> ## Documentation Index
> Fetch the complete documentation index at: https://documentation.rebase.energy/llms.txt
> Use this file to discover all available pages before exploring further.

# Logging

> Log objects like assets, datasets and models to the Rebase Platform

{/*
Inspired by: 
https://docs.ray.io/en/latest/serve/getting_started.html
https://modal.com/docs/guide/webhooks
*/}

```python
# Import the Rebase Toolkit
import rebase
rocket-launch

# 1. Start a W&B Run
run = wandb.init(project="cat-classification", notes="", tags=["baseline", "paper1"])

#  2. Capture a dictionary of hyperparameters
wandb.config = {"epochs": 100, "learning_rate": 0.001, "batch_size": 128}

# Set up model and data
model, dataloader = get_model(), get_data()

for epoch in range(wandb.config.epochs):
    for batch in dataloader:
        loss, accuracy = model.training_step()
        #  3. Log metrics inside your training loop to visualize
        # model performance
        wandb.log({"accuracy": accuracy, "loss": loss})

# 4. Log an artifact to W&B
wandb.log_artifact(model)

# Optional: save model at the end
model.to_onnx()
wandb.save("model.onnx")
```