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Run this benchmark via CM

Do a test run to detect and record the system performance

cm run script --tags=run-mlperf,inference,_find-performance,_all-scenarios \
--model=bert-99 --implementation=reference --device=cpu --backend=onnxruntime \
--category=edge --division=open --quiet --rerun
  • Use --device=cuda to run the inference on Nvidia GPU and --device=rocm to run on AMD GPUs
  • Use --division=closed to run all scenarios for the closed division (compliance tests are skipped for _find-performance mode)
  • Use --category=datacenter to run datacenter scenarios
  • Use --backend=pytorch and --backend=tf to use the pytorch and tensorflow backends respectively. --backend=deepsparse will run the sparse int8 model using deepsparse backend (not allowed to be submitted under closed division).
  • Use --model=bert-99.9 to run the high accuracy constraint bert-99 model. But since we are running the fp32 model, this is redundant and instead, we can reuse the results of bert-99 for bert-99.9

Do full accuracy and performance runs for all the scenarios

cm run script --tags=run-mlperf,inference,_submission,_all-scenarios --model=bert-99 \
--device=cpu --implementation=reference --backend=onnxruntime \
--execution-mode=valid --results_dir=$HOME/results_dir \
--category=edge --division=open --quiet
  • Use --power=yes for measuring power. It is ignored for accuracy and compliance runs
  • Use --division=closed to run all scenarios for the closed division including the compliance tests
  • --offline_target_qps, --server_target_qps, and --singlestream_target_latency can be used to override the determined performance numbers
  • --rerun flag can be used to force a rerun even when a valid result exists in the results_dir

Populate the README files describing your submission

cmr "run-mlperf inference _populate-readme _all-scenarios" \
--model=bert-99 --device=cpu --implementation=reference --backend=onnxruntime \
--execution-mode=valid --results_dir=$HOME/results_dir \
--category=edge --division=open --quiet

Generate and upload MLPerf submission

Follow this guide to generate the submission tree and upload your results.

Questions? Suggestions?

Check the MLCommons Task Force on Automation and Reproducibility and get in touch via public Discord server.