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style: apply /style-guide pass to models/artifacts #2717

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style: apply /style-guide pass to models/artifacts
johndmulhausen Jun 3, 2026
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docs: address Copilot review feedback on models/artifacts style pass
johndmulhausen Jun 5, 2026
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Merge origin/main into style-guide/models-artifacts; resolve construc…
johndmulhausen Jun 10, 2026
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docs: back out keywords frontmatter from models/artifacts pages
johndmulhausen Jun 10, 2026
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47 changes: 25 additions & 22 deletions models/artifacts/artifacts-walkthrough.mdx
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Expand Up @@ -2,46 +2,47 @@
description:
Create, track, and use a dataset artifact with W&B.
title: "Tutorial: Create, track, and use a dataset artifact"
keywords: ["artifact", "dataset versioning", "log_artifact", "use_artifact", "wandb.Artifact"]
---

This walkthrough demonstrates how to create, track, and use a dataset artifact.
This walkthrough demonstrates how to create, track, and use a dataset artifact with W&B. By the end, you've logged a dataset as a versioned artifact to W&B and downloaded it in a subsequent run. This lets you reproducibly share datasets across experiments and track them as inputs and outputs of your runs.

## 1. Log into W&B
## Log in to 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.
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
import wandb

wandb.login()
```

## 2. Initialize a run
## 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
# Create a W&B Run. Here we specify 'dataset' as the job type since this example
# 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
```

## 3. Create an artifact object
## 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.
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:
For example, the following code snippet demonstrates how to create an artifact called `'bicycle-dataset'` with a `'dataset'` label:

```python
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 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. 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
# Add a file to the artifact's contents
Expand All @@ -51,20 +52,20 @@ 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
## 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).
Use the W&B 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
# 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.
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, you script so far should look like this:
Putting this together, your script so far should look like this:

```python
import wandb
Expand All @@ -78,17 +79,17 @@ with wandb.init(project="artifacts-example", job_type="upload-dataset") as run:
```


## 6. Download and use the artifact
## 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.
Now that the dataset is logged as an artifact, you can pull it into other runs as a tracked input. 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. 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.
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
# Create a W&B Run. Here we specify 'training' for 'type'
# because we will use this run to track training.
# 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
Expand All @@ -98,4 +99,6 @@ with wandb.init(project="artifacts-example", job_type="training") as run:
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.
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. The artifact graph tracks both the upload and the download.
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