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Try in Colab

Try in W&B

Track machine learning experiments with a few lines of code. You can then review the results in an interactive dashboard or export your data to Python for programmatic access using our Public API. Utilize W&B Integrations if you use popular frameworks such as PyTorch, Keras, or Scikit. See our Integration guides for a full list of integrations and information on how to add W&B to your code.
Experiments dashboard
The image above shows an example dashboard where you can view and compare metrics across multiple runs.

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How it works

Track a machine learning experiment with a few lines of code:
  1. Create a W&B Run.
  2. Store a dictionary of hyperparameters, such as learning rate or model type, into your configuration (wandb.Run.config).
  3. Log metrics (wandb.Run.log()) over time in a training loop, such as accuracy and loss.
  4. Save outputs of a run, like the model weights or a table of predictions.
The following code demonstrates a common W&B experiment tracking workflow: 
# Start a run.
#
# When this block exits, it waits for logged data to finish uploading.
# If an exception is raised, the run is marked failed.
with wandb.init(entity="", project="my-project-name") as run:
  # Save mode inputs and hyperparameters.
  run.config.learning_rate = 0.01

  # Run your experiment code.
  for epoch in range(num_epochs):
    # Do some training...

    # Log metrics over time to visualize model performance.
    run.log({"loss": loss})

  # Upload model outputs as artifacts.
  run.log_artifact(model)

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Get started

Depending on your use case, explore the following resources to get started with W&B Experiments:
  • 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.

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Best practices and tips

For best practices and tips for experiments and logging, see Best Practices: Experiments and Logging.
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