Experiments overview - CoreWeave Docs

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 Weights & Biases integrations if you use popular frameworks such as Keras. See Weights & Biases integrations for a full list of integrations and information on how to add Weights & Biases 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 (wandb.Run.config).
Log metrics (wandb.Run.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 following code demonstrates a common Weights & Biases experiment tracking workflow. Replace my-team with your Weights & Biases team entity and my-project-name with your project name.

import wandb

entity = "<my-team>"  # Set entity to your W&B team name
project = "my-project-name"  # Set project to your W&B project name

# 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=entity, project="my-project-name") as run:
  # Save model 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)

Get started

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

Read the Weights & Biases Quickstart to learn how to set up Weights & Biases and track your first experiment.
Try the Get Started with Weights & Biases to learn how to use Weights & Biases to track, share, and manage model artifacts in your machine learning workflows.
Explore this chapter to learn how to:
Explore the W&B Python Library within the W&B API Reference Guide.

Best practices and tips

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

​How it works

​Get started

​Best practices and tips

How it works

Get started

Best practices and tips