Quickstart

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Quickstart
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.
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.​
Log into Weights & Biases
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.
import wandb
​
wandb.login()
Initialize a run
Use the wandb.init() API to generate a background process to sync and log data as a W&B Run. 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.
run = wandb.init(project="artifacts-example", job_type='dataset')
Create an artifact object
Create an artifact object with the wandb.Artifact() API. 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.
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 preceding code snippet with the path to the file you want to add to the artifact.
Log the dataset
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
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.
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.
1.First, initialize a new run object with wandb.init().
2.Second, use the run objects use_artifact() method to tell Weights & Biases what artifact to use. This returns an artifact object.
3.Third, use the artifacts 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.
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()
Alternatively, you can use the Public API (wandb.Api) to export (or update data) data already saved in a Weights & Biases outside of a Run. See Track external files for more information.
Artifacts
Construct artifacts
Last modified 27d ago
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Outline
Log into Weights & Biases
Initialize a run
Create an artifact object
Add the dataset to the artifact
Log the dataset
Download and use the artifact