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Restore "intro" to colabs links
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johndmulhausen committed Jan 14, 2025
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8 changes: 4 additions & 4 deletions content/guides/core/reports/_index.md
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---


{{< 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.
Expand Down Expand Up @@ -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).
2 changes: 1 addition & 1 deletion content/guides/core/reports/clone-and-export-reports.md
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{{% tab header="Python SDK" value="python"%}}

<!--
{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/Report_API_Quickstart.ipynb" >}}
{{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/intro/Report_API_Quickstart.ipynb" >}}
-->

Load a Report from a URL to use it as a template.
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2 changes: 1 addition & 1 deletion content/guides/core/reports/create-a-report.md
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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 >}}
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4 changes: 2 additions & 2 deletions content/guides/integrations/keras.md
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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

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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`

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14 changes: 7 additions & 7 deletions content/guides/integrations/pytorch.md
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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.

Expand All @@ -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
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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.
{{% /alert %}}

## 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`.

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{{< 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/"
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