mlcommons / mlcommons/inference

CM script failed to run harness after docker done

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Python
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Description

Hi @arjunsuresh

I am running the Resnet50 benchmark with the command:

cm run script --tags=run-mlperf,inference,_find-performance,_full,_r4.1-dev
--model=resnet50
--implementation=nvidia
--framework=tensorrt
--category=edge
--scenario=Offline
--execution_mode=test
--device=cuda
--docker --quiet
--test_query_count=5000

failed to run harness as below and the docker was created successfully. How to resolve it?
log with dock done.txt

make: *** [Makefile:45: run_harness] Error 1

CM error: Portable CM script failed (name = benchmark-program, return code = 512)


^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Note that it is often a portability issue of a third-party tool or a native script
wrapped and unified by this CM script (automation recipe). Please re-run
this script with --repro flag and report this issue with the original
command line, cm-repro directory and full log here:

https://github.com/mlcommons/cm4mlops/issues

The CM concept is to collaboratively fix such issues inside portable CM scripts
to make existing tools and native scripts more portable, interoperable
and deterministic. Thank you!
cmuser@9951fc73ce5b:~$ pwd
/home/cmuser

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reviewing the attached log and the run_harness target at Makefile:45, then reproduce the supplied cm run script command with --repro as requested. Done means the Docker-created benchmark proceeds through the harness without the reported Error 1 and the resulting failure is documented or fixed.

Written by the indexing model from the issue text.

Assessment

Tech stack
docker, python
Domain
machine-learning
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
30/100

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