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.
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()
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='upload-dataset')
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')
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.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.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.
# 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.Last modified 19d ago