Python Library

Use wandb to track machine learning work.

Train and fine-tune models, manage models from experimentation to production.

For guides and examples, see https://docs.wandb.ai.

For scripts and interactive notebooks, see https://github.com/wandb/examples.

For reference documentation, see https://docs.wandb.com/ref/python.

Classes

class Artifact: Flexible and lightweight building block for dataset and model versioning.

class Run: A unit of computation logged by wandb. Typically, this is an ML experiment.

Functions

agent(...): Start one or more sweep agents.

controller(...): Public sweep controller constructor.

finish(...): Finish a run and upload any remaining data.

init(...): Start a new run to track and log to W&B.

log(...): Upload run data.

login(...): Set up W&B login credentials.

save(...): Sync one or more files to W&B.

sweep(...): Initialize a hyperparameter sweep.

watch(...): Hooks into the given PyTorch model(s) to monitor gradients and the model’s computational graph.