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@jeet4320 jeet4320 commented May 1, 2021

Issue #, if available:

PR Checklist

  • I've prepended PR tag with frameworks/job this applies to : [mxnet, tensorflow, pytorch] | [ei/neuron] | [build] | [test] | [benchmark] | [ec2, ecs, eks, sagemaker]
  • (If applicable) I've documented below the DLC image/dockerfile this relates to
  • (If applicable) I've documented below the tests I've run on the DLC image
  • (If applicable) I've reviewed the licenses of updated and new binaries and their dependencies to make sure all licenses are on the Apache Software Foundation Third Party License Policy Category A or Category B license list. See https://www.apache.org/legal/resolved.html.
  • (If applicable) I've scanned the updated and new binaries to make sure they do not have vulnerabilities associated with them.

Pytest Marker Checklist

  • (If applicable) I have added the marker @pytest.mark.model("<model-type>") to the new tests which I have added, to specify the Deep Learning model that is used in the test (use "N/A" if the test doesn't use a model)
  • (If applicable) I have added the marker @pytest.mark.integration("<feature-being-tested>") to the new tests which I have added, to specify the feature that will be tested
  • (If applicable) I have added the marker @pytest.mark.multinode(<integer-num-nodes>) to the new tests which I have added, to specify the number of nodes used on a multi-node test
  • (If applicable) I have added the marker @pytest.mark.processor(<"cpu"/"gpu"/"eia"/"neuron">) to the new tests which I have added, if a test is specifically applicable to only one processor type

EIA/NEURON Checklist

  • When creating a PR:
  • I've modified src/config/build_config.py in my PR branch by setting ENABLE_EI_MODE = True or ENABLE_NEURON_MODE = True
  • When PR is reviewed and ready to be merged:
  • I've reverted the code change on the config file mentioned above

Benchmark Checklist

  • When creating a PR:
  • I've modified src/config/test_config.py in my PR branch by setting ENABLE_BENCHMARK_DEV_MODE = True
  • When PR is reviewed and ready to be merged:
  • I've reverted the code change on the config file mentioned above

Reviewer Checklist

  • For reviewer, before merging, please cross-check:
  • I've verified the code change on the config file mentioned above has already been reverted

Description:

Tests run:

DLC image/dockerfile:

Additional context:

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license. I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

if job_type == "training":
if (framework == "tensorflow" and framework_major_version >= "2") or framework == "pytorch":
efa_dedicated = os.getenv("EFA_DEDICATED", "False").lower() == "true"
efa_flag = '--efa' if efa_dedicated else '-m \"not efa\"'
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added double quotes

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jeet4320 commented May 2, 2021

Tested all scenarios locally and it is working now. Also on first commit all non-efa tests ran.

@jeet4320 jeet4320 merged commit 1d92ebd into aws:master May 2, 2021
@jeet4320 jeet4320 deleted the add-back-efa branch May 2, 2021 13:54
jeet4320 added a commit to jeet4320/deep-learning-containers that referenced this pull request May 3, 2021
* aws/master:
  Skip temporarily to revert it (aws#1087)
  Add back efa configs to SM tests (aws#1086)
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