Deploy Model with Conda Runtime
*******************************

Once you have ADS Model object, you can call ``deploy`` function to deploy the model and generate the endpoint.

Here is an example of deploying LightGBM model:

.. code-block:: python3

    import lightgbm as lgb
    import tempfile
    from ads.common.model_metadata import UseCaseType
    from ads.model.framework.lightgbm_model import LightGBMModel
    from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split

    # Load dataset and Prepare train and test split
    iris = load_iris()
    X, y = iris.data, iris.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

    # Train a LightGBM Classifier model
    train = lgb.Dataset(X_train, label=y_train)
    param = {
      'objective': 'multiclass', 'num_class': 3,
    }
    lightgbm_estimator = lgb.train(param, train)

    # Instantiate ads.model.LightGBMModel using the trained LGBM Model
    lightgbm_model = LightGBMModel(estimator=lightgbm_estimator, artifact_dir=tempfile.mkdtemp())

    # Autogenerate score.py, pickled model, runtime.yaml, input_schema.json and output_schema.json
    lightgbm_model.prepare(
        inference_conda_env="generalml_p38_cpu_v1",
        X_sample=X_train,
        y_sample=y_train,
        use_case_type=UseCaseType.BINARY_CLASSIFICATION,
    )

    # Verify generated artifacts
    lightgbm_model.verify(X_test)

    # Register LightGBM model
    model_id = lightgbm_model.save()

    # Deploy LightGBM model
    lightgbm_model.deploy(
            display_name="LightGBM Model",
            deployment_log_group_id="ocid1.loggroup.oc1.xxx.xxxxx",
            deployment_access_log_id="ocid1.log.oc1.xxx.xxxxx",
            deployment_predict_log_id="ocid1.log.oc1.xxx.xxxxx",
            # Shape config details mandatory for flexible shapes:
            # deployment_instance_shape="VM.Standard.E4.Flex",
            # deployment_ocpus=<number>,
            # deployment_memory_in_gbs=<number>,
        )

    # Get endpoint of deployed model
    model_deployment_url = lightgbm_model.model_deployment.url

    # Generate prediction by invoking the deployed endpoint
    lightgbm_model.predict(X_test)["prediction"]

Here example retrieve predictions from model deployment endpoint using oci-cli:

.. code-block:: bash

    export model_deployment_url=<model_deployment_url>/predict
    oci raw-request --http-method POST \
        --target-uri $model_deployment_url \
        --request-body '{"data": [[5.6, 2.7, 4.2, 1.3]]}'

Find more information about oci raw-request command `here <https://docs.oracle.com/iaas/tools/oci-cli/latest/oci_cli_docs/cmdref/raw-request.html>`_.

Deploy
------

.. include:: _template/deploy.rst

Predict
-------

.. include:: _template/predict.rst

Observability
-------------

``tail`` or ``head`` logs generated by the model deployment instances -

.. code-block:: python3

  lightgbm_model.model_deployment.logs().tail()

You cal also call the ``.watch()`` from model deployment instance to stream the logs

.. code-block:: python3

  lightgbm_model.model_deployment.watch()

Update Model Deployment
-------------------------

You can update the existing Model Deployment by using ``.update_deployment()`` method. See `API documentation <../../ads.model.html#ads.model.generic_model.GenericModel.update_deployment>`__ for more details.


.. code-block:: python3

  lightgbm_model.update_deployment(
    wait_for_completion = True,
    access_log={
      log_id="ocid1.log.oc1.xxx.xxxxx",
    },
    description="Description for Custom Model",
    freeform_tags={"key": "value"},
  )