♻️ change name to get_inference_request

Signed-off-by: Prashant Gupta <[email protected]>
wgifford · Sep 21, 2023 · da67d77 · da67d77
1 parent 67aba55
commit da67d77
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Showing 4 changed files with 19 additions and 19 deletions.
diff --git a/caikit/runtime/__init__.py b/caikit/runtime/__init__.py
@@ -13,4 +13,4 @@
 # limitations under the License.
 
 # Local
-from .service_factory import get_request, get_train_params, get_train_request
+from .service_factory import get_inference_request, get_train_params, get_train_request
diff --git a/caikit/runtime/service_factory.py b/caikit/runtime/service_factory.py
@@ -269,7 +269,7 @@ def _get_and_filter_modules(
         return clean_modules
 
 
-def get_request(
+def get_inference_request(
     module_class: Type[ModuleBase],
     input_streaming: bool = False,
     output_streaming: bool = False,

diff --git a/examples/text-sentiment/client.py b/examples/text-sentiment/client.py
@@ -21,7 +21,7 @@
 
 # Local
 from caikit.config.config import get_config
-from caikit.runtime import get_request
+from caikit.runtime import get_inference_request
 from caikit.runtime.service_factory import ServicePackageFactory
 from text_sentiment.runtime_model import HuggingFaceSentimentModule
 import caikit
@@ -52,7 +52,7 @@
 
         # Run inference for two sample prompts
         for text in ["I am not feeling well today!", "Today is a nice sunny day"]:
-            request = get_request(HuggingFaceSentimentModule)(
+            request = get_inference_request(HuggingFaceSentimentModule)(
                 text_input=text
             ).to_proto()
             response = client_stub.HuggingFaceSentimentTaskPredict(

diff --git a/tests/runtime/test_grpc_server.py b/tests/runtime/test_grpc_server.py
@@ -49,7 +49,7 @@
     TrainingStatus,
     TrainingStatusResponse,
 )
-from caikit.runtime import get_request, get_train_params, get_train_request
+from caikit.runtime import get_inference_request, get_train_params, get_train_request
 from caikit.runtime.grpc_server import RuntimeGRPCServer
 from caikit.runtime.model_management.model_manager import ModelManager
 from caikit.runtime.protobufs import (
@@ -214,7 +214,7 @@ def test_predict_sample_module_ok_response(
 ):
     """Test RPC CaikitRuntime.SampleTaskPredict successful response"""
     stub = sample_inference_service.stub_class(runtime_grpc_server.make_local_channel())
-    predict_request = get_request(SampleModule)(
+    predict_request = get_inference_request(SampleModule)(
         sample_input=HAPPY_PATH_INPUT_DM
     ).to_proto()
 
@@ -244,7 +244,7 @@ def test_global_predict_build_caikit_library_request_dict_creates_caikit_core_ru
     assert proto_expected_arguments == set(proto_request_dict.keys())
 
     # pythonic data model request
-    predict_class = get_request(SampleModule)
+    predict_class = get_inference_request(SampleModule)
     python_request = predict_class(sample_input=HAPPY_PATH_INPUT_DM).to_proto()
 
     python_sample_module_request_dict = build_caikit_library_request_dict(
@@ -263,7 +263,7 @@ def test_predict_streaming_module(
 ):
     """Test RPC CaikitRuntime.StreamingTaskPredict successful response"""
     stub = sample_inference_service.stub_class(runtime_grpc_server.make_local_channel())
-    predict_class = get_request(
+    predict_class = get_inference_request(
         StreamingModule, input_streaming=False, output_streaming=True
     )
     predict_request = predict_class(sample_input=HAPPY_PATH_INPUT_DM).to_proto()
@@ -287,7 +287,7 @@ def test_predict_sample_module_error_response(
         stub = sample_inference_service.stub_class(
             runtime_grpc_server.make_local_channel()
         )
-        predict_class = get_request(SampleModule)
+        predict_class = get_inference_request(SampleModule)
         predict_request = predict_class(sample_input=HAPPY_PATH_INPUT_DM).to_proto()
 
