> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/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 demonstrates how to create, track, and use a dataset artifact.

## 1. Log into W\&B

Import the W\&B 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.

```python theme={null}
import wandb

wandb.login()
```

## 2. 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 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()`](/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).

## 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:

```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.

## 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](/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)
```

A `'latest'` [alias](/models/artifacts/create-a-custom-alias) is created by default when you log an artifact. 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, you 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)
```

## 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 W\&B servers.

1. First, initialize a new run object with **`wandb.init()`.**
2. Second, use the run objects [`wandb.Run.use_artifact()`](/models/ref/python/experiments/run#use_artifact) method to tell W\&B what artifact to use. This returns an artifact object.
3. Third, use the artifacts [`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 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 a W\&B outside of a Run. See [Track external files](./track-external-files) for more information.