Python client usage overview

Connecting to MLOps with Python client

Note

You will need the values for the following constants in order to successfully connect to MLOps. Contact your administrator to obtain deployment specific values.

Constant	Value	Description
H2O_MLOPS_GATEWAY	Usually: https://api.mlops.my.domain	Defines the URL for the MLOps Gateway component. You can verify the correct URL by navigating to the API URL in your browser. It should provide a page with a list of available routes.
TOKEN_ENDPOINT	https://mlops.keycloak.domain/auth/realms/[fill-in-realm-name]/protocol/openid-connect/token	Defines the token endpoint URL of the Identity Provider. This uses Keycloak as the Identity Provider. Keycloak Realm should be provided.
REFRESH_TOKEN	<your-refresh-token>	Defines the user's refresh token
CLIENT_ID	<your-client-id>	Sets the client id for authentication. This is the client you will be using to connect to MLOps.

The following steps demonstrate how you can connect to MLOps using the MLOps Python client.

1. Create a connection.py file and add the content below. Make sure to replace the variable values as given above.

   Format

   connection.py

   import h2o_mlops_client as mlops
   from pprint import pprint
   
   ### Constants
   H2O_MLOPS_GATEWAY = <H2O_MLOPS_GATEWAY>
   TOKEN_ENDPOINT = <TOKEN_ENDPOINT>
   REFRESH_TOKEN = <REFRESH_TOKEN>
   CLIENT_ID = <CLIENT_ID>

   Sample

   connection.py

   import h2o_mlops_client as mlops
   from pprint import pprint
   
   H2O_MLOPS_GATEWAY = "https://api.mlops.my.domain"
   TOKEN_ENDPOINT_URL="https://mlops.keycloak.domain/auth/realms/[fill-in-realm-name]/protocol/openid-connect/token"
   REFRESH_TOKEN="<your-refresh-token>"
   CLIENT_ID="<your-mlops-client>"

2. Add the following content to the same connection.py file in order to set up the token provider using the refresh token, set up the MLOps client, and send a WhoAmI request to MLOps which displays information about the current user.

   connection.py

   def main():
       # Setting up the token provider using an existing refresh token.
           mlops_token_provider = mlops.TokenProvider(
               refresh_token=REFRESH_TOKEN,
               client_id=CLIENT_ID,
               token_endpoint_url=TOKEN_ENDPOINT_URL
           )
   
           # Setting up MLOPS client.
           mlops_client = mlops.Client(
               gateway_url=H2O_MLOPS_GATEWAY,
               token_provider=mlops_token_provider
           )
   
           # Sending a WhoAmI request to MLOps.
           r: mlops.StorageWhoAmIResponse = mlops_client.storage.user.who_am_i(body={})
           pprint(r.user)
   
       if __name__ == "__main__":
           main()

3. Run the connection.py file.

   Execution

   python3 connection.py

   Sample Response

   'created_time': datetime.datetime(2022, 7, 12, 16, 41, 13, 127190, tzinfo=tzutc()),
   'id': '3b1b1294-0172-4229-a6a8-47ee9117fc7f',
   'primary_email': '',
   'username': <your_username>

Making API calls

API calls have their corresponding functions in the client, which can be found using the following pattern:

mlops_client.grpc_server_name.entity_type.method

The following is an example call that lists projects for the current user:

mlops_client.storage.project.list_projects()

The preceding example consists of the following:

• mlops_client A client object
• storage The name of the platform gRPC server being contacted
• project The name of the entity type
• list_projects The name of the API method that is called

Available gRPC servers

• storage - The core service responsible for storing the platform's data
• deployer - The service used by the client for getting deployments' statuses
• ingest - The service responsible for ingesting models
• model-monitoring - The service used by the client to get monitoring details of deployments

Client API calls and Request/Response objects

Due to the nature of the gRPC framework, gRPC calls expect their corresponding gRPC messages as input. Because of this, client calls for the MLOps Python client consist of a specific API call and that call's request. In the example below Storage's gRPC List Projects call is being made using the mlops_client.storage.project.list_projects() client call including the mlops.StorageListProjectsRequest object after which the mlops.StorageListProjectsResponse response is being returned.

projects: mlops.StorageListProjectsResponse = (
    mlops_client.storage.project.list_projects(mlops.StorageListProjectsRequest())
)

All supported requests and responses are accessible directly through the MLOps client module.

Accessing a requested entity

In order to get a specific entity requested in an API call that entity needs to be accessed through a member with the same name as the entity. For API calls which return a collection of entities the same name is used to access the collection. Below are several examples of such calls and accessing their corresponding entities:

mlops_client.storage.experiment.get_experiment(
    mlops.StorageGetExperimentRequest(...)
).experiment  # accessing an experiment entity

mlops_client.storage.project.list_projects(
    mlops.StorageListProjectsRequest()
).project  # accessing a list of projects

mlops_client.storage.deployment.list_deployments(
    mlops.StorageListDeploymentsRequest(...)
).deployment  # accessing a list of deployments

Empty body calls

Some API calls don't have their own gRPC messages but instead expect an empty gRPC message. What's important to note is that an empty gRPC message is not equal to a None request. A good example of that is the :ref:WhoAmI example in which the who_am_i() API call doesn't have a corresponding request class but instead uses an empty JSON in the request body. The empty JSON is required for the JSON -> gRPC translation process which would otherwise fail.

r: mlops.StorageWhoAmIResponse = mlops_client.storage.user.who_am_i(body={})

Authentication

MLOps uses the OpenID Connect protocol for authentication. The client includes a helper class which helps with authentication and ensures that an active token is always available to the client.

mlops_token_provider = mlops.TokenProvider(...)
mlops_client = mlops.client(
    ...,
    token_provider=mlops_token_provider
)

Artifact names mapping

The following table describes the mapping of artifact names.

