[HWORKS-1048] Support multiple modularized project environments

python/hsml/deployment.py

@@ -461,6 +461,15 @@ def api_protocol(self):
     def api_protocol(self, api_protocol: str):
         self._predictor.api_protocol = api_protocol
 
+    @property
+    def environment(self):
+        """Name of inference environment"""
+        return self._predictor.environment
+
+    @environment.setter
+    def environment(self, environment: str):
+        self._predictor.environment = environment
+
     def __repr__(self):
         desc = (
             f", description: {self._description!r}"

python/hsml/engine/model_engine.py

@@ -14,7 +14,6 @@
 #   limitations under the License.
 #
 
-import importlib
 import json
 import os
 import tempfile
@@ -24,7 +23,7 @@
 from hsml import client, constants, util
 from hsml.client.exceptions import ModelRegistryException, RestAPIError
 from hsml.core import dataset_api, model_api
-from hsml.engine import hopsworks_engine, local_engine
+from hsml.engine import local_engine
 from tqdm.auto import tqdm
 
 
@@ -33,11 +32,7 @@ def __init__(self):
         self._model_api = model_api.ModelApi()
         self._dataset_api = dataset_api.DatasetApi()
 
-        pydoop_spec = importlib.util.find_spec("pydoop")
-        if pydoop_spec is None:
-            self._engine = local_engine.LocalEngine()
-        else:
-            self._engine = hopsworks_engine.HopsworksEngine()
+        self._engine = local_engine.LocalEngine()
 
     def _poll_model_available(self, model_instance, await_registration):
         if await_registration > 0:

python/hsml/model.py

@@ -173,6 +173,7 @@ def deploy(
         inference_batcher: Optional[Union[InferenceBatcher, dict]] = None,
         transformer: Optional[Union[Transformer, dict]] = None,
         api_protocol: Optional[str] = IE.API_PROTOCOL_REST,
+        environment: Optional[str] = None,
     ):
         """Deploy the model.
 
@@ -203,6 +204,7 @@ def deploy(
             inference_batcher: Inference batcher configuration.
             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 inference environment to use.
 
         # Returns
             `Deployment`: The deployment metadata object of a new or existing deployment.
@@ -223,6 +225,7 @@ def deploy(
             inference_batcher=inference_batcher,
             transformer=transformer,
             api_protocol=api_protocol,
+            environment=environment,
         )
 
         return predictor.deploy()

python/hsml/model_serving.py

@@ -285,7 +285,7 @@ def postprocess(self, outputs):
 
         return Transformer(script_file=script_file, resources=resources)
 
-    def create_deployment(self, predictor: Predictor, name: Optional[str] = None):
+    def create_deployment(self, predictor: Predictor, name: Optional[str] = None, environment: Optional[str] = None):
         """Create a Deployment metadata object.
 
         !!! example
@@ -348,12 +348,13 @@ def create_deployment(self, predictor: Predictor, name: Optional[str] = None):
         # Arguments
             predictor: predictor to be used in the deployment
             name: name of the deployment
+            environment: The inference environment to use
 
         # Returns
             `Deployment`. The model metadata object.
         """
 
-        return Deployment(predictor=predictor, name=name)
+        return Deployment(predictor=predictor, name=name, environment=environment)
 
     @property
     def project_name(self):

python/hsml/predictor.py

@@ -56,6 +56,7 @@ def __init__(
         created_at: Optional[str] = None,
         creator: Optional[str] = None,
         api_protocol: Optional[str] = INFERENCE_ENDPOINTS.API_PROTOCOL_REST,
+        environment: Optional[str] = None,
         **kwargs,
     ):
         serving_tool = (
@@ -91,6 +92,7 @@ def __init__(
         self._transformer = util.get_obj_from_json(transformer, Transformer)
         self._validate_script_file(self._model_framework, self._script_file)
         self._api_protocol = api_protocol
+        self._environment = environment
 
