Experiments overview

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 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)