[HWORKS-2476] Add support for configuring environment variables in Deployments

Author: robzor92

python/hsml/deployment.py

@@ -539,9 +539,18 @@ def environment(self):
     def environment(self, environment: str):
         self._predictor.environment = environment
 
+    @property
+    def env_vars(self):
+        """Environment variables of the deployment"""
+        return self._predictor.env_vars
+
+    @env_vars.setter
+    def env_vars(self, env_vars: str):
+        self._predictor.env_vars = env_vars
+
     @property
     def project_namespace(self):
-        """Name of inference environment"""
+        """Name of kubernetes namespace the project is in"""
         return self._predictor.project_namespace
 
     @project_namespace.setter

python/hsml/model.py

@@ -208,6 +208,7 @@ def deploy(
         transformer: Optional[Union[Transformer, dict]] = None,
         api_protocol: Optional[str] = IE.API_PROTOCOL_REST,
         environment: Optional[str] = None,
+        env_vars: Optional[dict] = None,
     ) -> deployment.Deployment:
         """Deploy the model.
 
@@ -266,6 +267,7 @@ def deploy(
             transformer=transformer,
             api_protocol=api_protocol,
             environment=environment,
+            env_vars=env_vars,
         )
 
         return predictor.deploy()

python/hsml/model_serving.py

@@ -171,6 +171,7 @@ def create_predictor(
         transformer: Optional[Union[Transformer, dict]] = None,
         api_protocol: Optional[str] = IE.API_PROTOCOL_REST,
         environment: Optional[str] = None,
+        env_vars: Optional[dict] = None,
     ) -> Predictor:
         """Create a Predictor metadata object.
 
@@ -212,6 +213,7 @@ def create_predictor(
             transformer: Transformer to be deployed together with the predictor.
             api_protocol: API protocol to be enabled in the deployment (i.e., 'REST' or 'GRPC'). Defaults to 'REST'.
             environment: The project Python environment to use
+            env_vars: Additional environment variables to use
 
         # Returns
             `Predictor`. The predictor metadata object.
@@ -232,6 +234,7 @@ def create_predictor(
             transformer=transformer,
             api_protocol=api_protocol,
             environment=environment,
+            env_vars=env_vars,
         )
 
     @usage.method_logger
@@ -313,6 +316,7 @@ def create_endpoint(
         inference_batcher: Optional[Union[InferenceBatcher, dict]] = None,
         api_protocol: Optional[str] = IE.API_PROTOCOL_REST,
         environment: Optional[str] = None,
+        env_vars: Optional[dict] = None,
     ) -> Predictor:
         """Create an Entrypoint metadata object.
 
@@ -341,6 +345,7 @@ def create_endpoint(
             inference_batcher: Inference batcher configuration.
             api_protocol: API protocol to be enabled in the deployment (i.e., 'REST' or 'GRPC'). Defaults to 'REST'.
             environment: The project Python environment to use
+            env_vars: Additional environment variables to use
 
         # Returns
             `Predictor`. The predictor metadata object.
@@ -355,6 +360,7 @@ def create_endpoint(
             inference_batcher=inference_batcher,
             api_protocol=api_protocol,
             environment=environment,
+            env_vars=env_vars,
         )
 
     @usage.method_logger

python/hsml/predictor.py

@@ -65,6 +65,7 @@ def __init__(
         api_protocol: Optional[str] = INFERENCE_ENDPOINTS.API_PROTOCOL_REST,
         environment: Optional[str] = None,
         project_namespace: str = None,
+        env_vars: Optional[dict] = None,
         **kwargs,
     ):
         serving_tool = (
@@ -104,6 +105,7 @@ def __init__(
         self._environment = environment
         self._project_namespace = project_namespace
         self._project_name = None
+        self._env_vars = env_vars
 
     def deploy(self):
         """Create a deployment for this predictor and persists it in the Model Serving.
@@ -302,6 +304,8 @@ def extract_fields_from_json(cls, json_decamelized):
         if "environment_dto" in json_decamelized:
             environment = json_decamelized.pop("environment_dto")
             kwargs["environment"] = environment["name"]
+        if "env_vars" in json_decamelized:
+            kwargs["env_vars"] = json_decamelized.pop("env_vars")
         kwargs["project_namespace"] = json_decamelized.pop("project_namespace")
         return kwargs
 
@@ -337,6 +341,8 @@ def to_dict(self):
             json = {**json, **{"modelVersion": self._model_version}}
         if self.model_framework is not None:
             json = {**json, **{"modelFramework": self._model_framework}}
+        if self.env_vars is not None:
+            json = {**json, **{"envVars": self._env_vars}}
         if self.environment is not None:
             json = {**json, **{"environmentDTO": {"name": self._environment}}}
         if self._resources is not None:
@@ -533,6 +539,15 @@ def api_protocol(self):
     def api_protocol(self, api_protocol):
         self._api_protocol = api_protocol
 
+    @property
+    def env_vars(self):
+        """Environment variables of the inference environment"""
+        return self._env_vars
+
+    @env_vars.setter
+    def env_vars(self, env_vars):
+        self._env_vars = env_vars
+
     @property
     def environment(self):
         """Name of the inference environment"""