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https://github.com/clearml/clearml-serving
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36 lines
1.9 KiB
Markdown
36 lines
1.9 KiB
Markdown
# Train and Deploy XGBoost model
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## training iris classifier model
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Run the mock python training code
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```bash
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pip install -r examples/xgboost/requirements.txt
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python examples/xgboost/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 xgboost 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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3. `clearml-serving --id <service_id> model add --engine xgboost --endpoint "test_model_xgb" --preprocess "examples/xgboost/preprocess.py" --name "train xgboost model - xgb_model" --project "serving examples"`
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Or auto update
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`clearml-serving --id <service_id> model auto-update --engine xgboost --endpoint "test_model_xgb_auto" --preprocess "examples/xgboost/preprocess.py" --name "train xgboost model - xgb_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_xgb_auto" --weights 0.1 0.9 --input-endpoint-prefix test_model_xgb_auto`
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3. 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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4. 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_xgb" -H "accept: application/json" -H "Content-Type: application/json" -d '{"x0": 1, "x1": 2, "x2": 3, "x3": 4}'`
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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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