         stub.SampleTaskPredict(
@@ -302,7 +302,7 @@ def test_rpc_validation_on_predict(
 ):
     """Check that the server catches models sent to the wrong task RPCs"""
     stub = sample_inference_service.stub_class(runtime_grpc_server.make_local_channel())
-    predict_class = get_request(OtherModule)
+    predict_class = get_inference_request(OtherModule)
     predict_request = predict_class(
         sample_input_sampleinputtype=HAPPY_PATH_INPUT_DM
     ).to_proto()
@@ -333,7 +333,7 @@ def test_rpc_validation_on_predict_for_unsupported_model(
         stub = sample_inference_service.stub_class(
             runtime_grpc_server.make_local_channel()
         )
-        predict_class = get_request(SampleModule)
+        predict_class = get_inference_request(SampleModule)
         predict_request = predict_class(sample_input=HAPPY_PATH_INPUT_DM).to_proto()
         with pytest.raises(grpc.RpcError) as context:
             stub.SampleTaskPredict(
@@ -366,7 +366,7 @@ def test_rpc_validation_on_predict_for_wrong_streaming_flavor(
             runtime_grpc_server.make_local_channel()
         )
 
-        predict_class = get_request(
+        predict_class = get_inference_request(
             SampleModule,
         )
         predict_request = predict_class(sample_input=HAPPY_PATH_INPUT_DM).to_proto()
@@ -423,7 +423,7 @@ def test_train_fake_module_ok_response_and_can_predict_with_trained_model(
     )
 
     # make sure the trained model can run inference
-    predict_class = get_request(SampleModule)
+    predict_class = get_inference_request(SampleModule)
     predict_request = predict_class(sample_input=HAPPY_PATH_INPUT_DM).to_proto()
     inference_response = inference_stub.SampleTaskPredict(
         predict_request, metadata=[("mm-model-id", actual_response.model_name)]
@@ -459,7 +459,7 @@ def test_train_fake_module_ok_response_with_loaded_model_can_predict_with_traine
     )
 
     # make sure the trained model can run inference
-    predict_class = get_request(CompositeModule)
+    predict_class = get_inference_request(CompositeModule)
     predict_request = predict_class(sample_input=HAPPY_PATH_INPUT_DM).to_proto()
     inference_response = inference_stub.SampleTaskPredict(
         predict_request, metadata=[("mm-model-id", actual_response.model_name)]
@@ -506,7 +506,7 @@ def test_train_fake_module_does_not_change_another_instance_model_of_block(
     )
 
     # make sure the trained model can run inference, and the batch size 100 was used
-    predict_class = get_request(OtherModule)
+    predict_class = get_inference_request(OtherModule)
     predict_request = predict_class(sample_input=HAPPY_PATH_INPUT_DM).to_proto()
     trained_inference_response = inference_stub.OtherTaskPredict(
         predict_request, metadata=[("mm-model-id", actual_response.model_name)]
@@ -566,7 +566,7 @@ def test_train_primitive_model(
     )
 
     # make sure the trained model can run inference
-    predict_class = get_request(SampleModule)
+    predict_class = get_inference_request(SampleModule)
     predict_request = predict_class(sample_input=HAPPY_PATH_INPUT_DM).to_proto()
 
     inference_response = inference_stub.SampleTaskPredict(
@@ -613,7 +613,7 @@ def test_train_fake_module_ok_response_with_datastream_jsondata(
     )
 
     # make sure the trained model can run inference
-    predict_class = get_request(SampleModule)
+    predict_class = get_inference_request(SampleModule)
     predict_request = predict_class(sample_input=HAPPY_PATH_INPUT_DM).to_proto()
     inference_response = inference_stub.SampleTaskPredict(
         predict_request, metadata=[("mm-model-id", actual_response.model_name)]
@@ -651,7 +651,7 @@ def test_train_fake_module_ok_response_with_datastream_csv_file(
     )
 
     # make sure the trained model can run inference
-    predict_class = get_request(SampleModule)
+    predict_class = get_inference_request(SampleModule)
     predict_request = predict_class(sample_input=HAPPY_PATH_INPUT_DM).to_proto()
     inference_response = inference_stub.SampleTaskPredict(
         predict_request, metadata=[("mm-model-id", actual_response.model_name)]
@@ -1116,7 +1116,7 @@ def test_metrics_stored_after_server_interrupt(
     with temp_config({"runtime": {"metering": {"enabled": True}}}, "merge"):
         with runtime_grpc_test_server(open_port) as server:
             stub = sample_inference_service.stub_class(server.make_local_channel())
-            predict_class = get_request(SampleModule)
+            predict_class = get_inference_request(SampleModule)
             predict_request = predict_class(sample_input=HAPPY_PATH_INPUT_DM).to_proto()
             _ = stub.SampleTaskPredict(
                 predict_request, metadata=[("mm-model-id", sample_task_model_id)]