_federation',
],
},
{
@@ -400,7 +391,7 @@ const sidebars = {
},
'guides/hosting/iam/automate_iam',
'guides/hosting/iam/scim',
- 'guides/hosting/iam/advanced_env_vars',
+ 'guides/hosting/iam/advanced_env_vars',
],
},
{
@@ -414,7 +405,7 @@ const sidebars = {
'guides/hosting/data-security/data-encryption',
],
},
- 'guides/hosting/privacy-settings',
+ 'guides/hosting/privacy-settings',
{
type: 'category',
label: 'Monitoring and Usage',
@@ -439,6 +430,7 @@ const sidebars = {
'guides/integrations/add-wandb-to-any-library',
'guides/integrations/other/azure-openai-fine-tuning',
'guides/integrations/other/catalyst',
+ 'guides/integrations/other/cohere-fine-tuning',
'guides/integrations/dagster',
'guides/integrations/other/databricks',
'guides/integrations/other/deepchecks',
@@ -449,9 +441,7 @@ const sidebars = {
type: 'category',
label: 'Fastai',
link: {type: 'doc', id: 'guides/integrations/fastai/README'},
- items: [
- 'guides/integrations/fastai/v1',
- ],
+ items: ['guides/integrations/fastai/v1'],
},
'guides/integrations/huggingface',
'guides/integrations/diffusers',
@@ -483,15 +473,15 @@ const sidebars = {
label: 'Prompts',
link: {
type: 'doc',
- id: 'guides/integrations/prompts/intro'
+ id: 'guides/integrations/prompts/intro',
},
items: [
- 'guides/integrations/prompts/quickstart',
- 'guides/integrations/prompts/openai'
+ 'guides/integrations/prompts/quickstart',
+ 'guides/integrations/prompts/openai',
],
},
'guides/integrations/other/ray-tune',
- 'guides/integrations/other/sagemaker',
+ 'guides/integrations/other/sagemaker',
'guides/integrations/scikit',
'guides/integrations/other/simpletransformers',
'guides/integrations/other/skorch',
@@ -562,5 +552,3 @@ const sidebars = {
},
],
};
-
-module.exports = sidebars;
diff --git a/src/theme/DocBreadcrumbs/index.tsx b/src/theme/DocBreadcrumbs/index.tsx
index 3b1b4a918..900e55844 100644
--- a/src/theme/DocBreadcrumbs/index.tsx
+++ b/src/theme/DocBreadcrumbs/index.tsx
@@ -1,10 +1,8 @@
import React, {type ReactNode} from 'react';
import clsx from 'clsx';
import {ThemeClassNames} from '@docusaurus/theme-common';
-import {
- useSidebarBreadcrumbs,
- useHomePageRoute,
-} from '@docusaurus/theme-common/internal';
+import {useSidebarBreadcrumbs} from '@docusaurus/plugin-content-docs/client';
+import {useHomePageRoute} from '@docusaurus/theme-common/internal';
import Link from '@docusaurus/Link';
import useBaseUrl from '@docusaurus/useBaseUrl';
import {translate} from '@docusaurus/Translate';
diff --git a/src/theme/SearchBar/index.tsx b/src/theme/SearchBar/index.tsx
index 0901c0c57..c0a638ae1 100644
--- a/src/theme/SearchBar/index.tsx
+++ b/src/theme/SearchBar/index.tsx
@@ -6,7 +6,7 @@ import {useBaseUrlUtils} from '@docusaurus/useBaseUrl';
import Link from '@docusaurus/Link';
import Head from '@docusaurus/Head';
import {isRegexpStringMatch} from '@docusaurus/theme-common';
-import {useSearchPage} from '@docusaurus/theme-common/internal';
+import {useSearchLinkCreator} from '@docusaurus/theme-common';
import {DocSearchButton, useDocSearchKeyboardEvents} from '@docsearch/react';
import {useAlgoliaContextualFacetFilters} from '@docusaurus/theme-search-algolia/client';
import Translate from '@docusaurus/Translate';
@@ -51,7 +51,7 @@ type ResultsFooterProps = {
};
function ResultsFooter({state, onClose}: ResultsFooterProps) {
- const {generateSearchPageLink} = useSearchPage();
+ const generateSearchPageLink = useSearchLinkCreator();
return (
diff --git a/static/images/app_ui/add_single_panel.gif b/static/images/app_ui/add_single_panel.gif
new file mode 100644
index 000000000..0be4281cd
Binary files /dev/null and b/static/images/app_ui/add_single_panel.gif differ
diff --git a/static/images/app_ui/auto_panel_off.png b/static/images/app_ui/auto_panel_off.png
new file mode 100644
index 000000000..99f65702b
Binary files /dev/null and b/static/images/app_ui/auto_panel_off.png differ
diff --git a/static/images/app_ui/auto_panel_on.png b/static/images/app_ui/auto_panel_on.png
new file mode 100644
index 000000000..4ac9cd432
Binary files /dev/null and b/static/images/app_ui/auto_panel_on.png differ
diff --git a/static/images/app_ui/bulk_panels.gif b/static/images/app_ui/bulk_panels.gif
new file mode 100644
index 000000000..ab3404862
Binary files /dev/null and b/static/images/app_ui/bulk_panels.gif differ
diff --git a/static/images/app_ui/panel_generation.png b/static/images/app_ui/panel_generation.png
new file mode 100644
index 000000000..fe5751be3
Binary files /dev/null and b/static/images/app_ui/panel_generation.png differ
diff --git a/static/images/app_ui/rearrange_panels.gif b/static/images/app_ui/rearrange_panels.gif
new file mode 100644
index 000000000..5cc3fdfcd
Binary files /dev/null and b/static/images/app_ui/rearrange_panels.gif differ
diff --git a/static/images/general/custom-charts-1.png b/static/images/general/custom-charts-1.png
new file mode 100644
index 000000000..b446d7a0c
Binary files /dev/null and b/static/images/general/custom-charts-1.png differ
diff --git a/static/images/general/custom-charts-2.png b/static/images/general/custom-charts-2.png
new file mode 100644
index 000000000..30077d3ee
Binary files /dev/null and b/static/images/general/custom-charts-2.png differ
diff --git a/static/images/general/custom-charts-3.png b/static/images/general/custom-charts-3.png
new file mode 100644
index 000000000..fda6593d9
Binary files /dev/null and b/static/images/general/custom-charts-3.png differ
diff --git a/static/images/general/custom-charts-4.png b/static/images/general/custom-charts-4.png
new file mode 100644
index 000000000..4fc8870ae
Binary files /dev/null and b/static/images/general/custom-charts-4.png differ
diff --git a/static/images/general/parameter-importance-1.png b/static/images/general/parameter-importance-1.png
new file mode 100644
index 000000000..b37d7a217
Binary files /dev/null and b/static/images/general/parameter-importance-1.png differ
diff --git a/static/images/general/parameter-importance-2.png b/static/images/general/parameter-importance-2.png
new file mode 100644
index 000000000..f5c61a1f4
Binary files /dev/null and b/static/images/general/parameter-importance-2.png differ
diff --git a/static/images/general/parameter-importance-3.png b/static/images/general/parameter-importance-3.png
new file mode 100644
index 000000000..f8dc89e67
Binary files /dev/null and b/static/images/general/parameter-importance-3.png differ
diff --git a/static/images/general/parameter-importance-4.png b/static/images/general/parameter-importance-4.png
new file mode 100644
index 000000000..313c70dc2
Binary files /dev/null and b/static/images/general/parameter-importance-4.png differ
diff --git a/static/images/general/run-comparer-1.png b/static/images/general/run-comparer-1.png
new file mode 100644
index 000000000..901c3c793
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diff --git a/yarn.lock b/yarn.lock
index 992aea3a6..e0a2386c6 100644
--- a/yarn.lock
+++ b/yarn.lock
@@ -2,1066 +2,610 @@
# yarn lockfile v1
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+"@algolia/autocomplete-core@1.9.3":
+ version "1.9.3"
+ resolved "https://registry.yarnpkg.com/@algolia/autocomplete-core/-/autocomplete-core-1.9.3.tgz#1d56482a768c33aae0868c8533049e02e8961be7"
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dependencies:
- "@algolia/autocomplete-shared" "1.7.4"
+ "@algolia/autocomplete-plugin-algolia-insights" "1.9.3"
+ "@algolia/autocomplete-shared" "1.9.3"
-"@algolia/autocomplete-preset-algolia@1.7.4":
