mlcommons
Add popper workflow for single stage detector and object detection
In this PR, we add a Popper workflow to the single stage detector and object detection benchmark, which makes it reproducible in a container native manner.
Through this PR,
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We cleaned up the scripts
download_dataset.sh,verify_dataset.shandrun_benchmark.shand also added few scripts likecollect_system_info.shto collect the details of the machine where the benchmark workflow is being run,extract_dataset.shto extract the downloaded dataset only if it is verified,report_result.shto generate a report that complies with the MLPerf training rules. All the scripts have been moved to thescripts/directory. -
A
wf.ymlfile has been added to the benchmark directories that describes the entire benchmark workflow in YML format. -
A
settings.pyconfiguration file has also been added which includes configuration for the Docker engine to use the NVIDIA runtime, access privileges of the container, etc. -
We also modified the
README.mdof the benchmarks to include information about how to reproduce the workflow using Popper.
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This PR seems to refer to 2 different benchmarks: the object_detection benchmark, and the now-retired ssd-v1 benchmark. I'm not sure whether we want to incorporate the changes to the object_detection benchmark (I don't know what "popper" is or whether the committee requested this change). If we do decide to incorporate the change for the object_detection benchmark, though, we should delete the part of the PR devoted to the retired ssd-v1 benchmark.
@JayjeetAtGithub do you believe this (embarrassingly old) PR is still relevant? If so, can you address Matt's comments above? If not, can we close?
