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30 lines
1.7 KiB
Markdown
30 lines
1.7 KiB
Markdown
# Deploy a model inference pipeline
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## prerequisites
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Training a scikit-learn model (see example/sklearn)
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## setting up the serving service
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1. Create serving Service (if not already running):
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`clearml-serving create --name "serving example"` (write down the service ID)
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2. Create model base two endpoints:
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`clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_model_sklearn_a" --preprocess "examples/sklearn/preprocess.py" --name "train sklearn model" --project "serving examples"`
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`clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_model_sklearn_b" --preprocess "examples/sklearn/preprocess.py" --name "train sklearn model" --project "serving examples"`
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3. Create pipeline model endpoint:
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`clearml-serving --id <service_id> model add --engine custom --endpoint "test_model_pipeline" --preprocess "examples/pipeline/preprocess.py"`
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4. If you already have the `clearml-serving` docker-compose running, it might take it a minute or two to sync with the new endpoint.
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Or you can run the clearml-serving container independently `docker run -v ~/clearml.conf:/root/clearml.conf -p 8080:8080 -e CLEARML_SERVING_TASK_ID=<service_id> clearml-serving:latest`
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5. Test new endpoint (do notice the first call will trigger the model pulling, so it might take longer, from here on, it's all in memory): `curl -X POST "http://127.0.0.1:8080/serve/test_model_pipeline" -H "accept: application/json" -H "Content-Type: application/json" -d '{"x0": 1, "x1": 2}'`
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> **_Notice:_** You can also change the serving service while it is already running!
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This includes adding/removing endpoints, adding canary model routing etc.
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by default new endpoints/models will be automatically updated after 1 minute
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