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- resolved "https://registry.npmjs.org/@algolia/autocomplete-preset-algolia/-/autocomplete-preset-algolia-1.7.4.tgz"
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+"@algolia/autocomplete-plugin-algolia-insights@1.9.3":
+ version "1.9.3"
+ resolved "https://registry.yarnpkg.com/@algolia/autocomplete-plugin-algolia-insights/-/autocomplete-plugin-algolia-insights-1.9.3.tgz#9b7f8641052c8ead6d66c1623d444cbe19dde587"
+ integrity sha512-a/yTUkcO/Vyy+JffmAnTWbr4/90cLzw+CC3bRbhnULr/EM0fGNvM13oQQ14f2moLMcVDyAx/leczLlAOovhSZg==
dependencies:
- "@algolia/autocomplete-shared" "1.7.4"
+ "@algolia/autocomplete-shared" "1.9.3"
-"@algolia/autocomplete-shared@1.7.4":
- version "1.7.4"
- resolved "https://registry.npmjs.org/@algolia/autocomplete-shared/-/autocomplete-shared-1.7.4.tgz"
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-
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+ resolved "https://registry.yarnpkg.com/@algolia/autocomplete-preset-algolia/-/autocomplete-preset-algolia-1.9.3.tgz#64cca4a4304cfcad2cf730e83067e0c1b2f485da"
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+ dependencies:
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+ dependencies:
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+ dependencies:
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+ "@algolia/transporter" "4.24.0"
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+ dependencies:
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+
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+ version "5.6.1"
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+
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+ version "4.24.0"
+ resolved "https://registry.yarnpkg.com/@algolia/client-personalization/-/client-personalization-4.24.0.tgz#8b47789fb1cb0f8efbea0f79295b7c5a3850f6ae"
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+ "@algolia/transporter" "4.24.0"
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+ "@algolia/transporter" "4.24.0"
+
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- dependencies:
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+ jest-message-util "^29.7.0"
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+ version "29.7.0"
+ resolved "https://registry.yarnpkg.com/@jest/reporters/-/reporters-29.7.0.tgz#04b262ecb3b8faa83b0b3d321623972393e8f4c7"
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+ "@jridgewell/trace-mapping" "^0.3.18"
"@types/node" "*"
chalk "^4.0.0"
collect-v8-coverage "^1.0.0"
@@ -2727,153 +1850,145 @@
glob "^7.1.3"
graceful-fs "^4.2.9"
istanbul-lib-coverage "^3.0.0"
- istanbul-lib-instrument "^5.1.0"
+ istanbul-lib-instrument "^6.0.0"
istanbul-lib-report "^3.0.0"
istanbul-lib-source-maps "^4.0.0"
istanbul-reports "^3.1.3"
- jest-message-util "^29.5.0"
- jest-util "^29.5.0"
- jest-worker "^29.5.0"
+ jest-message-util "^29.7.0"
+ jest-util "^29.7.0"
+ jest-worker "^29.7.0"
slash "^3.0.0"
string-length "^4.0.1"
strip-ansi "^6.0.0"
v8-to-istanbul "^9.0.1"
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+"@jest/schemas@^29.6.3":
+ version "29.6.3"
+ resolved "https://registry.yarnpkg.com/@jest/schemas/-/schemas-29.6.3.tgz#430b5ce8a4e0044a7e3819663305a7b3091c8e03"
+ integrity sha512-mo5j5X+jIZmJQveBKeS/clAueipV7KgiX1vMgCxam1RNYiqE1w62n0/tJJnHtjW8ZHcQco5gY85jA3mi0L+nSA==
dependencies:
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+ "@sinclair/typebox" "^0.27.8"
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dependencies:
- "@jridgewell/trace-mapping" "^0.3.15"
+ "@jridgewell/trace-mapping" "^0.3.18"
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graceful-fs "^4.2.9"
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graceful-fs "^4.2.9"
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"@jridgewell/trace-mapping@0.3.9":
version "0.3.9"
- resolved "https://registry.npmjs.org/@jridgewell/trace-mapping/-/trace-mapping-0.3.9.tgz"
+ resolved "https://registry.yarnpkg.com/@jridgewell/trace-mapping/-/trace-mapping-0.3.9.tgz#6534fd5933a53ba7cbf3a17615e273a0d1273ff9"
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dependencies:
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+ "@jridgewell/resolve-uri" "^3.1.0"
+ "@jridgewell/sourcemap-codec" "^1.4.14"
"@laxels/docusaurus-plugin-segment@^1.0.6":
version "1.0.6"
- resolved "https://registry.npmjs.org/@laxels/docusaurus-plugin-segment/-/docusaurus-plugin-segment-1.0.6.tgz"
+ resolved "https://registry.yarnpkg.com/@laxels/docusaurus-plugin-segment/-/docusaurus-plugin-segment-1.0.6.tgz#224fc874483e11cd753d3d9d8f7e99d73c01dafa"
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dependencies:
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"@laxels/serve-static@^1.15.1":
version "1.15.1"
- resolved "https://registry.npmjs.org/@laxels/serve-static/-/serve-static-1.15.1.tgz"
+ resolved "https://registry.yarnpkg.com/@laxels/serve-static/-/serve-static-1.15.1.tgz#8891c97297b610adf2d6aaefaeb9680d84ff43e2"
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dependencies:
encodeurl "~1.0.2"
@@ -2882,72 +1997,68 @@
send "0.18.0"
"@leichtgewicht/ip-codec@^2.0.1":
- version "2.0.4"
- resolved "https://registry.npmjs.org/@leichtgewicht/ip-codec/-/ip-codec-2.0.4.tgz"
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-
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- resolved "https://registry.npmjs.org/@mdx-js/mdx/-/mdx-1.6.22.tgz"
- integrity sha512-AMxuLxPz2j5/6TpF/XSdKpQP1NlG0z11dFOlq+2IP/lSgl11GY8ji6S/rgsViN/L0BDvHvUMruRb7ub+24LUYA==
- dependencies:
- "@babel/core" "7.12.9"
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aggregate-error@^3.0.0:
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ajv-formats@^2.1.1:
version "2.1.1"
- resolved "https://registry.npmjs.org/ajv-formats/-/ajv-formats-2.1.1.tgz"
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dependencies:
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version "6.12.6"
- resolved "https://registry.npmjs.org/ajv/-/ajv-6.12.6.tgz"
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dependencies:
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dependencies:
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version "6.2.1"
- resolved "https://registry.npmjs.org/boxen/-/boxen-6.2.1.tgz"
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@@ -4188,132 +3282,138 @@ boxen@^6.2.1:
widest-line "^4.0.1"
wrap-ansi "^8.0.1"
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version "4.1.2"
- resolved "https://registry.npmjs.org/camel-case/-/camel-case-4.1.2.tgz"
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version "5.3.1"
- resolved "https://registry.npmjs.org/camelcase/-/camelcase-5.3.1.tgz"
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camelcase@^6.2.0:
version "6.3.0"
- resolved "https://registry.npmjs.org/camelcase/-/camelcase-6.3.0.tgz"
+ resolved "https://registry.yarnpkg.com/camelcase/-/camelcase-6.3.0.tgz#5685b95eb209ac9c0c177467778c9c84df58ba9a"
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+camelcase@^7.0.1:
+ version "7.0.1"
+ resolved "https://registry.yarnpkg.com/camelcase/-/camelcase-7.0.1.tgz#f02e50af9fd7782bc8b88a3558c32fd3a388f048"
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+
camelize@^1.0.0:
version "1.0.1"
resolved "https://registry.yarnpkg.com/camelize/-/camelize-1.0.1.tgz#89b7e16884056331a35d6b5ad064332c91daa6c3"
@@ -4321,7 +3421,7 @@ camelize@^1.0.0:
caniuse-api@^3.0.0:
version "3.0.0"
- resolved "https://registry.npmjs.org/caniuse-api/-/caniuse-api-3.0.0.tgz"
