Fix malformed links

content/guides/_index.md

@@ -20,34 +20,34 @@ Weights & Biases (W&B) is the AI developer platform, with tools for training mod
 
 {{< img src="/images/general/architecture.png" alt="" >}}
 
-W&B consists of three major components: [Models](/guides/models.md), [Weave](https://wandb.github.io/weave/), and [Core](/guides/core.md):
+W&B consists of three major components: [Models](/guides/models/), [Weave](https://wandb.github.io/weave/), and [Core](/guides/core/):
 
-**[W&B Models](/guides/models.md)** is a set of lightweight, interoperable tools for machine learning practitioners training and fine-tuning models.
-- [Experiments](/guides/track/intro.md): Machine learning experiment tracking
-- [Sweeps](/guides/sweeps/intro.md): Hyperparameter tuning and model optimization
-- [Registry](/guides/registry/intro.md): Publish and share your ML models and datasets
+**[W&B Models](/guides/models/)** is a set of lightweight, interoperable tools for machine learning practitioners training and fine-tuning models.
+- [Experiments](/guides/track/): Machine learning experiment tracking
+- [Sweeps](/guides/sweeps/): Hyperparameter tuning and model optimization
+- [Registry](/guides/registry/): Publish and share your ML models and datasets
 
 **[W&B Weave](https://wandb.github.io/weave/)** is a lightweight toolkit for tracking and evaluating LLM applications.
 
-**[W&B Core](/guides/core.md)** is set of powerful building blocks for tracking and visualizing data and models, and communicating results.
-- [Artifacts](/guides/artifacts/intro.md): Version assets and track lineage
-- [Tables](/guides/tables/intro.md): Visualize and query tabular data
-- [Reports](/guides/reports/intro.md): Document and collaborate on your discoveries
+**[W&B Core](/guides/core/)** is set of powerful building blocks for tracking and visualizing data and models, and communicating results.
+- [Artifacts](/guides/artifacts/): Version assets and track lineage
+- [Tables](/guides/tables/): Visualize and query tabular data
+- [Reports](/guides/reports/): Document and collaborate on your discoveries
 <!-- - [Weave](/guides/app/features/panels/weave) Query and create visualizations of your data -->
 
 ## How does W&B work?
 Read the following sections in this order if you are a first-time user of W&B and you are interested in training, tracking, and visualizing machine learning models and experiments:
 
-1. Learn about [runs](./runs/intro.md), W&B's basic unit of computation.
-2. Create and track machine learning experiments with [Experiments](./track/intro.md).
-3. Discover W&B's flexible and lightweight building block for dataset and model versioning with [Artifacts](./artifacts/intro.md).
-4. Automate hyperparameter search and explore the space of possible models with [Sweeps](./sweeps/intro.md).
-5. Manage the model lifecycle from training to production with [Model Registry](./model_registry/intro.md).
-6. Visualize predictions across model versions with our [Data Visualization](./tables/intro.md) guide.
-7. Organize runs, embed and automate visualizations, describe your findings, and share updates with collaborators with [Reports](./reports/intro.md).
+1. Learn about [runs](./runs/), W&B's basic unit of computation.
+2. Create and track machine learning experiments with [Experiments](./track/).
+3. Discover W&B's flexible and lightweight building block for dataset and model versioning with [Artifacts](./artifacts/).
+4. Automate hyperparameter search and explore the space of possible models with [Sweeps](./sweeps/).
+5. Manage the model lifecycle from training to production with [Model Registry](./model_registry/).
+6. Visualize predictions across model versions with our [Data Visualization](./tables/) guide.
+7. Organize runs, embed and automate visualizations, describe your findings, and share updates with collaborators with [Reports](./reports/).
 
 <iframe width="100%" height="330" src="https://www.youtube.com/embed/tHAFujRhZLA" title="Weights &amp; Biases End-to-End Demo" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
 
 ## Are you a first-time user of W&B?
 
-Try the [quickstart](../quickstart.md) to learn how to install W&B and how to add W&B to your code.
+Try the [quickstart](../quickstart/) to learn how to install W&B and how to add W&B to your code.

content/guides/core/_index.md

@@ -7,13 +7,13 @@ weight: 5
 no_list: true
 ---
 
-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/_index.md). 
+W&B Core is the foundational framework supporting [W&B Models](./models/) and [W&B Weave](./weave_platform/), and is itself supported by the [W&B Platform](./hosting/). 
 
