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update EFA version in cu130 base image#5468

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Yadan-Wei merged 11 commits intoaws:masterfrom
DevakiBolleneni:efa-base-update
Nov 12, 2025
Merged

update EFA version in cu130 base image#5468
Yadan-Wei merged 11 commits intoaws:masterfrom
DevakiBolleneni:efa-base-update

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@DevakiBolleneni
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@DevakiBolleneni DevakiBolleneni commented Nov 11, 2025

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

Tests Run

  • 3ed4b37 Passed with base-cuda 129 image.
  • a9faf7c Passed with base-cuda130 image.

By default, docker image builds and tests are disabled. Two ways to run builds and tests:

  1. Using dlc_developer_config.toml
  2. Using this PR description (currently only supported for PyTorch, TensorFlow, vllm, and base images)
How to use the helper utility for updating dlc_developer_config.toml

Assuming your remote is called origin (you can find out more with git remote -v)...

  • Run default builds and tests for a particular buildspec - also commits and pushes changes to remote; Example:

python src/prepare_dlc_dev_environment.py -b </path/to/buildspec.yml> -cp origin

  • Enable specific tests for a buildspec or set of buildspecs - also commits and pushes changes to remote; Example:

python src/prepare_dlc_dev_environment.py -b </path/to/buildspec.yml> -t sanity_tests -cp origin

  • Restore TOML file when ready to merge

python src/prepare_dlc_dev_environment.py -rcp origin

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

  • sagemaker_remote_tests = true
  • sagemaker_efa_tests = true
  • sagemaker_rc_tests = true
  • sagemaker_local_tests = true
How to use PR description Use the code block below to uncomment commands and run the PR CodeBuild jobs. There are two commands available:
  • # /buildspec <buildspec_path>
    • e.g.: # /buildspec pytorch/training/buildspec.yml
    • If this line is commented out, dlc_developer_config.toml will be used.
  • # /tests <test_list>
    • e.g.: # /tests sanity security ec2
    • If this line is commented out, it will run the default set of tests (same as the defaults in dlc_developer_config.toml): sanity, security, ec2, ecs, eks, sagemaker, sagemaker-local.
# /buildspec <buildspec_path>
# /tests <test_list>

Formatting

PR Checklist

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  • 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.

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.

@DevakiBolleneni DevakiBolleneni requested a review from a team as a code owner November 11, 2025 01:13
@aws-deep-learning-containers-ci aws-deep-learning-containers-ci bot added authorized Size:XS Determines the size of the PR labels Nov 11, 2025
@aws-deep-learning-containers-ci aws-deep-learning-containers-ci bot added the Size:S Determines the size of the PR label Nov 11, 2025
@Yadan-Wei
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Looks good to me. Next time please add your passed test commit link under Tests Run parts for easier review. I add them this time. After reverting the toml file, I can approve the PR.

@Yadan-Wei Yadan-Wei merged commit 3622cc2 into aws:master Nov 12, 2025
28 checks passed
@DevakiBolleneni DevakiBolleneni deleted the efa-base-update branch November 20, 2025 23:04
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