Skorch
You can use Weights & Biases 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 servers.
See example run.
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 servers. |
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: A simple demo to try the integration
- A step by step guide: to tracking your Skorch model performance
# Install wandb
... pip install wandb
import wandb
from skorch.callbacks import WandbLogger
# Create a wandb Run
wandb_run = wandb.init()
# Alternative: Create a wandb Run without a W&B account
wandb_run = wandb.init(anonymous="allow")
# 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)
Methods
| 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. |