     def deploy(self):
         """Create a deployment for this predictor and persists it in the Model Serving.
@@ -268,6 +270,9 @@ def extract_fields_from_json(cls, json_decamelized):
         kwargs["created_at"] = json_decamelized.pop("created")
         kwargs["creator"] = json_decamelized.pop("creator")
         kwargs["api_protocol"] = json_decamelized.pop("api_protocol")
+        if "environmentdto" in json_decamelized:
+            environment = json_decamelized.pop("environmentdto")
+            kwargs["environment"] = environment["name"]
         return kwargs
 
     def update_from_response_json(self, json_dict):
@@ -296,6 +301,8 @@ def to_dict(self):
             "predictor": self._script_file,
             "apiProtocol": self._api_protocol,
         }
+        if self.environment is not None:
+            json = {**json, **{"environmentDTO": {"name": self._environment}}}
         if self._resources is not None:
             json = {**json, **self._resources.to_dict()}
         if self._inference_logger is not None:
@@ -457,6 +464,15 @@ def api_protocol(self):
     def api_protocol(self, api_protocol):
         self._api_protocol = api_protocol
 
+    @property
+    def environment(self):
+        """Name of the inference environment"""
+        return self._environment
+
+    @environment.setter
+    def environment(self, environment):
+        self._environment = environment
+
     def __repr__(self):
         desc = (
             f", description: {self._description!r}"

python/tests/fixtures/predictor_fixtures.json

@@ -40,6 +40,9 @@
           "inference_logging": "ALL",
           "kafka_topic_dto": {
             "name": "topic"
+          },
+          "environment_dto": {
+            "name": "misc-inference-pipeline"
           }
         }
       ]
@@ -92,6 +95,9 @@
           "inference_logging": "ALL",
           "kafka_topic_dto": {
             "name": "topic"
+          },
+          "environment_dto": {
+            "name": "misc-inference-pipeline"
           }
         },
         {
@@ -131,6 +137,9 @@
           "inference_logging": "ALL",
           "kafka_topic_dto": {
             "name": "topic"
+          },
+          "environment_dto": {
+            "name": "misc-inference-pipeline"
           }
         }
       ]
@@ -160,6 +169,9 @@
       "inference_logging": "ALL",
       "kafka_topic_dto": {
         "name": "topic"
+      },
+      "environment_dto": {
+        "name": "tensorflow-inference-pipeline"
       }
     }
   },
@@ -200,6 +212,9 @@
       "inference_logging": "ALL",
       "kafka_topic_dto": {
         "name": "topic"
+      },
+      "environment_dto": {
+        "name": "tensorflow-inference-pipeline"
       }
     }
   },
@@ -235,6 +250,9 @@
       "inference_logging": "ALL",
       "kafka_topic_dto": {
         "name": "topic"
+      },
+      "environment_dto": {
+        "name": "misc-inference-pipeline"
       }
     }
   },
@@ -277,6 +295,9 @@
       },
       "kafka_topic_dto": {
         "name": "topic"
+      },
+      "environment_dto": {
+        "name": "misc-inference-pipeline"
       }
     }
   },
@@ -312,6 +333,9 @@
       },
       "kafka_topic_dto": {
         "name": "topic"
+      },
+      "environment_dto": {
+        "name": "misc-inference-pipeline"
       }
     }
   },
@@ -354,6 +378,9 @@
       },
       "kafka_topic_dto": {
         "name": "topic"
+      },
+      "environment_dto": {
+        "name": "misc-inference-pipeline"
       }
     }
   },

python/tests/test_model.py

@@ -202,7 +202,7 @@ def test_deploy(self, mocker, backend_fixtures):
             inference_logger=inference_logger,
             inference_batcher=inference_batcher,
             transformer=transformer,
-            api_protocol=p_json["api_protocol"],
+            environment=p_json["environment_dto"]["name"],
         )
 
         # Assert
@@ -218,6 +218,7 @@ def test_deploy(self, mocker, backend_fixtures):
             inference_batcher=inference_batcher,
             transformer=transformer,
             api_protocol=p_json["api_protocol"],
+            environment=p_json["environment_dto"]["name"],
         )
         mock_predictor.deploy.assert_called_once()