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Add async pipeline version

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allegroai 2024-02-27 09:44:20 +02:00
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examples/pipeline/readme.md
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@ -15,8 +15,15 @@ Training a scikit-learn model (see example/sklearn)
`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"` `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"`
3. Create pipeline model endpoint: 3. Create pipeline model endpoint:
Threaded version
`clearml-serving --id <service_id> model add --engine custom --endpoint "test_model_pipeline" --preprocess "examples/pipeline/preprocess.py"` `clearml-serving --id <service_id> model add --engine custom --endpoint "test_model_pipeline" --preprocess "examples/pipeline/preprocess.py"`
AsyncIO version
`clearml-serving --id <service_id> model add --engine custom_async --endpoint "test_model_pipeline" --preprocess "examples/pipeline/async_preprocess.py"`
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. 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.
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` 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`