> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-css-tab-borders.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> Integrate W&B with Skorch to log scikit-learn compatible PyTorch model training metrics and hyperparameters.

# Skorch

This page shows you how to use W\&B with [Skorch](https://skorch.readthedocs.io/) so you can track Skorch model training without writing custom logging code. When you integrate the two, W\&B automatically logs the model with the best performance, along with all model performance metrics, the model topology, and compute resources after each epoch. W\&B automatically logs every file you save in `wandb_run.dir`.

For more information, see this [example run](https://app.wandb.ai/borisd13/skorch/runs/s20or4ct?workspace=user-borisd13).

## Parameters

The following table lists the parameters that the `WandbLogger` callback accepts.

| Parameter      | Type                                    | Description                                                                                                                                                                   |
| :------------- | :-------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `wandb_run`    | `wandb.wandb_run`. Run                  | The W\&B run used to log data.                                                                                                                                                |
| `save_model`   | `bool` (default=`True`)                 | Whether to save a checkpoint of the best model and upload it to your run on W\&B.                                                                                             |
| `keys_ignored` | `str` or list of `str` (default=`None`) | Key or list of keys not to log to TensorBoard. In addition to the keys you provide, W\&B ignores keys such as those starting with `event_` or ending with `_best` by default. |

## Example code

The following examples show end-to-end usage of `WandbLogger` with Skorch:

* [Colab](https://colab.research.google.com/drive/1Bo8SqN1wNPMKv5Bn9NjwGecBxzFlaNZn?usp=sharing): A simple demo to try the integration.
* [Step-by-step guide](https://app.wandb.ai/cayush/uncategorized/reports/Automate-Kaggle-model-training-with-Skorch-and-W%26B--Vmlldzo4NTQ1NQ): A walkthrough for tracking your Skorch model performance.

```bash theme={null}
pip install wandb
```

```python theme={null}
import wandb
from skorch.callbacks import WandbLogger

# Create a wandb run
wandb_run = wandb.init()

# Log hyper-parameters (optional)
wandb_run.config.update({"learning rate": 1e-3, "batch size": 32})

net = NeuralNet(..., callbacks=[WandbLogger(wandb_run)])
net.fit(X, y)
```

## Method reference

The following table lists the callback methods that `WandbLogger` provides and when Skorch invokes each one.

| Method                                                | Description                                                                                |
| :---------------------------------------------------- | :----------------------------------------------------------------------------------------- |
| `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_end`(net, \*\*kwargs)                       | Log values from the last history step and save the best model.                             |
| `on_grad_computed`(net, named\_parameters\[, X, ...]) | Called once per batch after gradients are computed but before an update step is 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.                                                             |