[pytorch][build][sagemaker] new binaries for pytorch 1.10 which fix smdebugger issues

[ztlevi]

https://github.com/aws/deep-learning-containers/issues/1782

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

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:

• [ ] 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:

• [ ] sagemaker_local_tests = true

DLC image/dockerfile

Additional context

Label Checklist

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

PR Checklist

• [x] 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.

[ztlevi]

/rerun

[ydaiming]

/rerun

[ztlevi]

/rerun

[ztlevi]

/rerun

[ztlevi]

/rerun

[ztlevi]

/rerun

[ztlevi]

/rerun

[ydaiming]

Close due to duplicate effort in https://github.com/aws/deep-learning-containers/pull/2219