chore: patch huggingface pytorch 1.13 image

[Captainia]

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Description

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 <INSERT COMMIT ID> for my changes.

Confused on how to run tests? Try using the helper utility...

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 standard, rc, and efa sagemaker remote tests by updating the dlc_developer_config.toml file:

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• [ ] 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

• [ ] I have run black -l 100 on my code (formatting tool: https://black.readthedocs.io/en/stable/getting_started.html)

DLC image/dockerfile

Builds to Execute

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Fill out the template and click the checkbox of the builds you'd like to execute

Note: Replace with <X.Y> with the major.minor framework version (i.e. 2.2) you would like to start.

• [ ] build_pytorch_training_<X.Y>_sm

• [ ] build_pytorch_training_<X.Y>_ec2

• [ ] build_pytorch_inference_<X.Y>_sm

• [ ] build_pytorch_inference_<X.Y>_ec2

• [ ] build_pytorch_inference_<X.Y>_graviton

• [ ] build_tensorflow_training_<X.Y>_sm

• [ ] build_tensorflow_training_<X.Y>_ec2

• [ ] build_tensorflow_inference_<X.Y>_sm

• [ ] build_tensorflow_inference_<X.Y>_ec2

• [ ] build_tensorflow_inference_<X.Y>_graviton

Additional context

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.

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

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• [ ] (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.