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Model registry

Use W&B Models as a central system of record for your best models, standardized and organized in a model registry across projects and teams.

With W&B Models, you can:

How it worksโ€‹

Track and manage your trained 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
with wandb.init(project="models_quickstart") as run:
# 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()))

# Save the dummy model to W&B
best_model = wandb.Artifact(f"model_{}", type="model")

# Link the model to the Model Registry
run.link_artifact(best_model, "model-registry/My Registered Model")

  1. Test and deploy your model: Transition model versions through customizable workflows stages, such as staging and production.

How to get startedโ€‹

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

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