+ resolved "https://registry.yarnpkg.com/caniuse-api/-/caniuse-api-3.0.0.tgz#5e4d90e2274961d46291997df599e3ed008ee4c0"
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dependencies:
browserslist "^4.0.0"
@@ -4329,61 +3429,66 @@ caniuse-api@^3.0.0:
lodash.memoize "^4.1.2"
lodash.uniq "^4.5.0"
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+ccount@^2.0.0:
+ version "2.0.1"
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-chalk@^2.0.0:
+chalk@^2.4.2:
version "2.4.2"
- resolved "https://registry.npmjs.org/chalk/-/chalk-2.4.2.tgz"
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escape-string-regexp "^1.0.5"
supports-color "^5.3.0"
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supports-color "^7.1.0"
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+ version "5.3.0"
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+
+character-reference-invalid@^2.0.0:
+ version "2.0.1"
+ resolved "https://registry.yarnpkg.com/character-reference-invalid/-/character-reference-invalid-2.0.1.tgz#85c66b041e43b47210faf401278abf808ac45cb9"
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cheerio-select@^2.1.0:
version "2.1.0"
- resolved "https://registry.npmjs.org/cheerio-select/-/cheerio-select-2.1.0.tgz"
+ resolved "https://registry.yarnpkg.com/cheerio-select/-/cheerio-select-2.1.0.tgz#4d8673286b8126ca2a8e42740d5e3c4884ae21b4"
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dependencies:
boolbase "^1.0.0"
@@ -4393,9 +3498,9 @@ cheerio-select@^2.1.0:
domhandler "^5.0.3"
domutils "^3.0.1"
-cheerio@^1.0.0-rc.12:
+cheerio@1.0.0-rc.12:
version "1.0.0-rc.12"
- resolved "https://registry.npmjs.org/cheerio/-/cheerio-1.0.0-rc.12.tgz"
+ resolved "https://registry.yarnpkg.com/cheerio/-/cheerio-1.0.0-rc.12.tgz#788bf7466506b1c6bf5fae51d24a2c4d62e47683"
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dependencies:
cheerio-select "^2.1.0"
@@ -4406,10 +3511,10 @@ cheerio@^1.0.0-rc.12:
parse5 "^7.0.0"
parse5-htmlparser2-tree-adapter "^7.0.0"
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cliui@^8.0.1:
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- resolved "https://registry.npmjs.org/cliui/-/cliui-8.0.1.tgz"
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- resolved "https://registry.npmjs.org/clone-deep/-/clone-deep-4.0.1.tgz"
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concat-map@0.0.1:
version "0.0.1"
- resolved "https://registry.npmjs.org/concat-map/-/concat-map-0.0.1.tgz"
+ resolved "https://registry.yarnpkg.com/concat-map/-/concat-map-0.0.1.tgz#d8a96bd77fd68df7793a73036a3ba0d5405d477b"
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+config-chain@^1.1.11:
+ version "1.1.13"
+ resolved "https://registry.yarnpkg.com/config-chain/-/config-chain-1.1.13.tgz#fad0795aa6a6cdaff9ed1b68e9dff94372c232f4"
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- make-dir "^3.0.0"
- unique-string "^2.0.0"
- write-file-atomic "^3.0.0"
- xdg-basedir "^4.0.0"
+ ini "^1.3.4"
+ proto-list "~1.2.1"
+
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+ version "6.0.0"
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+ graceful-fs "^4.2.6"
+ unique-string "^3.0.0"
+ write-file-atomic "^3.0.3"
+ xdg-basedir "^5.0.1"
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version "2.0.0"
- resolved "https://registry.npmjs.org/connect-history-api-fallback/-/connect-history-api-fallback-2.0.0.tgz"
+ resolved "https://registry.yarnpkg.com/connect-history-api-fallback/-/connect-history-api-fallback-2.0.0.tgz#647264845251a0daf25b97ce87834cace0f5f1c8"
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consola@^2.15.3:
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content-disposition@0.5.2:
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content-disposition@0.5.4:
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version "2.0.0"
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cookie-signature@1.0.6:
version "1.0.6"
- resolved "https://registry.npmjs.org/cookie-signature/-/cookie-signature-1.0.6.tgz"
+ resolved "https://registry.yarnpkg.com/cookie-signature/-/cookie-signature-1.0.6.tgz#e303a882b342cc3ee8ca513a79999734dab3ae2c"
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+copy-text-to-clipboard@^3.2.0:
+ version "3.2.0"
+ resolved "https://registry.yarnpkg.com/copy-text-to-clipboard/-/copy-text-to-clipboard-3.2.0.tgz#0202b2d9bdae30a49a53f898626dcc3b49ad960b"
+ integrity sha512-RnJFp1XR/LOBDckxTib5Qjr/PMfkatD0MUCQgdpqS8MdKiNUzBjAQBEN6oUy+jW7LI93BBG3DtMB2KOOKpGs2Q==
copy-webpack-plugin@^11.0.0:
version "11.0.0"
- resolved "https://registry.npmjs.org/copy-webpack-plugin/-/copy-webpack-plugin-11.0.0.tgz"
+ resolved "https://registry.yarnpkg.com/copy-webpack-plugin/-/copy-webpack-plugin-11.0.0.tgz#96d4dbdb5f73d02dd72d0528d1958721ab72e04a"
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dependencies:
fast-glob "^3.2.11"
@@ -4714,38 +3814,31 @@ copy-webpack-plugin@^11.0.0:
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serialize-javascript "^6.0.0"
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- dependencies:
- browserslist "^4.21.4"
-
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- version "3.31.0"
- resolved "https://registry.yarnpkg.com/core-js-compat/-/core-js-compat-3.31.0.tgz#4030847c0766cc0e803dcdfb30055d7ef2064bf1"
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+core-js-compat@^3.37.1, core-js-compat@^3.38.0:
+ version "3.38.1"
+ resolved "https://registry.yarnpkg.com/core-js-compat/-/core-js-compat-3.38.1.tgz#2bc7a298746ca5a7bcb9c164bcb120f2ebc09a09"
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dependencies:
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+ browserslist "^4.23.3"
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+core-js-pure@^3.30.2:
+ version "3.38.1"
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core-util-is@~1.0.0:
version "1.0.3"
- resolved "https://registry.npmjs.org/core-util-is/-/core-util-is-1.0.3.tgz"
+ resolved "https://registry.yarnpkg.com/core-util-is/-/core-util-is-1.0.3.tgz#a6042d3634c2b27e9328f837b965fac83808db85"
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cosmiconfig@^6.0.0:
version "6.0.0"
- resolved "https://registry.npmjs.org/cosmiconfig/-/cosmiconfig-6.0.0.tgz"
+ resolved "https://registry.yarnpkg.com/cosmiconfig/-/cosmiconfig-6.0.0.tgz#da4fee853c52f6b1e6935f41c1a2fc50bd4a9982"
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dependencies:
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@@ -4754,82 +3847,89 @@ cosmiconfig@^6.0.0:
path-type "^4.0.0"
yaml "^1.7.2"
-cosmiconfig@^7.0.0, cosmiconfig@^7.0.1:
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+cosmiconfig@^8.1.3, cosmiconfig@^8.3.5:
+ version "8.3.6"
+ resolved "https://registry.yarnpkg.com/cosmiconfig/-/cosmiconfig-8.3.6.tgz#060a2b871d66dba6c8538ea1118ba1ac16f5fae3"
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dependencies:
- "@types/parse-json" "^4.0.0"
- import-fresh "^3.2.1"
- parse-json "^5.0.0"
+ import-fresh "^3.3.0"
+ js-yaml "^4.1.0"
+ parse-json "^5.2.0"
path-type "^4.0.0"
- yaml "^1.10.0"
+
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+ version "29.7.0"
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+ chalk "^4.0.0"
+ exit "^0.1.2"
+ graceful-fs "^4.2.9"
+ jest-config "^29.7.0"
+ jest-util "^29.7.0"
+ prompts "^2.0.1"
create-require@^1.1.0:
version "1.1.1"
- resolved "https://registry.npmjs.org/create-require/-/create-require-1.1.1.tgz"
+ resolved "https://registry.yarnpkg.com/create-require/-/create-require-1.1.1.tgz#c1d7e8f1e5f6cfc9ff65f9cd352d37348756c333"
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-