 {{< img src="/images/general/core.png" alt="" >}}
 
 W&B Core provides capabilities across the entire ML lifecycle. With W&B Core, you can:
 
-- [Version and manage ML](./artifacts/_index.md) pipelines with full lineage tracing for easy auditing and reproducibility.
-- Explore and evaluate data and metrics using [interactive, configurable visualizations](./tables/_index.md).
-- [Document and share](./reports/_index_.md) insights across the entire organization by generating live reports in digestible, visual formats that are easily understood by non-technical stakeholders.
-- [Query and create visualizations of your data](../guides/app/features/panels/query-panel/_index.md) that serve your custom needs.
+- [Version and manage ML](./artifacts/) pipelines with full lineage tracing for easy auditing and reproducibility.
+- Explore and evaluate data and metrics using [interactive, configurable visualizations](./tables/).
+- [Document and share](./reports/) insights across the entire organization by generating live reports in digestible, visual formats that are easily understood by non-technical stakeholders.
+- [Query and create visualizations of your data](../guides/app/features/panels/query-panel/) that serve your custom needs.

content/guides/core/artifacts/_index.md

@@ -15,18 +15,18 @@ weight: 1
 
 {{< cta-button productLink="https://wandb.ai/wandb/arttest/artifacts/model/iv3_trained/5334ab69740f9dda4fed/lineage" colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/wandb-artifacts/Artifact_fundamentals.ipynb" >}}
 
-Use W&B Artifacts to track and version data as the inputs and outputs of your [W&B Runs](../runs/intro.md). For example, a model training run might take in a dataset as input and produce a trained model as output. You can log hyperparameters, metadatra, and metrics to a run, and you can use an artifact to log, track, and version the dataset used to train the model as input and another artifact for the resulting model checkpoints as output.
+Use W&B Artifacts to track and version data as the inputs and outputs of your [W&B Runs](../runs/). For example, a model training run might take in a dataset as input and produce a trained model as output. You can log hyperparameters, metadatra, and metrics to a run, and you can use an artifact to log, track, and version the dataset used to train the model as input and another artifact for the resulting model checkpoints as output.
 
 ## Use cases
-You can use artifacts throughout your entire ML workflow as inputs and outputs of [runs](../runs/intro.md). You can use datasets, models, or even other artifacts as inputs for processing.
+You can use artifacts throughout your entire ML workflow as inputs and outputs of [runs](../runs/). You can use datasets, models, or even other artifacts as inputs for processing.
 
 {{< img src="/images/artifacts/artifacts_landing_page2.png" >}}
 
 | Use Case               | Input                       | Output                       |
 |------------------------|-----------------------------|------------------------------|
 | Model Training         | Dataset (training and validation data)     | Trained Model                |
 | Dataset Pre-Processing | Dataset (raw data)          | Dataset (pre-processed data) |
-| Model Evaluation       | Model + Dataset (test data) | [W&B Table](../tables/intro.md)                        |
+| Model Evaluation       | Model + Dataset (test data) | [W&B Table](../tables/)                        |
 | Model Optimization     | Model                       | Optimized Model              |
 
 
@@ -37,8 +37,8 @@ The proceeding code snippets are meant to be run in order.
 ## Create an artifact
 
 Create an artifact with four lines of code:
-1. Create a [W&B run](../runs/intro.md).
-2. Create an artifact object with the [`wandb.Artifact`](../../ref/python/artifact.md) API.
+1. Create a [W&B run](../runs/).
+2. Create an artifact object with the [`wandb.Artifact`](../../ref/python/artifact/) API.
 3. Add one or more files, such as a model file or dataset, to your artifact object.
 4. Log your artifact to W&B.
 
@@ -56,7 +56,7 @@ artifact.save()
 ```
 
 {{% alert %}}
-See the [track external files](./track-external-files.md) page for information on how to add references to files or directories stored in external object storage, like an Amazon S3 bucket. 
+See the [track external files](./track-external-files/) page for information on how to add references to files or directories stored in external object storage, like an Amazon S3 bucket. 
 {{% /alert %}}
 
 ## Download an artifact
@@ -76,12 +76,12 @@ datadir = artifact.download() #downloads the full "my_data" artifact to the defa
 ```
 
 {{% alert %}}
-You can pass a custom path into the `root` [parameter](../../ref/python/artifact.md) to download an artifact to a specific directory. For alternate ways to download artifacts and to see additional parameters, see the guide on [downloading and using artifacts](./download-and-use-an-artifact.md).
+You can pass a custom path into the `root` [parameter](../../ref/python/artifact/) to download an artifact to a specific directory. For alternate ways to download artifacts and to see additional parameters, see the guide on [downloading and using artifacts](./download-and-use-an-artifact/).
 {{% /alert %}}
 
