The proceeding quick start demonstrates how to create, track, and use a dataset artifact. Ensure you have a Weights & Biases account before you begin.
The following procedure lists how to construct and use an artifact. Steps 1 and 2 are not unique to W&B Artifacts.
Import the Weights & Biases library and log in to W&B. You will need to sign up for a free W&B account if you have not done so already.
# Create a W&B Run. Here we specify 'dataset' as the job type since this example
# shows how to create a dataset artifact.
run = wandb.init(project="artifacts-example", job_type='upload-dataset')
For example, the following code snippet demonstrates how to create an artifact called
artifact = wandb.Artifact(name='bicycle-dataset', type='dataset')
Add a file to the artifact. Common file types include models and datasets. The following example adds a dataset named
dataset.h5that is saved locally on our machine to the artifact:
# Add a file to the artifact's contents
Replace the filename
dataset.h5in the preceding code snippet with the path to the file you want to add to the artifact.
Use the W&B run objects
log_artifact()method to both save your artifact version and declare the artifact as an output of the run.
# Save the artifact version to W&B and mark it as the output of this run
The following code example demonstrates the steps you can take to use an artifact you have logged and saved to the Weights & Biases servers.
# Create a W&B Run. Here we specify 'training' for 'type' because
# we will use this run to track training.
run = wandb.init(project="artifacts-example", job_type='training')
# Query W&B for an artifact and mark it as input to this run
artifact = run.use_artifact('bicycle-dataset:latest')
# Download the artifact's contents
artifact_dir = artifact.download()