Fail to analyze ensemble model: "inference.ModelConfig" should not have multiple "scheduling_choice" oneof fields
See original GitHub issueWhen I use model-analyzer to analyze a ensemble model with local luanch mode, it always fails with following error:
root@dl:/inference# model-analyzer profile --checkpoint-directory checkpoints -m $PWD/model_repo --profile-models quartznet-ensemble --output-model-repository-path=/output_repo/temp --override-output-model-repository --client-protocol grpc --run-config-search-max-concurrency 800 --run-config-search-max-instance-count 2 --run-config-search-max-preferred-batch-size 64
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/google/protobuf/json_format.py", line 538, in _ConvertFieldValuePair
raise ParseError('Message type "{0}" should not have multiple '
google.protobuf.json_format.ParseError: Message type "inference.ModelConfig" should not have multiple "scheduling_choice" oneof fields.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/bin/model-analyzer", line 8, in <module>
sys.exit(main())
File "/usr/local/lib/python3.8/dist-packages/model_analyzer/entrypoint.py", line 315, in main
analyzer.profile(client=client)
File "/usr/local/lib/python3.8/dist-packages/model_analyzer/analyzer.py", line 104, in profile
self._model_manager.run_model(model=model)
File "/usr/local/lib/python3.8/dist-packages/model_analyzer/model_manager.py", line 84, in run_model
self._run_model_with_search(model)
File "/usr/local/lib/python3.8/dist-packages/model_analyzer/model_manager.py", line 138, in _run_model_with_search
self._run_model_config_sweep(model, search_model_config=True)
File "/usr/local/lib/python3.8/dist-packages/model_analyzer/model_manager.py", line 167, in _run_model_config_sweep
self._run_config_generator.generate_run_config_for_model_sweep(
File "/usr/local/lib/python3.8/dist-packages/model_analyzer/config/run/run_config_generator.py", line 98, in generate_run_config_for_model_sweep
model_config = ModelConfig.create_from_dictionary(
File "/usr/local/lib/python3.8/dist-packages/model_analyzer/triton/model/model_config.py", line 117, in create_from_dictionary
protobuf_message = json_format.ParseDict(model_dict,
File "/usr/local/lib/python3.8/dist-packages/google/protobuf/json_format.py", line 454, in ParseDict
parser.ConvertMessage(js_dict, message)
File "/usr/local/lib/python3.8/dist-packages/google/protobuf/json_format.py", line 485, in ConvertMessage
self._ConvertFieldValuePair(value, message)
File "/usr/local/lib/python3.8/dist-packages/google/protobuf/json_format.py", line 599, in _ConvertFieldValuePair
raise ParseError(str(e))
google.protobuf.json_format.ParseError: Message type "inference.ModelConfig" should not have multiple "scheduling_choice" oneof fields.
The model repository I used can be downloaded here.
Issue Analytics
- State:
- Created 2 years ago
- Reactions:1
- Comments:7 (3 by maintainers)
Top Results From Across the Web
nvidia_inferenceserver - Go Packages
GRPCInferenceServiceClient is the client API for GRPCInferenceService service. For semantics around ctx use and closing/ending streaming RPCs, please refer to ...
Read more >Getting Started MovieLens: Serving a TensorFlow Model
Before we get started, you should launch the Triton Inference Server docker ... Below, we will request the Triton server to load the...
Read more >Top Related Medium Post
No results found
Top Related StackOverflow Question
No results found
Troubleshoot Live Code
Lightrun enables developers to add logs, metrics and snapshots to live code - no restarts or redeploys required.
Start FreeTop Related Reddit Thread
No results found
Top Related Hackernoon Post
No results found
Top Related Tweet
No results found
Top Related Dev.to Post
No results found
Top Related Hashnode Post
No results found
Top GitHub Comments
@dhaval24 it’s on our short term roadmap since it is a high priority feature. I can share a more granular timeline with you over the email thread we have
@okanlv We don’t have any updates regarding the ensemble support. We’ll update this issue as soon as more information is available.