# Train and Deploy Scikit-Learn model ensemble

## training mock voting regression model

Run the mock python training code
```bash
pip install -r examples/ensemble/requirements.txt 
python examples/ensemble/train_ensemble.py
```

The output will be a model created on the project "serving examples", by the name "train model ensemble"

## setting up the serving service

1. Create serving Service: `clearml-serving create --name "serving example"` (write down the service ID)
2. Create model endpoint: 
`clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_model_ensemble" --preprocess "examples/ensemble/preprocess.py" --name "train model ensemble" --project "serving examples"`

Or auto update 

`clearml-serving --id <service_id> model auto-update --engine sklearn --endpoint "test_model_ensemble_auto" --preprocess "examples/ensemble/preprocess.py" --name "train model ensemble" --project "serving examples" --max-versions 2`

Or add Canary endpoint

`clearml-serving --id <service_id> model canary --endpoint "test_model_ensemble_auto" --weights 0.1 0.9 --input-endpoint-prefix test_model_ensemble_auto`

3. 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`
4. Test new endpoint: `curl -X POST "http://127.0.0.1:8080/serve/test_model_ensemble" -H "accept: application/json" -H "Content-Type: application/json" -d '{"x0": 1, "x1": 2}'`

> **_Notice:_**  You can also change the serving service while it is already running!
This includes adding/removing endpoints, adding canary model routing etc.
by default new endpoints/models will be automatically updated after 1 minute