deep-learning-containers [Pytorch] | [Training] Re-release PT SM 1.12 containers with latest SM Training toolkit package

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.

Description

This PR would include latest SMTT package which provides a fix for the ORTE lost communication issue while using mpi.

Tests run

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:

[x] Revision A: sagemaker_remote_tests = "standard"
[ ] Revision B: sagemaker_remote_tests = "rc"
[ ] Revision C: sagemaker_remote_tests = "efa"

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

[x] sagemaker_local_tests = true

DLC image/dockerfile

Additional context

Label Checklist

[ ] I have added the project label for this PR (<project_name> or "Improvement")

PR Checklist

[ ] 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

[ ] (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/GRAVITON Testing Checklist

When creating a PR:

[ ] I've modified dlc_developer_config.toml in my PR branch by setting ei_mode = true, 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 benchmark_mode = true

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.

Sep 10 '22 00:09 satishpasumarthi

Please upgrade the sagemaker version in the GPU dockerfile. It is pinned currently

Sep 14 '22 20:09 tejaschumbalkar

Feel free to reopen if you are actively working on the PR

Mar 22 '23 06:03 tejaschumbalkar