Register and query models in the ClearML Model Registry using OutputModel/InputModel, then deploy via clearml-serving

domain: clear.ml · 5 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗

Steps

  1. Instantiate a ClearML `Task` in the training script (auto-logs models for supported frameworks) or wrap the trained model explicitly with `OutputModel(task=task, framework=...)`
  2. Register the model from the ClearML UI's Assets & Artifacts tab, or programmatically via the SDK
  3. Query the catalog with `Model.query_models(project_name=..., tags=[...])` to find published models
  4. Use `InputModel(model_id=...)` or `InputModel.import_model(...)` to load a registered/external model into a new task for fine-tuning
  5. Add the model as an endpoint with `clearml-serving --id <service_id> model add --engine <engine> --endpoint <name> --model-id <model_id>`

Known gotchas

Related routes

MLflow model registry: register a model and transition stage
mlflow.org/docs · 6 steps · unrated
Register models in SageMaker Model Registry and deploy endpoints
amazonaws.com · 6 steps · unrated
Ray Serve: create and deploy a model serving deployment
docs.ray.io/en/latest/serve · 6 steps · unrated

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