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Skip EFA tests for PT 1.13 #3908

Merged
merged 18 commits into from
May 13, 2024
Merged

Skip EFA tests for PT 1.13 #3908

merged 18 commits into from
May 13, 2024

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sirutBuasai
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@sirutBuasai sirutBuasai commented May 11, 2024

GitHub Issue #, if available:

Note:

  • If merging this PR should also close the associated Issue, please also add that Issue # to the Linked Issues section on the right.

  • All PR's are checked weekly for staleness. This PR will be closed if not updated in 30 days.

Description

  • EFA currently not supported in PT 1.13
  • GDRCopy not added to PT 1.13 EC2 images
  • Increase timeout for ddp test

Tests run

NOTE: By default, docker builds are disabled. In order to build your container, please update dlc_developer_config.toml and specify the framework to build in "build_frameworks"

  • I have run builds/tests on commit for my changes.

NOTE: If you are creating a PR for a new framework version, please ensure success of the standard, rc, and efa sagemaker remote tests by updating the dlc_developer_config.toml file:

Expand
  • sagemaker_remote_tests = true
  • sagemaker_efa_tests = true
  • sagemaker_rc_tests = true

Additionally, please run the sagemaker local tests in at least one revision:

  • sagemaker_local_tests = true

Formatting

DLC image/dockerfile

Builds to Execute

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Click the checkbox to enable a build to execute upon merge.

Note: By default, pipelines are set to "latest". Replace with major.minor framework version if you do not want "latest".

  • build_pytorch_training_1.13_ec2
  • build_pytorch_training_1.13_sm
  • build_pytorch_inference_latest
  • build_tensorflow_training_latest
  • build_tensorflow_inference_latest

Additional context

PR Checklist

Expand
  • I've prepended PR tag with frameworks/job this applies to : [mxnet, tensorflow, pytorch] | [ei/neuron/graviton] | [build] | [test] | [benchmark] | [ec2, ecs, eks, sagemaker]
  • If the PR changes affects SM test, I've modified dlc_developer_config.toml in my PR branch by setting sagemaker_tests = true and efa_tests = true
  • If this PR changes existing code, the change fully backward compatible with pre-existing code. (Non backward-compatible changes need special approval.)
  • (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.

NEURON/GRAVITON Testing Checklist

  • When creating a PR:
  • I've modified dlc_developer_config.toml in my PR branch by setting neuron_mode = true or graviton_mode = true

Benchmark Testing Checklist

  • When creating a PR:
  • I've modified dlc_developer_config.toml in my PR branch by setting ec2_benchmark_tests = true or sagemaker_benchmark_tests = true

Pytest Marker Checklist

Expand
  • (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

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.

@sirutBuasai sirutBuasai requested review from a team as code owners May 11, 2024 06:22
@aws-deep-learning-containers-ci aws-deep-learning-containers-ci bot added ec2 Reflects file change in dlc_tests/ec2 folder sagemaker_tests Size:S Determines the size of the PR test Reflects file change in test folder labels May 11, 2024
@aws-deep-learning-containers-ci aws-deep-learning-containers-ci bot added the Size:XS Determines the size of the PR label May 11, 2024
@sirutBuasai sirutBuasai enabled auto-merge (squash) May 11, 2024 20:14
@@ -76,6 +78,12 @@ def test_pytorch_efa(
:param region: str Region in which EFA-enabled instances are launched
:param gpu_only: pytest fixture to limit test only to GPU DLCs
"""

# NOTE: Skip PT1.13 autopatching
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putting skip condition inside the test function is not a good idea, because test setup will still launch instances. Instead, you can probably use an existing fixture like pt21_and_above_only

arjkesh
arjkesh previously approved these changes May 13, 2024
@sirutBuasai sirutBuasai disabled auto-merge May 13, 2024 19:47
@aws-deep-learning-containers-ci aws-deep-learning-containers-ci bot added the build Reflects file change in build folder label May 13, 2024
@aws-deep-learning-containers-ci aws-deep-learning-containers-ci bot added the pytorch Reflects file change in pytorch folder label May 13, 2024
@sirutBuasai sirutBuasai enabled auto-merge (squash) May 13, 2024 19:59
@sirutBuasai sirutBuasai merged commit 4714fd1 into aws:master May 13, 2024
28 checks passed
@sirutBuasai sirutBuasai deleted the 1.13-efa branch May 13, 2024 22:12
evakravi pushed a commit to evakravi/deep-learning-containers that referenced this pull request Sep 5, 2024
* Skip EFA tests for PT 1.13

* formatting

* fix comment

* use pt201 and above fixture

* test 1.13

* revert toml

* revert toml

* revert gdrcopy skip

* revert unused efa import

* revert gdrcopy skip

* test sagemaker only fixture

* fix comments

* revert toml

* split ec2 sm buildspec

* split ec2 sm

* add sm buildspec

---------

Co-authored-by: arjkesh <[email protected]>
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2 participants