Experiments

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 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:

1. Create a W&B run.
2. Store a dictionary of hyperparameters, such as learning rate or model type, into your configuration (wandb.config).
3. Log metrics (wandb.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 proceeding pseudocode demonstrates a common W&B Experiment tracking workflow:

# 1. Start a W&B Run
wandb.init(entity="", project="my-project-name")

# 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
wandb.log({"loss": loss})

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

How to 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.