cross-spawn@^7.0.3:
version "7.0.3"
- resolved "https://registry.npmjs.org/cross-spawn/-/cross-spawn-7.0.3.tgz"
+ resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-7.0.3.tgz#f73a85b9d5d41d045551c177e2882d4ac85728a6"
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+crypto-random-string@^4.0.0:
+ version "4.0.0"
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+ dependencies:
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css-color-keywords@^1.0.0:
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- postcss-modules-extract-imports "^3.0.0"
- postcss-modules-local-by-default "^4.0.0"
- postcss-modules-scope "^3.0.0"
+ postcss "^8.4.33"
+ postcss-modules-extract-imports "^3.1.0"
+ postcss-modules-local-by-default "^4.0.5"
+ postcss-modules-scope "^3.2.0"
postcss-modules-values "^4.0.0"
postcss-value-parser "^4.2.0"
- semver "^7.3.5"
+ semver "^7.5.4"
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+css-minimizer-webpack-plugin@^5.0.1:
+ version "5.0.1"
+ resolved "https://registry.yarnpkg.com/css-minimizer-webpack-plugin/-/css-minimizer-webpack-plugin-5.0.1.tgz#33effe662edb1a0bf08ad633c32fa75d0f7ec565"
+ integrity sha512-3caImjKFQkS+ws1TGcFn0V1HyDJFq1Euy589JlD6/3rV2kj+w7r5G9WDMgSHvpvXHNZ2calVypZWuEDQd9wfLg==
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- postcss "^8.4.17"
- schema-utils "^4.0.0"
- serialize-javascript "^6.0.0"
- source-map "^0.6.1"
+ "@jridgewell/trace-mapping" "^0.3.18"
+ cssnano "^6.0.1"
+ jest-worker "^29.4.3"
+ postcss "^8.4.24"
+ schema-utils "^4.0.1"
+ serialize-javascript "^6.0.1"
css-select@^4.1.3:
version "4.3.0"
- resolved "https://registry.npmjs.org/css-select/-/css-select-4.3.0.tgz"
+ resolved "https://registry.yarnpkg.com/css-select/-/css-select-4.3.0.tgz#db7129b2846662fd8628cfc496abb2b59e41529b"
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dependencies:
boolbase "^1.0.0"
@@ -4840,7 +3940,7 @@ css-select@^4.1.3:
css-select@^5.1.0:
version "5.1.0"
- resolved "https://registry.npmjs.org/css-select/-/css-select-5.1.0.tgz"
+ resolved "https://registry.yarnpkg.com/css-select/-/css-select-5.1.0.tgz#b8ebd6554c3637ccc76688804ad3f6a6fdaea8a6"
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@@ -4851,10 +3951,10 @@ css-select@^5.1.0:
css-selector-parser@^1.0.0:
version "1.4.1"
- resolved "https://registry.npmjs.org/css-selector-parser/-/css-selector-parser-1.4.1.tgz"
+ resolved "https://registry.yarnpkg.com/css-selector-parser/-/css-selector-parser-1.4.1.tgz#03f9cb8a81c3e5ab2c51684557d5aaf6d2569759"
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+css-to-react-native@3.2.0:
version "3.2.0"
resolved "https://registry.yarnpkg.com/css-to-react-native/-/css-to-react-native-3.2.0.tgz#cdd8099f71024e149e4f6fe17a7d46ecd55f1e32"
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@@ -4863,166 +3963,192 @@ css-to-react-native@^3.2.0:
css-color-keywords "^1.0.0"
postcss-value-parser "^4.0.2"
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- source-map "^0.6.1"
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+ source-map-js "^1.0.1"
+
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+docusaurus-lunr-search@^3.5.0:
+ version "3.5.0"
+ resolved "https://registry.yarnpkg.com/docusaurus-lunr-search/-/docusaurus-lunr-search-3.5.0.tgz#52832d7bdd858ed093e98a743ee7bd4df596cfbf"
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dependencies:
autocomplete.js "^0.37.0"
- classnames "^2.2.6"
+ clsx "^1.2.1"
gauge "^3.0.0"
hast-util-select "^4.0.0"
hast-util-to-text "^2.0.0"
hogan.js "^3.0.2"
lunr "^2.3.8"
lunr-languages "^1.4.0"
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minimatch "^3.0.4"
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rehype-parse "^7.0.1"
to-vfile "^6.1.0"
unified "^9.0.0"
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version "0.1.4"
- resolved "https://registry.npmjs.org/docusaurus-plugin-image-zoom/-/docusaurus-plugin-image-zoom-0.1.4.tgz"
+ resolved "https://registry.yarnpkg.com/docusaurus-plugin-image-zoom/-/docusaurus-plugin-image-zoom-0.1.4.tgz#9b5fd06e3a79d84979265b7e74881a65bb3e1872"
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dependencies:
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dom-converter@^0.2.0:
version "0.2.0"
- resolved "https://registry.npmjs.org/dom-converter/-/dom-converter-0.2.0.tgz"
+ resolved "https://registry.yarnpkg.com/dom-converter/-/dom-converter-0.2.0.tgz#6721a9daee2e293682955b6afe416771627bb768"
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dependencies:
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dom-serializer@^1.0.1:
version "1.4.1"
- resolved "https://registry.npmjs.org/dom-serializer/-/dom-serializer-1.4.1.tgz"
+ resolved "https://registry.yarnpkg.com/dom-serializer/-/dom-serializer-1.4.1.tgz#de5d41b1aea290215dc45a6dae8adcf1d32e2d30"
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dom-serializer@^2.0.0:
version "2.0.0"
- resolved "https://registry.npmjs.org/dom-serializer/-/dom-serializer-2.0.0.tgz"
+ resolved "https://registry.yarnpkg.com/dom-serializer/-/dom-serializer-2.0.0.tgz#e41b802e1eedf9f6cae183ce5e622d789d7d8e53"
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version "2.3.0"
- resolved "https://registry.npmjs.org/domelementtype/-/domelementtype-2.3.0.tgz"
+ resolved "https://registry.yarnpkg.com/domelementtype/-/domelementtype-2.3.0.tgz#5c45e8e869952626331d7aab326d01daf65d589d"
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version "4.3.1"
- resolved "https://registry.npmjs.org/domhandler/-/domhandler-4.3.1.tgz"
+ resolved "https://registry.yarnpkg.com/domhandler/-/domhandler-4.3.1.tgz#8d792033416f59d68bc03a5aa7b018c1ca89279c"
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+domhandler@^5.0.2, domhandler@^5.0.3:
version "5.0.3"
- resolved "https://registry.npmjs.org/domhandler/-/domhandler-5.0.3.tgz"
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domutils@^2.5.2, domutils@^2.8.0:
version "2.8.0"
- resolved "https://registry.npmjs.org/domutils/-/domutils-2.8.0.tgz"
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dependencies:
dom-serializer "^2.0.0"
domelementtype "^2.3.0"
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dot-case@^3.0.4:
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- resolved "https://registry.npmjs.org/dot-case/-/dot-case-3.0.4.tgz"
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execa@^5.0.0:
version "5.1.1"
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array-flatten "1.1.1"
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content-disposition "0.5.4"
content-type "~1.0.4"
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cookie-signature "1.0.6"
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version "2.0.1"
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dependencies:
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+fast-glob@^3.2.11, fast-glob@^3.2.9, fast-glob@^3.3.0:
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find-up@^4.0.0, find-up@^4.1.0:
version "4.1.0"
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find-up@^5.0.0:
version "5.0.0"
- resolved "https://registry.npmjs.org/find-up/-/find-up-5.0.0.tgz"
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semver "^7.3.2"
tapable "^1.0.0"
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+ version "11.2.0"
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jsonfile "^6.0.1"
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fs-extra@^9.0.0:
version "9.1.0"
- resolved "https://registry.npmjs.org/fs-extra/-/fs-extra-9.1.0.tgz"