 
 ## Next steps
-* Learn how to [version](./create-a-new-artifact-version.md), [update](./update-an-artifact.md), or [delete](./delete-artifacts.md) artifacts.
-* Learn how to trigger downstream workflows in response to changes to your artifacts with [artifact automation](./project-scoped-automations.md).
-* Learn about the [model registry](../model_registry/intro.md), a space that houses trained models.
-* Explore the [Python SDK](../../ref/python/artifact.md) and [CLI](../../ref/cli/wandb-artifact/README.md) reference guides.
+* Learn how to [version](./create-a-new-artifact-version/), [update](./update-an-artifact/), or [delete](./delete-artifacts/) artifacts.
+* Learn how to trigger downstream workflows in response to changes to your artifacts with [artifact automation](./project-scoped-automations/).
+* Learn about the [model registry](../model_registry/), a space that houses trained models.
+* Explore the [Python SDK](../../ref/python/artifact/) and [CLI](../../ref/cli/wandb-artifact/README/) reference guides.

content/guides/core/artifacts/artifacts-walkthrough.md

@@ -5,7 +5,7 @@ description: >-
 displayed_sidebar: default
 title: "Tutorial: Create, track, and use a dataset artifact"
 ---
-This walkthrough demonstrates how to create, track, and use a dataset artifact from [W&B Runs](../runs/intro.md).
+This walkthrough demonstrates how to create, track, and use a dataset artifact from [W&B Runs](../runs/).
 
 ## 1. Log into W&B
 
@@ -19,7 +19,7 @@ wandb.login()
 
 ## 2. Initialize a run
 
-Use the [`wandb.init()`](../../ref/python/init.md) API to generate a background process to sync and log data as a W&B Run. Provide a project name and a job type:
+Use the [`wandb.init()`](../../ref/python/init/) API to generate a background process to sync and log data as a W&B Run. Provide a project name and a job type:
 
 ```python
 # Create a W&B Run. Here we specify 'dataset' as the job type since this example
@@ -29,15 +29,15 @@ run = wandb.init(project="artifacts-example", job_type="upload-dataset")
 
 ## 3. Create an artifact object
 
-Create an artifact object with the [`wandb.Artifact()`](../../ref/python/artifact.md) API. Provide a name for the artifact and a description of the file type for the `name` and `type` parameters, respectively.
+Create an artifact object with the [`wandb.Artifact()`](../../ref/python/artifact/) API. Provide a name for the artifact and a description of the file type for the `name` and `type` parameters, respectively.
 
 For example, the following code snippet demonstrates how to create an artifact called `‘bicycle-dataset’` with a `‘dataset’` label:
 
 ```python
 artifact = wandb.Artifact(name="bicycle-dataset", type="dataset")
 ```
 
-For more information about how to construct an artifact, see [Construct artifacts](./construct-an-artifact.md).
+For more information about how to construct an artifact, see [Construct artifacts](./construct-an-artifact/).
 
 ## Add the dataset to the artifact
 
@@ -60,7 +60,7 @@ Use the W&B run objects `log_artifact()` method to both save your artifact versi
 run.log_artifact(artifact)
 ```
 
-A `'latest'` alias is created by default when you log an artifact. For more information about artifact aliases and versions, see [Create a custom alias](./create-a-custom-alias.md) and [Create new artifact versions](./create-a-new-artifact-version.md), respectively.
+A `'latest'` alias is created by default when you log an artifact. For more information about artifact aliases and versions, see [Create a custom alias](./create-a-custom-alias/) and [Create new artifact versions](./create-a-new-artifact-version/), respectively.
 
