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TorchServe gRPC API

TorchServe also supports gRPC APIs for both inference and management calls.

TorchServe provides following gRPCs apis

  • Inference API

    • Ping : Gets the health status of the running server

    • Predictions : Gets predictions from the served model

  • Management API

    • RegisterModel : Serve a model/model-version on TorchServe

    • UnregisterModel : Free up system resources by unregistering specific version of a model from TorchServe

    • ScaleWorker : Dynamically adjust the number of workers for any version of a model to better serve different inference request loads.

    • ListModels : Query default versions of current registered models

    • DescribeModel : Get detail runtime status of default version of a model

    • SetDefault : Set any registered version of a model as default version

By default, TorchServe listens on port 7070 for the gRPC Inference API and 7071 for the gRPC Management API. To configure gRPC APIs on different ports refer configuration documentation

Python client example for gRPC APIs

Run following commands to Register, run inference and unregister, densenet161 model from TorchServe model zoo using gRPC python client.

git clone https://github.com/pytorch/serve
cd serve
  • Install gRPC python dependencies

pip install -U grpcio protobuf grpcio-tools
  • Start torchServe

mkdir model_store
torchserve --start 
  • Generate python gRPC client stub using the proto files

python -m grpc_tools.protoc --proto_path=frontend/server/src/main/resources/proto/ --python_out=ts_scripts --grpc_python_out=ts_scripts frontend/server/src/main/resources/proto/inference.proto frontend/server/src/main/resources/proto/management.proto
  • Register densenet161 model

python ts_scripts/torchserve_grpc_client.py register densenet161
  • Run inference using

python ts_scripts/torchserve_grpc_client.py infer densenet161 examples/image_classifier/kitten.jpg
  • Unregister densenet161 model

python ts_scripts/torchserve_grpc_client.py unregister densenet161

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