FARM provides an automated speed benchmarking pipeline with options to parameterize the benchmarks with batch_size, max sequence length, document size, and so on.

The pipeline is implemented using pytest-benchmark. The warmup/iterations for each benchmark are configurable and the results can be exported to a JSON file.

The benchmarks/question_answering.py file contains tests for inference with PyTorch(test_question_answering_pytorch) and ONNXRuntime(test_question_answering_onnx).

The benchmarks are available here.

For running benchmark on a GPU, bash into the Docker Image using docker run -it --gpus all deepset/farm-onnxruntime-gpu:0.4.3 bash. Once inside the container, execute cd FARM/test && pytest benchmarks/question_answering.py -k test_question_answering_pytorch --use_gpu --benchmark-json result.json.

Bash into the Docker container with docker run -it deepset/farm-inference-api:0.4.3 bash and then execute cd test && pytest benchmarks/question_answering.py -k test_question_answering_pytorch --benchmark-json result.json.

The results of benchmarks are exported to a result.json file in the test folder. To convert results to csv format, execute python benchmarks/convert_result_to_csv.py.