+ resolved "https://registry.yarnpkg.com/fs-extra/-/fs-extra-9.1.0.tgz#5954460c764a8da2094ba3554bf839e6b9a7c86d"
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- resolved "https://registry.npmjs.org/fs.realpath/-/fs.realpath-1.0.0.tgz"
+ resolved "https://registry.yarnpkg.com/fs.realpath/-/fs.realpath-1.0.0.tgz#1504ad2523158caa40db4a2787cb01411994ea4f"
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gauge@^3.0.0:
version "3.0.2"
- resolved "https://registry.npmjs.org/gauge/-/gauge-3.0.2.tgz"
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glob-parent@^6.0.1:
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glob-to-regexp@^0.4.1:
version "0.4.1"
- resolved "https://registry.npmjs.org/glob-to-regexp/-/glob-to-regexp-0.4.1.tgz"
+ resolved "https://registry.yarnpkg.com/glob-to-regexp/-/glob-to-regexp-0.4.1.tgz#c75297087c851b9a578bd217dd59a92f59fe546e"
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version "7.2.3"
- resolved "https://registry.npmjs.org/glob/-/glob-7.2.3.tgz"
+ resolved "https://registry.yarnpkg.com/glob/-/glob-7.2.3.tgz#b8df0fb802bbfa8e89bd1d938b4e16578ed44f2b"
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dependencies:
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ini "2.0.0"
global-modules@^2.0.0:
version "2.0.0"
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global-prefix@^3.0.0:
version "3.0.0"
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dependencies:
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@@ -5922,12 +5059,12 @@ global-prefix@^3.0.0:
globals@^11.1.0:
version "11.12.0"
- resolved "https://registry.npmjs.org/globals/-/globals-11.12.0.tgz"
+ resolved "https://registry.yarnpkg.com/globals/-/globals-11.12.0.tgz#ab8795338868a0babd8525758018c2a7eb95c42e"
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version "11.1.0"
- resolved "https://registry.npmjs.org/globby/-/globby-11.1.0.tgz"
+ resolved "https://registry.yarnpkg.com/globby/-/globby-11.1.0.tgz#bd4be98bb042f83d796f7e3811991fbe82a0d34b"
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slash "^3.0.0"
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+ version "13.2.2"
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dir-glob "^3.0.1"
- fast-glob "^3.2.11"
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+ fast-glob "^3.3.0"
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+ version "4.2.11"
+ resolved "https://registry.yarnpkg.com/graceful-fs/-/graceful-fs-4.2.11.tgz#4183e4e8bf08bb6e05bbb2f7d2e0c8f712ca40e3"
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+
gray-matter@^4.0.3:
version "4.0.3"
- resolved "https://registry.npmjs.org/gray-matter/-/gray-matter-4.0.3.tgz"
+ resolved "https://registry.yarnpkg.com/gray-matter/-/gray-matter-4.0.3.tgz#e893c064825de73ea1f5f7d88c7a9f7274288798"
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-
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version "6.0.0"
- resolved "https://registry.npmjs.org/gzip-size/-/gzip-size-6.0.0.tgz"
+ resolved "https://registry.yarnpkg.com/gzip-size/-/gzip-size-6.0.0.tgz#065367fd50c239c0671cbcbad5be3e2eeb10e462"
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handle-thing@^2.0.0:
version "2.0.1"
- resolved "https://registry.npmjs.org/handle-thing/-/handle-thing-2.0.1.tgz"
+ resolved "https://registry.yarnpkg.com/handle-thing/-/handle-thing-2.0.1.tgz#857f79ce359580c340d43081cc648970d0bb234e"
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has-flag@^3.0.0:
version "3.0.0"
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+ resolved "https://registry.yarnpkg.com/has-flag/-/has-flag-3.0.0.tgz#b5d454dc2199ae225699f3467e5a07f3b955bafd"
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has-flag@^4.0.0:
version "4.0.0"
- resolved "https://registry.npmjs.org/has-flag/-/has-flag-4.0.0.tgz"
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- space-separated-tokens "^1.0.0"
- style-to-object "^0.3.0"
- unist-util-is "^4.0.0"
- web-namespaces "^1.0.0"
+ function-bind "^1.1.2"
hast-util-from-parse5@^6.0.0:
version "6.0.1"
- resolved "https://registry.npmjs.org/hast-util-from-parse5/-/hast-util-from-parse5-6.0.1.tgz"
+ resolved "https://registry.yarnpkg.com/hast-util-from-parse5/-/hast-util-from-parse5-6.0.1.tgz#554e34abdeea25ac76f5bd950a1f0180e0b3bc2a"
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dependencies:
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@@ -6087,40 +5197,64 @@ hast-util-from-parse5@^6.0.0:
vfile-location "^3.2.0"
web-namespaces "^1.0.0"
+hast-util-from-parse5@^8.0.0:
+ version "8.0.1"
+ resolved "https://registry.yarnpkg.com/hast-util-from-parse5/-/hast-util-from-parse5-8.0.1.tgz#654a5676a41211e14ee80d1b1758c399a0327651"
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+ dependencies:
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+ devlop "^1.0.0"
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+ property-information "^6.0.0"
+ vfile "^6.0.0"
+ vfile-location "^5.0.0"
+ web-namespaces "^2.0.0"
+
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version "1.0.4"
- resolved "https://registry.npmjs.org/hast-util-has-property/-/hast-util-has-property-1.0.4.tgz"
+ resolved "https://registry.yarnpkg.com/hast-util-has-property/-/hast-util-has-property-1.0.4.tgz#9f137565fad6082524b382c1e7d7d33ca5059f36"
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hast-util-is-element@^1.0.0:
version "1.1.0"
- resolved "https://registry.npmjs.org/hast-util-is-element/-/hast-util-is-element-1.1.0.tgz"
+ resolved "https://registry.yarnpkg.com/hast-util-is-element/-/hast-util-is-element-1.1.0.tgz#3b3ed5159a2707c6137b48637fbfe068e175a425"
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hast-util-parse-selector@^2.0.0:
version "2.2.5"
- resolved "https://registry.npmjs.org/hast-util-parse-selector/-/hast-util-parse-selector-2.2.5.tgz"
+ resolved "https://registry.yarnpkg.com/hast-util-parse-selector/-/hast-util-parse-selector-2.2.5.tgz#d57c23f4da16ae3c63b3b6ca4616683313499c3a"
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- web-namespaces "^1.0.0"
- xtend "^4.0.0"
- zwitch "^1.0.0"
+hast-util-parse-selector@^4.0.0:
+ version "4.0.0"
+ resolved "https://registry.yarnpkg.com/hast-util-parse-selector/-/hast-util-parse-selector-4.0.0.tgz#352879fa86e25616036037dd8931fb5f34cb4a27"
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+ dependencies:
+ "@types/hast" "^3.0.0"
+
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+ version "9.0.4"
+ resolved "https://registry.yarnpkg.com/hast-util-raw/-/hast-util-raw-9.0.4.tgz#2da03e37c46eb1a6f1391f02f9b84ae65818f7ed"
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+ dependencies:
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+ "@types/unist" "^3.0.0"
+ "@ungap/structured-clone" "^1.0.0"
+ hast-util-from-parse5 "^8.0.0"
+ hast-util-to-parse5 "^8.0.0"
+ html-void-elements "^3.0.0"
+ mdast-util-to-hast "^13.0.0"
+ parse5 "^7.0.0"
+ unist-util-position "^5.0.0"
+ unist-util-visit "^5.0.0"
+ vfile "^6.0.0"
+ web-namespaces "^2.0.0"
+ zwitch "^2.0.0"
hast-util-select@^4.0.0:
version "4.0.2"
- resolved "https://registry.npmjs.org/hast-util-select/-/hast-util-select-4.0.2.tgz"
+ resolved "https://registry.yarnpkg.com/hast-util-select/-/hast-util-select-4.0.2.tgz#ae3ef2860e02cda2ad3a2e72b47c1f5e8f44e9e7"
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dependencies:
bcp-47-match "^1.0.0"
@@ -6138,25 +5272,70 @@ hast-util-select@^4.0.0:
unist-util-visit "^2.0.0"
zwitch "^1.0.0"
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+hast-util-to-estree@^3.0.0:
+ version "3.1.0"
+ resolved "https://registry.yarnpkg.com/hast-util-to-estree/-/hast-util-to-estree-3.1.0.tgz#f2afe5e869ddf0cf690c75f9fc699f3180b51b19"
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+ dependencies:
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+
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- property-information "^5.0.0"
- web-namespaces "^1.0.0"
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+ space-separated-tokens "^2.0.0"
+ web-namespaces "^2.0.0"
+ zwitch "^2.0.0"
hast-util-to-string@^1.0.0:
version "1.0.4"
- resolved "https://registry.npmjs.org/hast-util-to-string/-/hast-util-to-string-1.0.4.tgz"
+ resolved "https://registry.yarnpkg.com/hast-util-to-string/-/hast-util-to-string-1.0.4.tgz#9b24c114866bdb9478927d7e9c36a485ac728378"
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hast-util-to-text@^2.0.0:
version "2.0.1"
- resolved "https://registry.npmjs.org/hast-util-to-text/-/hast-util-to-text-2.0.1.tgz"
+ resolved "https://registry.yarnpkg.com/hast-util-to-text/-/hast-util-to-text-2.0.1.tgz#04f2e065642a0edb08341976084aa217624a0f8b"
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hast-util-whitespace@^1.0.0:
version "1.0.4"
- resolved "https://registry.npmjs.org/hast-util-whitespace/-/hast-util-whitespace-1.0.4.tgz"
+ resolved "https://registry.yarnpkg.com/hast-util-whitespace/-/hast-util-whitespace-1.0.4.tgz#e4fe77c4a9ae1cb2e6c25e02df0043d0164f6e41"
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+hast-util-whitespace@^3.0.0:
+ version "3.0.0"
+ resolved "https://registry.yarnpkg.com/hast-util-whitespace/-/hast-util-whitespace-3.0.0.tgz#7778ed9d3c92dd9e8c5c8f648a49c21fc51cb621"
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+ dependencies:
+ "@types/hast" "^3.0.0"
+
hastscript@^6.0.0:
version "6.0.0"
- resolved "https://registry.npmjs.org/hastscript/-/hastscript-6.0.0.tgz"
+ resolved "https://registry.yarnpkg.com/hastscript/-/hastscript-6.0.0.tgz#e8768d7eac56c3fdeac8a92830d58e811e5bf640"
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@@ -6179,19 +5365,30 @@ hastscript@^6.0.0:
property-information "^5.0.0"
space-separated-tokens "^1.0.0"
+hastscript@^8.0.0:
+ version "8.0.0"
+ resolved "https://registry.yarnpkg.com/hastscript/-/hastscript-8.0.0.tgz#4ef795ec8dee867101b9f23cc830d4baf4fd781a"
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+ dependencies:
+ "@types/hast" "^3.0.0"
+ comma-separated-tokens "^2.0.0"
+ hast-util-parse-selector "^4.0.0"
+ property-information "^6.0.0"
+ space-separated-tokens "^2.0.0"
+
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version "1.2.0"
- resolved "https://registry.npmjs.org/he/-/he-1.2.0.tgz"
+ resolved "https://registry.yarnpkg.com/he/-/he-1.2.0.tgz#84ae65fa7eafb165fddb61566ae14baf05664f0f"
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+ version "6.2.0"
+ resolved "https://registry.yarnpkg.com/helmet/-/helmet-6.2.0.tgz#c29d62014be4c70b8ef092c9c5e54c8c26b8e16e"
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history@^4.9.0:
version "4.10.1"
- resolved "https://registry.npmjs.org/history/-/history-4.10.1.tgz"
+ resolved "https://registry.yarnpkg.com/history/-/history-4.10.1.tgz#33371a65e3a83b267434e2b3f3b1b4c58aad4cf3"
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hogan.js@^3.0.2:
version "3.0.2"
- resolved "https://registry.npmjs.org/hogan.js/-/hogan.js-3.0.2.tgz"
+ resolved "https://registry.yarnpkg.com/hogan.js/-/hogan.js-3.0.2.tgz#4cd9e1abd4294146e7679e41d7898732b02c7bfd"
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dependencies:
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hoist-non-react-statics@^3.1.0:
version "3.3.2"
- resolved "https://registry.npmjs.org/hoist-non-react-statics/-/hoist-non-react-statics-3.3.2.tgz"
+ resolved "https://registry.yarnpkg.com/hoist-non-react-statics/-/hoist-non-react-statics-3.3.2.tgz#ece0acaf71d62c2969c2ec59feff42a4b1a85b45"
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dependencies:
react-is "^16.7.0"
hpack.js@^2.1.6:
version "2.1.6"
- resolved "https://registry.npmjs.org/hpack.js/-/hpack.js-2.1.6.tgz"
+ resolved "https://registry.yarnpkg.com/hpack.js/-/hpack.js-2.1.6.tgz#87774c0949e513f42e84575b3c45681fade2a0b2"
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dependencies:
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wbuf "^1.1.0"
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+ resolved "https://registry.yarnpkg.com/html-entities/-/html-entities-2.5.2.tgz#201a3cf95d3a15be7099521620d19dfb4f65359f"
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-html-escaper@^2.0.0:
+html-escaper@^2.0.0, html-escaper@^2.0.2:
version "2.0.2"
- resolved "https://registry.npmjs.org/html-escaper/-/html-escaper-2.0.2.tgz"
+ resolved "https://registry.yarnpkg.com/html-escaper/-/html-escaper-2.0.2.tgz#dfd60027da36a36dfcbe236262c00a5822681453"
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-html-minifier-terser@^6.0.2, html-minifier-terser@^6.1.0:
+html-minifier-terser@^6.0.2:
version "6.1.0"
- resolved "https://registry.npmjs.org/html-minifier-terser/-/html-minifier-terser-6.1.0.tgz"
+ resolved "https://registry.yarnpkg.com/html-minifier-terser/-/html-minifier-terser-6.1.0.tgz#bfc818934cc07918f6b3669f5774ecdfd48f32ab"
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dependencies:
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relateurl "^0.2.7"
terser "^5.10.0"
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+html-minifier-terser@^7.2.0:
+ version "7.2.0"
+ resolved "https://registry.yarnpkg.com/html-minifier-terser/-/html-minifier-terser-7.2.0.tgz#18752e23a2f0ed4b0f550f217bb41693e975b942"
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+ dependencies:
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+ clean-css "~5.3.2"
+ commander "^10.0.0"
+ entities "^4.4.0"
+ param-case "^3.0.4"
+ relateurl "^0.2.7"
+ terser "^5.15.1"
+
+html-tags@^3.3.1:
+ version "3.3.1"
+ resolved "https://registry.yarnpkg.com/html-tags/-/html-tags-3.3.1.tgz#a04026a18c882e4bba8a01a3d39cfe465d40b5ce"
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+html-void-elements@^3.0.0:
+ version "3.0.0"
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+html-webpack-plugin@^5.5.3:
+ version "5.6.0"
+ resolved "https://registry.yarnpkg.com/html-webpack-plugin/-/html-webpack-plugin-5.6.0.tgz#50a8fa6709245608cb00e811eacecb8e0d7b7ea0"
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html-minifier-terser "^6.0.2"
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htmlparser2@^6.1.0:
version "6.1.0"
- resolved "https://registry.npmjs.org/htmlparser2/-/htmlparser2-6.1.0.tgz"
+ resolved "https://registry.yarnpkg.com/htmlparser2/-/htmlparser2-6.1.0.tgz#c4d762b6c3371a05dbe65e94ae43a9f845fb8fb7"
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entities "^2.0.0"
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- resolved "https://registry.npmjs.org/htmlparser2/-/htmlparser2-8.0.1.tgz"
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+ version "8.0.2"
+ resolved "https://registry.yarnpkg.com/htmlparser2/-/htmlparser2-8.0.2.tgz#f002151705b383e62433b5cf466f5b716edaec21"
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dependencies:
domelementtype "^2.3.0"
- domhandler "^5.0.2"
+ domhandler "^5.0.3"
domutils "^3.0.1"
- entities "^4.3.0"
+ entities "^4.4.0"
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+http-cache-semantics@^4.1.1:
+ version "4.1.1"
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http-deceiver@^1.2.7:
version "1.2.7"
