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

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)

Method reference