Artifacts

Use W&B Artifacts to track and version data as the inputs and outputs of your W&B Runs. For example, a model training run might take in a dataset as input and produce a trained model as output. You can log hyperparameters, metadata, and metrics to a run, and you can use an artifact to log, track, and version the dataset used to train the model as input and another artifact for the resulting model checkpoints as output.

Use cases

You can use artifacts throughout your entire ML workflow as inputs and outputs of runs. You can use datasets, models, or even other artifacts as inputs for processing.

Create an artifact

Create an artifact with four lines of code:

1. Create a W&B run.
2. Create an artifact object with the wandb.Artifact API.
3. Add one or more files, such as a model file or dataset, to your artifact object.
4. Log your artifact to W&B.

For example, the proceeding code snippet shows how to log a file called dataset.h5 to an artifact called example_artifact:

import wandb

run = wandb.init(project="artifacts-example", job_type="add-dataset")
artifact = wandb.Artifact(name="example_artifact", type="dataset")
artifact.add_file(local_path="./dataset.h5", name="training_dataset")
artifact.save()

# Logs the artifact version "my_data" as a dataset with data from dataset.h5

Download an artifact

Indicate the artifact you want to mark as input to your run with the use_artifact method.

Following the preceding code snippet, this next code block shows how to use the training_dataset artifact:

artifact = run.use_artifact(
    "training_dataset:latest"
)  # returns a run object using the "my_data" artifact

This returns an artifact object.

Next, use the returned object to download all contents of the artifact:

datadir = (
    artifact.download()
)  # downloads the full `my_data` artifact to the default directory.

Next steps

• Learn how to version and update artifacts.
• Learn how to trigger downstream workflows or notify a Slack channel in response to changes to your artifacts with automations.
• Learn about the registry, a space that houses trained models.
• Explore the Python SDK and CLI reference guides.