Storage artifact name	deployable_artifact_type_name	Artifact processor name
dai/mojo_pipeline	dai_mojo_pipeline	dai_mojo_pipeline_extractor
dai/scoring_pipeline	dai_python_scoring_pipeline	artifact-processor_dai_pipelines_193
h2o3/mojo	h2o3_mojo	h2o3_mojo_extractor
python/mlflow	python/mlflow.zip	unzip_processor
mlflow/mojo_pipeline	mlflow_mojo_pipeline	mlflow_mojo_pipeline_extractor
mlflow/scoring_pipeline	mlflow_scoring_pipeline	mlflow_scoring_pipeline_extractor
python/pickle	python/pickle	noop_processor

Runtime names mapping

The following table describes the mapping of runtime names.

Model type	Model description	Human-readable runtime name	Runtime name
mlflow	MLFlow non-H2O.ai models created with Python 3.6	MLflow Model Scorer [Python 3.6]	python-scorer_mlflow_36
mlflow	MLFlow non-H2O.ai models created with Python 3.7	MLflow Model Scorer [Python 3.7]	python-scorer_mlflow_37
mlflow	MLFlow non-H2O.ai models created with Python 3.8	MLflow Model Scorer [Python 3.8]	python-scorer_mlflow_38
mlflow	MLFlow non-H2O.ai models created with Python 3.9	MLflow Model Scorer [Python 3.9]	python-scorer_mlflow_39
h2o3_mojo	H2O-3 MOJO models	H2O.ai MOJO scorer	h2o3_mojo_runtime
dai_mojo	DAI MOJO models (Java runtime)	H2O.ai MOJO scorer	dai_mojo_runtime
dai_mojo	DAI MOJO models (Java runtime) - with Shapley contributions for original features	DAI MOJO Scorer (Shapley original only)	mojo_runtime_shapley_original
dai_mojo	DAI MOJO models (Java runtime) - with Shapley contributions for transformed features	DAI MOJO Scorer (Shapley transformed only)	mojo_runtime_shapley_transformed
dai_mojo	DAI MOJO models (Java runtime) - with Shapley contributions for both original and transformed features	DAI MOJO Scorer (Shapley all)	mojo_runtime_shapley_all
dai_mojo	DAI MOJO models (C++ runtime) - supports all Shapley contribution types and is expected to have significantly lower memory usage	DAI MOJO Scorer (C++ Runtime)	dai-mojo-cpp_experimental
dai_python_scoring_pipeline	DAI Python Scoring Pipeline models created by DAI 1.9.3	Python Pipeline Scorer [DAI 1.9.3]	python-scorer_dai_pipelines_193
dai_python_scoring_pipeline	DAI Python Scoring Pipeline models created by DAI 1.10.0	Python Pipeline Scorer [DAI 1.10.0]	python-scorer_dai_pipelines_110
dai_python_scoring_pipeline	DAI Python Scoring Pipeline models created by DAI 1.10.1	Python Pipeline Scorer [DAI 1.10.1]	python-scorer_dai_pipelines_1101
dai_python_scoring_pipeline	DAI Python Scoring Pipeline models created by DAI 1.10.2	Python Pipeline Scorer [DAI 1.10.2]	python-scorer_dai_pipelines_1102
dai_python_scoring_pipeline	DAI Python Scoring Pipeline models created by DAI 1.10.3	Python Pipeline Scorer [DAI 1.10.3]	python-scorer_dai_pipelines_1103
dai_python_scoring_pipeline	DAI Python Scoring Pipeline models created by DAI 1.10.3.1	Python Pipeline Scorer [DAI 1.10.3.1]	python-scorer_dai_pipelines_11031
dai_python_scoring_pipeline	DAI Python Scoring Pipeline models created by DAI 1.10.3.2	Python Pipeline Scorer [DAI 1.10.3.2]	python-scorer_dai_pipelines_11032
dai_python_scoring_pipeline	DAI Python Scoring Pipeline models created by DAI 1.10.4	Python Pipeline Scorer [DAI 1.10.4]	python-scorer_dai_pipelines_1104
dai_python_scoring_pipeline	DAI Python Scoring Pipeline models created by DAI 1.10.4.1	Python Pipeline Scorer [DAI 1.10.4.1]	python-scorer_dai_pipelines_11041
dai_python_scoring_pipeline	DAI Python Scoring Pipeline models created by DAI 1.10.4.2	Python Pipeline Scorer [DAI 1.10.4.2]	python-scorer_dai_pipelines_11042
dai_python_scoring_pipeline	DAI Python Scoring Pipeline models created by DAI 1.10.4.3	Python Pipeline Scorer [DAI 1.10.4.3]	python-scorer_dai_pipelines_11043

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