mirror of
https://github.com/clearml/clearml-serving
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40 lines
1.8 KiB
Markdown
40 lines
1.8 KiB
Markdown
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# Train and Deploy Scikit-Learn model
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## training mock model
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Run the mock python training code
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```bash
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python3 train_model.py
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```
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The output will be a model created on the project "serving examples", by the name "train sklearn model"
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## setting up the serving service
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1. Create serving Service: `clearml-serving create --name "serving example"` (write down the service ID)
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2. Create model endpoint:
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`clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_model_sklearn" --preprocess "preprocess.py" --name "train sklearn model" --project "serving examples"`
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Or auto update
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`clearml-serving --id <service_id> model auto-update --engine sklearn --endpoint "test_model_sklearn_auto" --preprocess "preprocess.py" --name "train sklearn model" --project "serving examples" --max-versions 2`
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Or add Canary endpoint
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`clearml-serving --id <service_id> model canary --endpoint "test_model_sklearn_auto" --weights 0.1 0.9 --input-endpoint-prefix test_model_sklearn_auto`
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4. Run the clearml-serving container `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: `curl -X POST "http://127.0.0.1:8080/serve/test_model_sklearn" -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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### Running / debugging the serving service manually
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Once you have setup the Serving Service Task
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```bash
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$ pip3 install -r clearml_serving/serving/requirements.txt
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$ CLEARML_SERVING_TASK_ID=<service_id> PYHTONPATH=$(pwd) python3 -m gunicorn \
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--preload clearml_serving.serving.main:app \
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--workers 4 \
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--worker-class uvicorn.workers.UvicornWorker \
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--bind 0.0.0.0:8080
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```
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