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Model registry

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:

How it worksโ€‹

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")

  1. Connect model transitions to CI/DC workflows: transition candidate models through workflow stages and automate downstream actions with webhooks or jobs.

How to get startedโ€‹

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

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