- resolved "https://registry.npmjs.org/http-deceiver/-/http-deceiver-1.2.7.tgz"
+ resolved "https://registry.yarnpkg.com/http-deceiver/-/http-deceiver-1.2.7.tgz#fa7168944ab9a519d337cb0bec7284dc3e723d87"
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http-errors@2.0.0:
version "2.0.0"
- resolved "https://registry.npmjs.org/http-errors/-/http-errors-2.0.0.tgz"
+ resolved "https://registry.yarnpkg.com/http-errors/-/http-errors-2.0.0.tgz#b7774a1486ef73cf7667ac9ae0858c012c57b9d3"
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dependencies:
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@@ -6313,7 +5523,7 @@ http-errors@2.0.0:
http-errors@~1.6.2:
version "1.6.3"
- resolved "https://registry.npmjs.org/http-errors/-/http-errors-1.6.3.tgz"
+ resolved "https://registry.yarnpkg.com/http-errors/-/http-errors-1.6.3.tgz#8b55680bb4be283a0b5bf4ea2e38580be1d9320d"
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dependencies:
depd "~1.1.2"
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http-parser-js@>=0.5.1:
version "0.5.8"
- resolved "https://registry.npmjs.org/http-parser-js/-/http-parser-js-0.5.8.tgz"
+ resolved "https://registry.yarnpkg.com/http-parser-js/-/http-parser-js-0.5.8.tgz#af23090d9ac4e24573de6f6aecc9d84a48bf20e3"
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- dependencies:
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- agent-base "6"
- debug "4"
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http-proxy-middleware@^2.0.3:
version "2.0.6"
- resolved "https://registry.npmjs.org/http-proxy-middleware/-/http-proxy-middleware-2.0.6.tgz"
+ resolved "https://registry.yarnpkg.com/http-proxy-middleware/-/http-proxy-middleware-2.0.6.tgz#e1a4dd6979572c7ab5a4e4b55095d1f32a74963f"
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dependencies:
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http-proxy@^1.18.1:
version "1.18.1"
- resolved "https://registry.npmjs.org/http-proxy/-/http-proxy-1.18.1.tgz"
+ resolved "https://registry.yarnpkg.com/http-proxy/-/http-proxy-1.18.1.tgz#401541f0534884bbf95260334e72f88ee3976549"
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follow-redirects "^1.0.0"
requires-port "^1.0.0"
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semver "^6.3.0"
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supports-color "^7.1.0"
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parse-numeric-range@^1.3.0:
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- resolved "https://registry.npmjs.org/parse-numeric-range/-/parse-numeric-range-1.3.0.tgz"
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version "7.0.0"
- resolved "https://registry.npmjs.org/parse5-htmlparser2-tree-adapter/-/parse5-htmlparser2-tree-adapter-7.0.0.tgz"
+ resolved "https://registry.yarnpkg.com/parse5-htmlparser2-tree-adapter/-/parse5-htmlparser2-tree-adapter-7.0.0.tgz#23c2cc233bcf09bb7beba8b8a69d46b08c62c2f1"
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parseurl@~1.3.2, parseurl@~1.3.3:
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pascal-case@^3.1.2:
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-
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version "12.0.1"
- resolved "https://registry.npmjs.org/react-dev-utils/-/react-dev-utils-12.0.1.tgz"
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react-error-overlay@^6.0.11:
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version "16.13.1"
- resolved "https://registry.npmjs.org/react-is/-/react-is-16.13.1.tgz"
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- react-textarea-autosize "^8.3.2"
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+ resolved "https://registry.yarnpkg.com/react-router-config/-/react-router-config-5.1.1.tgz#0f4263d1a80c6b2dc7b9c1902c9526478194a988"
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+react-router-dom@^5.3.4:
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- resolved "https://registry.npmjs.org/react-router-dom/-/react-router-dom-5.3.4.tgz"
+ resolved "https://registry.yarnpkg.com/react-router-dom/-/react-router-dom-5.3.4.tgz#2ed62ffd88cae6db134445f4a0c0ae8b91d2e5e6"
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tiny-invariant "^1.0.2"
tiny-warning "^1.0.0"
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+react-router@5.3.4, react-router@^5.3.4:
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- resolved "https://registry.npmjs.org/react-router/-/react-router-5.3.4.tgz"
+ resolved "https://registry.yarnpkg.com/react-router/-/react-router-5.3.4.tgz#8ca252d70fcc37841e31473c7a151cf777887bb5"
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tiny-warning "^1.0.0"
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resolved "https://registry.yarnpkg.com/regexpu-core/-/regexpu-core-5.3.2.tgz#11a2b06884f3527aec3e93dbbf4a3b958a95546b"
@@ -8812,105 +8544,133 @@ regexpu-core@^5.3.1:
unicode-match-property-ecmascript "^2.0.0"
unicode-match-property-value-ecmascript "^2.1.0"
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dependencies:
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regjsparser@^0.9.1:
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rehype-parse@^7.0.1:
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+
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renderkid@^3.0.0:
version "3.0.0"
- resolved "https://registry.npmjs.org/renderkid/-/renderkid-3.0.0.tgz"
+ resolved "https://registry.yarnpkg.com/renderkid/-/renderkid-3.0.0.tgz#5fd823e4d6951d37358ecc9a58b1f06836b6268a"
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dependencies:
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@@ -8919,196 +8679,181 @@ renderkid@^3.0.0:
lodash "^4.17.21"
strip-ansi "^6.0.1"
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+repeat-string@^1.0.0:
version "1.6.1"
- resolved "https://registry.npmjs.org/repeat-string/-/repeat-string-1.6.1.tgz"
+ resolved "https://registry.yarnpkg.com/repeat-string/-/repeat-string-1.6.1.tgz#8dcae470e1c88abc2d600fff4a776286da75e637"
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require-directory@^2.1.1:
version "2.1.1"
- resolved "https://registry.npmjs.org/require-directory/-/require-directory-2.1.1.tgz"
+ resolved "https://registry.yarnpkg.com/require-directory/-/require-directory-2.1.1.tgz#8c64ad5fd30dab1c976e2344ffe7f792a6a6df42"
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require-from-string@^2.0.2:
version "2.0.2"
- resolved "https://registry.npmjs.org/require-from-string/-/require-from-string-2.0.2.tgz"
+ resolved "https://registry.yarnpkg.com/require-from-string/-/require-from-string-2.0.2.tgz#89a7fdd938261267318eafe14f9c32e598c36909"
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"require-like@>= 0.1.1":
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- resolved "https://registry.npmjs.org/require-like/-/require-like-0.1.2.tgz"
+ resolved "https://registry.yarnpkg.com/require-like/-/require-like-0.1.2.tgz#ad6f30c13becd797010c468afa775c0c0a6b47fa"
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requires-port@^1.0.0:
version "1.0.0"
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version "3.0.0"
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dependencies:
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ajv-formats "^2.1.1"
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+ ajv-keywords "^5.1.0"
+
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section-matter@^1.0.0:
version "1.0.0"
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version "2.0.0"
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dependencies:
