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ptibench

README.md

PTIBench: PyTorch Inference Server Benchmark

About

This repository is a playground to benchmark several existing (and popular) model serving frameworks for PyTorch.
It also acts as a reference kit to generate, run and benchmark your own model in available frameworks.

Models

Model Name Precision GPU Type
EfficientNet-B0 FP32 True NVIDIA Triton Inference Server
EfficientNet-B0 FP32 True PyTorch Serve
EfficientNet-B0 FP32 True Vanilla Python gRPC

Results

Note: These results are captured on a machine with Intel i7 CPU, 16GB RAM and NVIDIA RTX 3050Ti GPU using Locust with 1000 users for 5 minutes.

Type Response Time Total Requests (Request Per Second)
Vanilla Python gRPC Vanilla Python gRPC 17340 (43.5)
PyTorch Serve PyTorch Serve 17493 (44.9)
NVIDIA Triton Inference Server NVIDIA Triton Inference Server 28133 (71.9)

Generate and Usage

This section will help you compile and generate modules which can be served.

  • Compile and generate assets to be used for serving using the command:
cd models/efficientnet-b0
docker run --rm -it --gpus all -v ${PWD}:/scratch_space nvcr.io/nvidia/pytorch:<xx.yy>-py3 # e.g. <xx.yy> = 22.05
cd /scratch_space
python3 conversion.py
exit
  • Copy the outputs in their respective directory for usage:
mv model.pt ../services/triton/model_repository/efficientnet_b0/1/model.pt
cp ts_model.pt ../services/torchserve/ts_model.pt
cp ts_model.pt ../services/python-grpc/ts_model.pt

NVIDIA Triton Inference Server

  • Run NVIDIA Triton Inference Server using:
cd services/triton
docker run --gpus all --rm -p 8000:8000 -p 8001:8001 -p 8002:8002 -v <absolute_path_to_ptibench_directory>/model_repository:/models \
    nvcr.io/nvidia/tritonserver:<xx.yy>-py3 tritonserver --model-repository=/models # e.g. <xx.yy> = 22.05

TorchServe

  • Generate MAR for running TorchServe:
cd services/torchserve
torch-model-archiver --model-name efficientnet_b0 --version 1.0 --serialized-file ts_model.pt --handler handler.py
  • Run TorchServe using:
docker run --rm -it --gpus all -p 8080:8080 -p 8081:8081 -v ${PWD}/model-store:/home/model-server/model-store \
    pytorch/torchserve:latest-gpu torchserve --model-store /home/model-server/model-store/ --models efficientnet_b0=efficientnet_b0.mar

Python gRPC

  • Generate Docker Image using:
cd services/python-grpc
docker build . -t torch-python-grpc
  • Run it in a container using:
docker run --rm --gpus all -p 8080:8080 torch-python-grpc

Running Benchmarks

This section will help you run benchmark and save results

NVIDIA Triton Inference Server

  • Run locust load tests using
cd services/triton
locust -f locust_client.py

TorchServe

  • Run locust load tests using
cd services/torchserve
locust -f locust_client.py

Python gRPC

  • Run locust load tests using
cd services/python-grpc
locust -f locust_client.py

TODOs/Improvements

  • Refactor the clients of locust testing to be consistent across all services
  • Run benchmarks weekly/on latest versions using common GPU spec
  • Use FP16 for inference as well
  • Generate some fancy graphs using the saved results
  • Improve the way load testing is done using locust

License

This project is licensed under MIT License.