> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-style-guide-models-artifacts-20260603-165211.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> Create, track, and use a dataset artifact with W&B.

# Tutorial: Create, track, and use a dataset artifact

This walkthrough shows you how to create, track, and use a dataset artifact with W\&B. You log a dataset as a versioned artifact to W\&B, then download it in a later run. This workflow helps you reproducibly share datasets across experiments and track them as inputs and outputs of your runs.

## Log in to W\&B

Import the W\&B library and log in to W\&B. If you haven't done so already, sign up for a free W\&B account.

```python theme={null}
import wandb

wandb.login()
```

## Initialize a run

Use [`wandb.init()`](/models/ref/python/functions/init) to initialize a run. This generates a background process to sync and log data. Provide a project name and a job type:

```python theme={null}
# Create a W&B Run. Here you 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
```

## Create an artifact object

Create an artifact object with [`wandb.Artifact()`](/models/ref/python/experiments/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:

```python theme={null}
artifact = wandb.Artifact(name="bicycle-dataset", type="dataset")
```

For more information about how to construct an artifact, see [Construct artifacts](./construct-an-artifact).

## 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 your machine to the artifact:

```python theme={null}
# 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.

## Log the dataset

Use the run object's `wandb.Run.log_artifact()` method to both save your artifact version and declare the artifact as an [output of the run](/models/artifacts/explore-and-traverse-an-artifact-graph).

```python theme={null}
# Save the artifact version to W&B and mark it
# as the output of this run
run.log_artifact(artifact)
```

When you log an artifact, W\&B creates a `latest` [alias](/models/artifacts/create-a-custom-alias) by default. For more information about artifact aliases and versions, see [Create a custom alias](./create-a-custom-alias) and [Create new artifact versions](./create-a-new-artifact-version), respectively.

Putting this together, your script so far should look like this:

```python theme={null}
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)
```

## Download and use the artifact

The following code example demonstrates the steps you can take to use an artifact you've logged and saved to the W\&B servers:

1. Initialize a new run object with `wandb.init()`.
2. Use the run object's [`wandb.Run.use_artifact()`](/models/ref/python/experiments/run#use_artifact) method to specify which artifact to use. This returns an artifact object.
3. Use the artifact's [`wandb.Artifact.download()`](/models/ref/python/experiments/artifact#download) method to download the contents of the artifact.

```python theme={null}
# Create a W&B Run. Here you specify 'training' for 'type'
# because you 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 already saved in W\&B outside of a run. For more information, see [Track external files](./track-external-files).

You now have a versioned dataset artifact logged to W\&B and consumed by a downstream run.