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send@0.18.0:
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content-disposition "0.5.2"
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mime-types "2.1.18"
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path-is-inside "1.0.2"
path-to-regexp "2.2.1"
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serve-index@^1.9.1:
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source-map-support@0.5.13:
version "0.5.13"
- resolved "https://registry.npmjs.org/source-map-support/-/source-map-support-0.5.13.tgz"
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source-map-support@~0.5.20:
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- resolved "https://registry.npmjs.org/source-map-support/-/source-map-support-0.5.21.tgz"
+ resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.21.tgz#04fe7c7f9e1ed2d662233c28cb2b35b9f63f6e4f"
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spdy-transport@^3.0.0:
version "3.0.0"
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spdy@^4.0.2:
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- resolved "https://registry.npmjs.org/spdy/-/spdy-4.0.2.tgz"
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- resolved "https://registry.npmjs.org/sprintf-js/-/sprintf-js-1.0.3.tgz"
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dependencies:
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is-fullwidth-code-point "^3.0.0"
strip-ansi "^6.0.1"
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string_decoder@~1.1.1:
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+stringify-entities@^4.0.0:
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source-map-support "~0.5.20"
test-exclude@^6.0.0:
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tmpl@1.0.5:
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version "5.0.1"
- resolved "https://registry.npmjs.org/to-regex-range/-/to-regex-range-5.0.1.tgz"
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to-vfile@^6.1.0:
version "6.1.0"
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- lodash.memoize "4.x"
- make-error "1.x"
- semver "7.x"
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+ lodash.memoize "^4.1.2"
+ make-error "^1.3.6"
+ semver "^7.6.3"
+ yargs-parser "^21.1.1"
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+ version "10.9.2"
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- resolved "https://registry.npmjs.org/type-fest/-/type-fest-0.21.3.tgz"
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version "2.19.0"
- resolved "https://registry.npmjs.org/type-fest/-/type-fest-2.19.0.tgz"
+ resolved "https://registry.yarnpkg.com/type-fest/-/type-fest-2.19.0.tgz#88068015bb33036a598b952e55e9311a60fd3a9b"
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type-is@~1.6.18:
version "1.6.18"
- resolved "https://registry.npmjs.org/type-is/-/type-is-1.6.18.tgz"
+ resolved "https://registry.yarnpkg.com/type-is/-/type-is-1.6.18.tgz#4e552cd05df09467dcbc4ef739de89f2cf37c131"
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dependencies:
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version "3.1.5"
- resolved "https://registry.npmjs.org/typedarray-to-buffer/-/typedarray-to-buffer-3.1.5.tgz"
+ resolved "https://registry.yarnpkg.com/typedarray-to-buffer/-/typedarray-to-buffer-3.1.5.tgz#a97ee7a9ff42691b9f783ff1bc5112fe3fca9080"
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schema-utils "^4.0.0"
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"@types/sockjs" "^0.3.33"
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bonjour-service "^1.0.11"
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html-entities "^2.3.2"
http-proxy-middleware "^2.0.3"
ipaddr.js "^2.0.1"
+ launch-editor "^2.6.0"
open "^8.0.9"
p-retry "^4.5.0"
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serve-index "^1.9.1"
sockjs "^0.3.24"
spdy "^4.0.2"
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loader-runner "^4.2.0"
mime-types "^2.1.27"
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tapable "^2.1.1"
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+ terser-webpack-plugin "^5.3.10"
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webpack-sources "^3.2.3"
webpackbar@^5.0.2:
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- resolved "https://registry.npmjs.org/webpackbar/-/webpackbar-5.0.2.tgz"
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version "0.7.4"
- resolved "https://registry.npmjs.org/websocket-driver/-/websocket-driver-0.7.4.tgz"
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websocket-extensions@>=0.1.1:
version "0.1.4"
- resolved "https://registry.npmjs.org/websocket-extensions/-/websocket-extensions-0.1.4.tgz"
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version "1.3.1"
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which@^2.0.1:
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wide-align@^1.1.2:
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wrap-ansi@^7.0.0:
version "7.0.0"
- resolved "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz"
+ resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43"
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dependencies:
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string-width "^4.1.0"
strip-ansi "^6.0.0"
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+wrap-ansi@^8.0.1, wrap-ansi@^8.1.0:
+ version "8.1.0"
+ resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-8.1.0.tgz#56dc22368ee570face1b49819975d9b9a5ead214"
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dependencies:
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string-width "^5.0.1"
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wrappy@1:
version "1.0.2"
- resolved "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz"
+ resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
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+write-file-atomic@^3.0.3:
version "3.0.3"
- resolved "https://registry.npmjs.org/write-file-atomic/-/write-file-atomic-3.0.3.tgz"
+ resolved "https://registry.yarnpkg.com/write-file-atomic/-/write-file-atomic-3.0.3.tgz#56bd5c5a5c70481cd19c571bd39ab965a5de56e8"
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dependencies:
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write-file-atomic@^4.0.2:
version "4.0.2"
- resolved "https://registry.npmjs.org/write-file-atomic/-/write-file-atomic-4.0.2.tgz"
+ resolved "https://registry.yarnpkg.com/write-file-atomic/-/write-file-atomic-4.0.2.tgz#a9df01ae5b77858a027fd2e80768ee433555fcfd"
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+yargs-parser@^21.1.1:
version "21.1.1"
- resolved "https://registry.npmjs.org/yargs-parser/-/yargs-parser-21.1.1.tgz"
+ resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-21.1.1.tgz#9096bceebf990d21bb31fa9516e0ede294a77d35"
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dependencies:
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escalade "^3.1.1"
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yn@3.1.1:
version "3.1.1"
- resolved "https://registry.npmjs.org/yn/-/yn-3.1.1.tgz"
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yocto-queue@^0.1.0:
version "0.1.0"
- resolved "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz"
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+yocto-queue@^1.0.0:
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+ version "2.0.4"
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