diff --git a/content/guides/core/reports/_index.md b/content/guides/core/reports/_index.md index fab5f591c..b1fe9d798 100644 --- a/content/guides/core/reports/_index.md +++ b/content/guides/core/reports/_index.md @@ -12,7 +12,7 @@ cascade: --- -{{< cta-button productLink="https://wandb.ai/stacey/deep-drive/reports/The-View-from-the-Driver-s-Seat--Vmlldzo1MTg5NQ?utm_source=fully_connected&utm_medium=blog&utm_campaign=view+from+the+drivers+seat" colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/Report_API_Quickstart.ipynb" >}} +{{< cta-button productLink="https://wandb.ai/stacey/deep-drive/reports/The-View-from-the-Driver-s-Seat--Vmlldzo1MTg5NQ?utm_source=fully_connected&utm_medium=blog&utm_campaign=view+from+the+drivers+seat" colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/intro/Report_API_Quickstart.ipynb" >}} Use W&B Reports to: - Organize Runs. @@ -42,12 +42,12 @@ Create a collaborative report with a few clicks. 6. Click **Publish to project**. 7. Click the **Share** button to share your report with collaborators. -See the [Create a report](./create-a-report/) page for more information on how to create reports interactively an programmatically with the W&B Python SDK. +See the [Create a report](./create-a-report.md) page for more information on how to create reports interactively an programmatically with the W&B Python SDK. ## 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. -* Explore the [Reports gallery](./reports-gallery/) for examples of live reports. -* Try the [Programmatic Workspaces](../../tutorials/workspaces/) tutorial to learn how to create and customize your workspace. +* 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). \ No newline at end of file diff --git a/content/guides/core/reports/clone-and-export-reports.md b/content/guides/core/reports/clone-and-export-reports.md index c377499e3..6af1c9502 100644 --- a/content/guides/core/reports/clone-and-export-reports.md +++ b/content/guides/core/reports/clone-and-export-reports.md @@ -26,7 +26,7 @@ Clone a report to reuse a project's template and format. Cloned projects are vis {{% tab header="Python SDK" value="python"%}} Load a Report from a URL to use it as a template. diff --git a/content/guides/core/reports/create-a-report.md b/content/guides/core/reports/create-a-report.md index 3ad6bbed1..21bcd69cd 100644 --- a/content/guides/core/reports/create-a-report.md +++ b/content/guides/core/reports/create-a-report.md @@ -11,7 +11,7 @@ weight: 10 Create a report interactively with the W&B App UI or programmatically with the W&B Python SDK. {{% alert %}} -See this [Google Colab for an example](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/Report_API_Quickstart.ipynb). +See this [Google Colab for an example](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/intro/Report_API_Quickstart.ipynb). {{% /alert %}} {{< tabpane text=true >}} diff --git a/content/guides/integrations/keras.md b/content/guides/integrations/keras.md index c0dabe031..33039ede7 100644 --- a/content/guides/integrations/keras.md +++ b/content/guides/integrations/keras.md @@ -6,7 +6,7 @@ menu: title: Keras weight: 160 --- -{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/Intro_to_Weights_%26_Biases_keras.ipynb" >}} +{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/intro/Intro_to_Weights_%26_Biases_keras.ipynb" >}} ## Keras callbacks @@ -258,7 +258,7 @@ See our [example repo](https://github.com/wandb/examples) for scripts, including 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/) for full details. +Check out [the reference documentation for the `keras.WandbCallback`](../../ref/python/integrations/keras/wandbcallback.md) for full details. The `WandbCallback` diff --git a/content/guides/integrations/pytorch.md b/content/guides/integrations/pytorch.md index 7955ac41c..5dcc51fb0 100644 --- a/content/guides/integrations/pytorch.md +++ b/content/guides/integrations/pytorch.md @@ -6,7 +6,7 @@ menu: title: PyTorch weight: 300 --- -{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/Intro_to_Weights_%26_Biases.ipynb" >}} +{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/intro/Intro_to_Weights_%26_Biases.ipynb" >}} 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. @@ -18,7 +18,7 @@ You can also see our [example repo](https://github.com/wandb/examples) for scrip ## Log gradients with `wandb.watch` -To automatically log gradients, you can call [`wandb.watch`](../../ref/python/watch/) and pass in your PyTorch model. +To automatically log gradients, you can call [`wandb.watch`](../../ref/python/watch.md) and pass in your PyTorch model. ```python import wandb @@ -40,7 +40,7 @@ for batch_idx, (data, target) in enumerate(train_loader): wandb.log({"loss": loss}) ``` -If you need to track multiple models in the same script, you can call `wandb.watch` on each model separately. Reference documentation for this function is [here](../../ref/python/watch/). +If you need to track multiple models in the same script, you can call `wandb.watch` on each model separately. Reference documentation for this function is [here](../../ref/python/watch.md). {{% alert color="secondary" %}} Gradients, metrics, and the graph won't be logged until `wandb.log` is called after a forward _and_ backward pass. @@ -48,14 +48,14 @@ Gradients, metrics, and the graph won't be logged until `wandb.log` is called af ## Log images and media -You can pass PyTorch `Tensors` with image data into [`wandb.Image`](../../ref/python/data-types/image/) and utilities from [`torchvision`](https://pytorch.org/vision/stable/index.html) will be used to convert them to images automatically: +You can pass PyTorch `Tensors` with image data into [`wandb.Image`](../../ref/python/data-types/image.md) and utilities from [`torchvision`](https://pytorch.org/vision/stable/index.html) will be used to convert them to images automatically: ```python images_t = ... # generate or load images as PyTorch Tensors wandb.log({"examples": [wandb.Image(im) for im in images_t]}) ``` -For more on logging rich media to W&B in PyTorch and other frameworks, check out our [media logging guide](../track/log/media/). +For more on logging rich media to W&B in PyTorch and other frameworks, check out our [media logging guide](../track/log/media.md). If you also want to include information alongside media, like your model's predictions or derived metrics, use a `wandb.Table`. @@ -72,13 +72,13 @@ wandb.log({"mnist_predictions": my_table}) {{< img src="/images/integrations/pytorch_example_table.png" alt="The code above generates a table like this one. This model's looking good!" >}} -For more on logging and visualizing datasets and models, check out our [guide to W&B Tables](../tables/). +For more on logging and visualizing datasets and models, check out our [guide to W&B Tables](../tables/intro.md). ## Profile PyTorch code {{< img src="/images/integrations/pytorch_example_dashboard.png" alt="View detailed traces of PyTorch code execution inside W&B dashboards." >}} -W&B integrates directly with [PyTorch Kineto](https://github.com/pytorch/kineto)'s [Tensorboard plugin](https://github.com/pytorch/kineto/blob/master/tb_plugin/README/) to provide tools for profiling PyTorch code, inspecting the details of CPU and GPU communication, and identifying bottlenecks and optimizations. +W&B integrates directly with [PyTorch Kineto](https://github.com/pytorch/kineto)'s [Tensorboard plugin](https://github.com/pytorch/kineto/blob/master/tb_plugin/README.md) to provide tools for profiling PyTorch code, inspecting the details of CPU and GPU communication, and identifying bottlenecks and optimizations. ```python profile_dir = "path/to/run/tbprofile/"