Tutorial: Create, track, and use a dataset artifact

This walkthrough demonstrates how to create, track, and use a dataset artifact.

1. Log into Weights & Biases

Import the W&B library and log in to Weights & Biases. You will need to sign up for a free Weights & Biases account if you have not done so already.

import wandb

wandb.login()

2. Initialize a run

Use wandb.init() to initialize a run. This generates a background process to sync and log data. Provide a project name and a job type:

# Create a W&B Run. Here we specify 'dataset' as the job type since this example
# shows how to create a dataset artifact.
with wandb.init(project="artifacts-example", job_type="upload-dataset") as run:
    # Your code here

3. Create an artifact object

Create an artifact object with the wandb.Artifact(). Provide a name for the artifact and a description of the file type for the name and type parameters, respectively. For example, the following code snippet demonstrates how to create an artifact called ‘bicycle-dataset’ with a ‘dataset’ label:

artifact = wandb.Artifact(name="bicycle-dataset", type="dataset")

For more information about how to construct an artifact, see Construct artifacts.

4. Add the dataset to the artifact

Add a file to the artifact. Common file types include models and datasets. The following example adds a dataset named dataset.h5 that is saved locally on our machine to the artifact:

# Add a file to the artifact's contents
artifact.add_file(local_path="dataset.h5")

Replace the filename dataset.h5 in the previous code snippet with the path to the file you want to add to the artifact.

5. Log the dataset

Use the W&B run objects wandb.Run.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
run.log_artifact(artifact)

A 'latest' alias is created by default when you log an artifact. For more information about artifact aliases and versions, see Create a custom alias and Create new artifact versions, respectively. Putting this together, you script so far should look like this:

import wandb

wandb.login()

with wandb.init(project="artifacts-example", job_type="upload-dataset") as run:
    artifact = wandb.Artifact(name="bicycle-dataset", type="dataset")
    artifact.add_file(local_path="dataset.h5")
    run.log_artifact(artifact)

6. Download and use the artifact

The following code example demonstrates the steps you can take to use an artifact you have logged and saved to the Weights & Biases servers.

First, initialize a new run object with wandb.init().
Second, use the run objects wandb.Run.use_artifact() method to tell Weights & Biases what artifact to use. This returns an artifact object.
Third, use the artifacts wandb.Artifact.download() method to download the contents of the artifact.

# Create a W&B Run. Here we specify 'training' for 'type'
# because we will use this run to track training.
with wandb.init(project="artifacts-example", job_type="training") as run:

  # 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()

Alternatively, you can use the Public API (wandb.Api) to export (or update data) data already saved in Weights & Biases outside of a Run. See Track external files for more information.

Weights & Biases

Guides

Integrations

Reference

Support

Tutorial: Create, track, and use a dataset artifact

1. Log into Weights & Biases

2. Initialize a run

3. Create an artifact object

4. Add the dataset to the artifact

5. Log the dataset

6. Download and use the artifact

​1. Log into Weights & Biases

​2. Initialize a run

​3. Create an artifact object

​4. Add the dataset to the artifact

​5. Log the dataset

​6. Download and use the artifact

1. Log into Weights & Biases

2. Initialize a run

3. Create an artifact object

4. Add the dataset to the artifact

5. Log the dataset

6. Download and use the artifact