[TensorFlow][Training][Sagemaker] TensorFlow 2.19.0 Currency Release

[bhanutejagk]

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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 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.

[sirutBuasai]

Overall great job on your first PRs. A few nit organizations here and there. I would retest the images especially EC2 image since the tag would've been wrong in your testing so we should figure out why the EC2 image passed your test even with the wrong image tag.

[sirutBuasai]

This will skip ALL TF images. Are we intending to skip both TF 2.18 and this new TF 2.19?

[bhanutejagk]

Oh i see, sagemaker profiler binary is not available for the new TF version. so probably we should skip it for only TF2.19. I will modify the code to only skip TF2.19 as

@pytest.mark.processor("gpu")
@pytest.mark.integration("smppy")
@pytest.mark.model("mnist")
@pytest.mark.multinode(2)
@pytest.mark.skip_cpu
@pytest.mark.skip_py2_containers
def test_training_smppy_multinode(ecr_image, sagemaker_regions, py_version, tmpdir):
    if "tensorflow-2.19" in ecr_image:
        pytest.skip("SageMaker Profiler binary is not installed in TF 2.19")
    
    _skip_if_image_is_not_compatible_with_smppy(ecr_image)
    invoke_sm_helper_function(ecr_image, sagemaker_regions, _test_smppy_mnist_multinode_function)

[sirutBuasai]

Please take a look at how we do version checks using SpecifierSet to check for version in ecr image.

if "tensorflow-2.19" in ecr_image is bad practice and many things can go wrong during the version checks if we update the image for various reasons.

[sirutBuasai]

ditto

[bhanutejagk]

will modify code to skip only TF2.19

[sirutBuasai]

ditto

[bhanutejagk]

will modify code to skip only TF2.19