Bump tensorflow-gpu from 2.0.0-beta1 to 2.12.0 in /east

[dependabot]

Bumps tensorflow-gpu from 2.0.0-beta1 to 2.12.0.

Release notes

Sourced from tensorflow-gpu's releases.

TensorFlow 2.12.0

Release 2.12.0

TensorFlow

Breaking Changes

• Build, Compilation and Packaging

  • Removed redundant packages tensorflow-gpu and tf-nightly-gpu. These packages were removed and replaced with packages that direct users to switch to tensorflow or tf-nightly respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.

• tf.function:

  • tf.function now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:
    • Using functools.wraps on a function with different signature
    • Using functools.partial with an invalid tf.function input
  • tf.function now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.
  • Parameterless tf.functions are assumed to have an empty input_signature instead of an undefined one even if the input_signature is unspecified.
  • tf.types.experimental.TraceType now requires an additional placeholder_value method to be defined.
  • tf.function now traces with placeholder values generated by TraceType instead of the value itself.

• Experimental APIs tf.config.experimental.enable_mlir_graph_optimization and tf.config.experimental.disable_mlir_graph_optimization were removed.

Major Features and Improvements

• Support for Python 3.11 has been added.

• Support for Python 3.7 has been removed. We are not releasing any more patches for Python 3.7.

• tf.lite:

  • Add 16-bit float type support for built-in op fill.
  • Transpose now supports 6D tensors.
  • Float LSTM now supports diagonal recurrent tensors: https://arxiv.org/abs/1903.08023

• tf.experimental.dtensor:

  • Coordination service now works with dtensor.initialize_accelerator_system, and enabled by default.
  • Add tf.experimental.dtensor.is_dtensor to check if a tensor is a DTensor instance.

• tf.data:

  • Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through the experimental_symbolic_checkpoint option of tf.data.Options().
  • Added a new rerandomize_each_iteration argument for the tf.data.Dataset.random() operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). If seed is set and rerandomize_each_iteration=True, the random() operation will produce a different (deterministic) sequence of numbers every epoch.
  • Added a new rerandomize_each_iteration argument for the tf.data.Dataset.sample_from_datasets() operation, which controls whether the sequence of generated random numbers used for sampling should be re-randomized every epoch or not. If seed is set and rerandomize_each_iteration=True, the sample_from_datasets() operation will use a different (deterministic) sequence of numbers every epoch.

• tf.test:

  • Added tf.test.experimental.sync_devices, which is useful for accurately measuring performance in benchmarks.

• tf.experimental.dtensor:

... (truncated)

Changelog

Sourced from tensorflow-gpu's changelog.

Release 2.12.0

Breaking Changes

• Build, Compilation and Packaging

  • Removed redundant packages tensorflow-gpu and tf-nightly-gpu. These packages were removed and replaced with packages that direct users to switch to tensorflow or tf-nightly respectively. Since TensorFlow 2.1, the only difference between these two sets of packages was their names, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.

• tf.function:

  • tf.function now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on. This change may break code where the function signature is malformed, but was ignored previously, such as:
    • Using functools.wraps on a function with different signature
    • Using functools.partial with an invalid tf.function input
  • tf.function now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel signature behavior.
  • Parameterless tf.functions are assumed to have an empty input_signature instead of an undefined one even if the input_signature is unspecified.
  • tf.types.experimental.TraceType now requires an additional placeholder_value method to be defined.
  • tf.function now traces with placeholder values generated by TraceType instead of the value itself.

• Experimental APIs tf.config.experimental.enable_mlir_graph_optimization and tf.config.experimental.disable_mlir_graph_optimization were removed.

Major Features and Improvements

• Support for Python 3.11 has been added.

• Support for Python 3.7 has been removed. We are not releasing any more patches for Python 3.7.

• tf.lite:

  • Add 16-bit float type support for built-in op fill.
  • Transpose now supports 6D tensors.
  • Float LSTM now supports diagonal recurrent tensors: https://arxiv.org/abs/1903.08023

• tf.experimental.dtensor:

  • Coordination service now works with dtensor.initialize_accelerator_system, and enabled by default.
  • Add tf.experimental.dtensor.is_dtensor to check if a tensor is a DTensor instance.

• tf.data:

  • Added support for alternative checkpointing protocol which makes it possible to checkpoint the state of the input pipeline without having to store the contents of internal buffers. The new functionality can be enabled through the experimental_symbolic_checkpoint option of tf.data.Options().
  • Added a new rerandomize_each_iteration argument for the tf.data.Dataset.random() operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). If seed is set and rerandomize_each_iteration=True, the random() operation will produce a different (deterministic) sequence of numbers every epoch.
  • Added a new rerandomize_each_iteration argument for the tf.data.Dataset.sample_from_datasets() operation, which controls whether the sequence of generated random numbers used for sampling should be re-randomized every epoch or not. If seed is set and rerandomize_each_iteration=True, the sample_from_datasets() operation will use a different (deterministic) sequence of numbers every epoch.

• tf.test:

  • Added tf.test.experimental.sync_devices, which is useful for accurately measuring performance in benchmarks.

• tf.experimental.dtensor:

  • Added experimental support to ReduceScatter fuse on GPU (NCCL).

... (truncated)

Commits

• 0db597d Merge pull request #60051 from tensorflow/venkat2469-patch-1
• 1a12f59 Update RELEASE.md
• aa4d558 Merge pull request #60050 from tensorflow/venkat-patch-6
• bd1ab8a Update the security section in RELEASE.md
• 4905be0 Merge pull request #60049 from tensorflow/venkat-patch-5
• 9f96caa Update setup.py on TF release branch with released version of Estimator and k...
• e719b6b Update Relese.md (#60033)
• 64a9d54 Merge pull request #60017 from tensorflow/joefernandez-patch-2.12-release-notes
• 7a4ebfd Update RELEASE.md
• e0e10a9 Merge pull request #59988 from tensorflow-jenkins/version-numbers-2.12.0-8756
• Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

---

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

• @dependabot rebase will rebase this PR
• @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
• @dependabot merge will merge this PR after your CI passes on it
• @dependabot squash and merge will squash and merge this PR after your CI passes on it
• @dependabot cancel merge will cancel a previously requested merge and block automerging
• @dependabot reopen will reopen this PR if it is closed
• @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
• @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
  You can disable automated security fix PRs for this repo from the Security Alerts page.