 ## 5. Download and use the artifact
 
@@ -82,4 +82,4 @@ artifact = run.use_artifact("bicycle-dataset:latest")
 artifact_dir = artifact.download()
 ```
 
-Alternatively, you can use the Public API (`wandb.Api`) to export (or update data) data already saved in a W&B outside of a Run. See [Track external files](./track-external-files.md) for more information.
+Alternatively, you can use the Public API (`wandb.Api`) to export (or update data) data already saved in a W&B outside of a Run. See [Track external files](./track-external-files/) for more information.

content/guides/core/artifacts/construct-an-artifact.md

@@ -9,17 +9,17 @@ title: Create an artifact
 weight: 2
 ---
 
-Use the W&B Python SDK to construct artifacts from [W&B Runs](../../ref/python/run.md). You can add [files, directories, URIs, and files from parallel runs to artifacts](#add-files-to-an-artifact). After you add a file to an artifact, save the artifact to the W&B Server or [your own private server](../hosting/hosting-options/self-managed.md).
+Use the W&B Python SDK to construct artifacts from [W&B Runs](../../ref/python/run/). You can add [files, directories, URIs, and files from parallel runs to artifacts](#add-files-to-an-artifact). After you add a file to an artifact, save the artifact to the W&B Server or [your own private server](../hosting/hosting-options/self-managed/).
 
-For information on how to track external files, such as files stored in Amazon S3, see the [Track external files](./track-external-files.md) page.
+For information on how to track external files, such as files stored in Amazon S3, see the [Track external files](./track-external-files/) page.
 
 ## How to construct an artifact
 
-Construct a [W&B Artifact](../../ref/python/artifact.md) in three steps:
+Construct a [W&B Artifact](../../ref/python/artifact/) in three steps:
 
 ### 1. Create an artifact Python object with `wandb.Artifact()`
 
-Initialize the [`wandb.Artifact()`](../../ref/python/artifact.md) class to create an artifact object. Specify the following parameters:
+Initialize the [`wandb.Artifact()`](../../ref/python/artifact/) class to create an artifact object. Specify the following parameters:
 
 * **Name**: Specify a name for your artifact. The name should be unique, descriptive, and easy to remember. Use an artifacts name to both: identify the artifact in the W&B App UI and when you want to use that artifact.
 * **Type**: Provide a type. The type should be simple, descriptive and correspond to a single step of your machine learning pipeline. Common artifact types include `'dataset'` or `'model'`.
@@ -28,15 +28,15 @@ Initialize the [`wandb.Artifact()`](../../ref/python/artifact.md) class to creat
 {{% alert %}}
 The "name" and "type" you provide is used to create a directed acyclic graph. This means you can view the lineage of an artifact on the W&B App. 
 
-See the [Explore and traverse artifact graphs](./explore-and-traverse-an-artifact-graph.md) for more information.
+See the [Explore and traverse artifact graphs](./explore-and-traverse-an-artifact-graph/) for more information.
 {{% /alert %}}
 
 
 {{% alert color="secondary" %}}
 Artifacts can not have the same name, even if you specify a different type for the types parameter. In other words, you can not create an artifact named `cats` of type `dataset` and another artifact with the same name of type `model`.
 {{% /alert %}}
 
-You can optionally provide a description and metadata when you initialize an artifact object. For more information on available attributes and parameters, see [`wandb.Artifact`](../../ref/python/artifact.md) Class definition in the Python SDK Reference Guide.
+You can optionally provide a description and metadata when you initialize an artifact object. For more information on available attributes and parameters, see [`wandb.Artifact`](../../ref/python/artifact/) Class definition in the Python SDK Reference Guide.
 
 The proceeding example demonstrates how to create a dataset artifact:
 
@@ -56,7 +56,7 @@ Add files, directories, external URI references (such as Amazon S3) and more wit
 artifact.add_file(local_path="hello_world.txt", name="optional-name")
 ```
 
-You can also add multiple files with the [`add_dir`](../../ref/python/artifact.md#add_dir) method. For more information on how to add files, see [Update an artifact](./update-an-artifact.md).
+You can also add multiple files with the [`add_dir`](../../ref/python/artifact.md#add_dir) method. For more information on how to add files, see [Update an artifact](./update-an-artifact/).
 
 ### 3. Save your artifact to the W&B server
 
@@ -69,7 +69,7 @@ run = wandb.init(project="artifacts-example", job_type="job-type")
 run.log_artifact(artifact)
 ```
 
-You can optionally construct an artifact outside of a W&B run. For more information, see [Track external files](./track-external-files.md).
+You can optionally construct an artifact outside of a W&B run. For more information, see [Track external files](./track-external-files/).
 
 {{% alert color="secondary" %}}
 Calls to `log_artifact` are performed asynchronously for performant uploads. This can cause surprising behavior when logging artifacts in a loop. For example:

content/guides/core/artifacts/create-a-new-artifact-version.md

@@ -9,7 +9,7 @@ title: Create an artifact version
 weight: 6
 ---
 
-Create a new artifact version with a single [run](../runs/intro.md) or collaboratively with distributed runs. You can optionally create a new artifact version from a previous version known as an [incremental artifact](#create-a-new-artifact-version-from-an-existing-version).
+Create a new artifact version with a single [run](../runs/) or collaboratively with distributed runs. You can optionally create a new artifact version from a previous version known as an [incremental artifact](#create-a-new-artifact-version-from-an-existing-version).
 
 {{% alert %}}
 We recommend that you create an incremental artifact when you need to apply changes to a subset of files in an artifact, where the size of the original artifact is significantly larger.