Utilize W&B Integrations if you use use popular frameworks such as PyTorch, Keras, or Scikit. See our Integration guides for a for a full list of integrations and information on how to add W&B to your code.
The image above shows an example dashboard where you can view and compare metrics across multiple runs.
How it works
Track a machine learning experiment with a few lines of code:
- Create a W&B run.
- Store a dictionary of hyperparameters, such as learning rate or model type, into your configuration (
- Log metrics (
wandb.log()) over time in a training loop, such as accuracy and loss.
- Save outputs of a run, like the model weights or a table of predictions.
The proceeding pseudocode demonstrates a common W&B Experiment tracking workflow:
# 1. Start a W&B Run
# 2. Save mode inputs and hyperparameters
wandb.config.learning_rate = 0.01
# Import model and data
model, dataloader = get_model(), get_data()
# Model training code goes here
# 3. Log metrics over time to visualize performance
# 4. Log an artifact to W&B
How to get started
Depending on your use case, explore the following resources to get started with W&B Experiments:
- If this is your first time using W&B Artifacts, we recommend you go through the Experiments Colab notebook.
- Read the W&B Quickstart for a step-by-step outline of the W&B Python SDK commands you could use to create, track, and use a dataset artifact.
- Explore this chapter to learn how to:
- Create an experiment
- Configure experiments
- Log data from experiments
- View results from experiments
- Explore the W&B Python Library within the W&B API Reference Guide.