> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-docs-hivemind-launch.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

You can use W\&B 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.

See [example run](https://app.wandb.ai/borisd13/skorch/runs/s20or4ct?workspace=user-borisd13).

## Parameters

| Parameter      | Type                              | Description                                                                                                                                                                                                  |
| :------------- | :-------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `wandb_run`    | `wandb.wandb_run`. Run            | wandb 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 that should not be logged to tensorboard. Note that in addition to the keys provided by the user, keys such as those starting with `event_` or ending on `_best` are ignored by default. |

## Example code

We've created a few examples for you to see how the integration works:

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

```python theme={null}
# Install wandb
... pip install wandb

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

| 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 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_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.                                                                    |