Annotate collections

Add human-friendly text to your collections to help users understand the purpose of the collection and the artifacts it contains. Depending on the collection, you might want to include information about the training data, model architecture, task, license, references, and deployment. The proceeding lists some topics worth documenting in a collection: W&B recommends including at minimum these details:

Summary: The purpose of the collection. The machine learning framework used for the machine learning experiment.
License: The legal terms and permissions associated with the use of the machine learning model. It helps model users understand the legal framework under which they can utilize the model. Common licenses include Apache 2.0, MIT, and GPL.
References: Citations or references to relevant research papers, datasets, or external resources.

If your collection contains training data, consider including these additional details:

Training data: Describe the training data used
Processing: Processing done on the training data set.
Data storage: Where is that data stored and how to access it.

If your collection contains a machine learning model, consider including these additional details:

Architecture: Information about the model architecture, layers, and any specific design choices.
Task: The specific type of task or problem that the machine that the collection model is designed to perform. It’s a categorization of the model’s intended capability.
Deserialize the model: Provide information on how someone on your team can load the model into memory.
Deployment: Details on how and where the model is deployed and guidance on how the model is integrated into other enterprise systems, such as a workflow orchestration platforms.

Add a description to a collection

Interactively or programmatically add a description to a collection with the W&B Registry UI or Python SDK.

W&B Registry UI
Python SDK

Navigate to the W&B Registry.
Click on a collection.
Select View details next to the name of the collection.
Within the Description field, provide information about your collection. Format text within with Markdown markup language.

Use the wandb.Api().artifact_collection() method to access a collection’s description. Use the returned object’s description property to add, or update, a description to the collection.Specify the collection’s type for the type_name parameter and the collection’s full name for the name parameter. A collection’s name consists of the prefix “wandb-registry”, the name of the registry, and the name of the collection separated by a forward slashes:

wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}

Copy and paste the following code snippet into your Python script or notebook. Replace values enclosed in angle brackets (<>) with your own.

import wandb

api = wandb.Api()

collection = api.artifact_collection(
  type_name = "<collection_type>", 
  name = "<collection_name>"
  )


collection.description = "This is a description."
collection.save()  

For example, the proceeding image shows a collection that documents a model’s architecture, intended use, performance information and more.

Guides

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Reference

Add a description to a collection

Guides

Integrations

Tutorials

Reference

​Add a description to a collection

Add a description to a collection