[Tensorflow]|[build]|[test] Update TF 2.6.5 training binaries

[mseth10]

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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:

• [x] Revision A: sagemaker_remote_tests = "standard"
• [x] Revision B: sagemaker_remote_tests = "rc"
• [x] 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
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• [ ] (If applicable) I've scanned the updated and new binaries to make sure they do not have vulnerabilities associated with them.

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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

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

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[mseth10]

@saimidu can you help with the SM test failures - 4 SM training compiler tests fail, are they even supported?

[mseth10]

The sanity tests are failing test_pip_check with the following stdout:

tensorflow-io 0.21.0 requires tensorflow, which is not installed.
tensorflow-gpu 2.6.5 has requirement protobuf<3.20,>=3.9.2, but you have protobuf 3.20.1.

@tejaschumbalkar I see you have made a similar PR for TF 2.7 https://github.com/aws/deep-learning-containers/commit/888accbcd06bb0e525d662220bff339f92c532a0 , can you advise what changes are needed here apart from adding 'protobuf>=3.20,<3.21'

[ydaiming]

Closing the PR as TF 2.6 is out of support by now, and the initial request is out-dated.