Model registry

Model registry to manage the model lifecycle from training to production

W&B will eventually stop supporting W&B Model Registry. Users are encouraged to instead use W&B Registry for linking and sharing their model artifacts versions. W&B Registry broadens the capabilities of the legacy W&B Model Registry. For more information about W&B Registry, see the Registry docs.

W&B will migrate existing model artifacts linked to the legacy Model Registry to the new W&B Registry in the near future. See Migrating from legacy Model Registry for information about the migration process.

The W&B Model Registry houses a team’s trained models where ML Practitioners can publish candidates for production to be consumed by downstream teams and stakeholders. It is used to house staged/candidate models and manage workflows associated with staging.

With W&B Model Registry, you can:

• Bookmark your best model versions for each machine learning task.
• Automate downstream processes and model CI/CD.
• Move model versions through its ML lifecycle; from staging to production.
• Track a model’s lineage and audit the history of changes to production models.

How it works

Track and manage your staged models with a few simple steps.

1. Log a model version: In your training script, add a few lines of code to save the model files as an artifact to W&B.
2. Compare performance: Check live charts to compare the metrics and sample predictions from model training and validation. Identify which model version performed the best.
3. Link to registry: Bookmark the best model version by linking it to a registered model, either programmatically in Python or interactively in the W&B UI.

The following code snippet demonstrates how to log and link a model to the Model Registry:

import wandb
import random

# Start a new W&B run
run = wandb.init(project="models_quickstart")

# Simulate logging model metrics
run.log({"acc": random.random()})

# Create a simulated model file
with open("my_model.h5", "w") as f:
    f.write("Model: " + str(random.random()))

# Log and link the model to the Model Registry
run.link_model(path="./my_model.h5", registered_model_name="MNIST")

run.finish()

4. Connect model transitions to CI/CD workflows: transition candidate models through workflow stages and automate downstream actions with webhooks.

How to get started

Depending on your use case, explore the following resources to get started with W&B Models:

• Check out the two-part video series:
  1. Logging and registering models
  2. Consuming models and automating downstream processes in the Model Registry.
• Read the models walkthrough for a step-by-step outline of the W&B Python SDK commands you could use to create, track, and use a dataset artifact.
• Learn about:
  • Protected models and access control.
  • How to connect Registry to CI/CD processes.
  • Set up Slack notifications when a new model version is linked to a registered model.
• Review What is an ML Model Registry? to learn how to integrate Model Registry into your ML workflow.
• Take the W&B Enterprise Model Management course and learn how to:
  • Use the W&B Model Registry to manage and version your models, track lineage, and promote models through different lifecycle stages
  • Automate your model management workflows using webhooks.
  • See how the Model Registry integrates with external ML systems and tools in your model development lifecycle for model evaluation, monitoring, and deployment.