Bump tensorflow from 2.0.0a0 to 2.5.0rc0

[dependabot]

Bumps tensorflow from 2.0.0a0 to 2.5.0rc0.

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.5.0-rc0

Release 2.5.0

Major Features and Improvements

• TPU embedding support
  • Added profile_data_directory to EmbeddingConfigSpec in _tpu_estimator_embedding.py. This allows embedding lookup statistics gathered at runtime to be used in embedding layer partitioning decisions.
• tf.keras.metrics.AUC now support logit predictions.
• Creating tf.random.Generator under tf.distribute.Strategy scopes is now allowed (except for tf.distribute.experimental.CentralStorageStrategy and tf.distribute.experimental.ParameterServerStrategy). Different replicas will get different random-number streams.
• tf.data:
  • tf.data service now supports strict round-robin reads, which is useful for synchronous training workloads where example sizes vary. With strict round robin reads, users can guarantee that consumers get similar-sized examples in the same step.
  • tf.data service now supports optional compression. Previously data would always be compressed, but now you can disable compression by passing compression=None to tf.data.experimental.service.distribute(...).
  • tf.data.Dataset.batch() now supports num_parallel_calls and deterministic arguments. num_parallel_calls is used to indicate that multiple input batches should be computed in parallel. With num_parallel_calls set, deterministic is used to indicate that outputs can be obtained in the non-deterministic order.
  • Options returned by tf.data.Dataset.options() are no longer mutable.
  • tf.data input pipelines can now be executed in debug mode, which disables any asynchrony, parallelism, or non-determinism and forces Python execution (as opposed to trace-compiled graph execution) of user-defined functions passed into transformations such as map. The debug mode can be enabled through tf.data.experimental.enable_debug_mode().
• tf.lite
  • Enabled the new MLIR-based quantization backend by default
    • The new backend is used for 8 bits full integer post-training quantization
    • The new backend removes the redundant rescales and fixes some bugs (shared weight/bias, extremely small scales, etc)
    • Set experimental_new_quantizer in tf.lite.TFLiteConverter to False to disable this change
• tf.keras
  • Enabled a new supported input type in Model.fit, tf.keras.utils.experimental.DatasetCreator, which takes a callable, dataset_fn. DatasetCreator is intended to work across all tf.distribute strategies, and is the only input type supported for Parameter Server strategy.
• tf.distribute
  • tf.distribute.experimental.ParameterServerStrategy now supports training with Keras Model.fit when used with DatasetCreator.
• PluggableDevice
  • Third-party devices can now connect to TensorFlow modularly through StreamExecutor C API and PluggableDevice interface.
    • Add custom ops and kernels through kernel and op registration C API.
    • Register custom graph optimization passes with

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.6.0

Breaking Changes

• tf.train.experimental.enable_mixed_precision_graph_rewrite is removed, as the API only works in graph mode and is not customizable. The function is still accessible under tf.compat.v1.mixed_precision.enable_mixed_precision_graph_rewrite, but it is recommended to use the Keras mixed precision API instead.

• tf.lite:

  • Remove experimental.nn.dynamic_rnn, experimental.nn.TfLiteRNNCell and experimental.nn.TfLiteLSTMCell since they're no longer supported. It's recommended to just use keras lstm instead.

* *

Known Caveats

* * *

• TF Core:
  • A longstanding bug in tf.while_loop, which caused it to execute sequentially, even when parallel_iterations>1, has now been fixed. However, the increased parallelism may result in increased memory use. Users who experience unwanted regressions should reset their while_loop's parallel_iterations value to 1, which is consistent with prior behavior.

Major Features and Improvements

* *

• tf.keras:
  • tf.keras.utils.experimental.DatasetCreator now takes an optional tf.distribute.InputOptions for specific options when used with distribution.
  • Updates to Preprocessing layers API for consistency and clarity:
    • StringLookup and IntegerLookup default for mask_token changed to None. This matches the default masking behavior of Hashing and Embedding layers. To keep existing behavior, pass mask_token="" during layer creation.

... (truncated)

Commits

• a8b6d5f Merge pull request #48222 from tensorflow/mm-fix-fileystem-on-r2.5
• b9e31e6 Fix typo/logic bug in modular plugins.
• 158505e Switch TF filesystems to keep backwards compatibility.
• 96dfa5c Merge pull request #48107 from tensorflow/mihaimaruseac-patch-1
• 5f7fd89 Fix typo in setup.py
• f8b5b9b Merge pull request #48093 from tensorflow/mihaimaruseac-patch-1
• b84dac5 Update setup.py
• b42047d Merge pull request #48091 from tensorflow-jenkins/version-numbers-2.5.0rc0-30114
• 1d4885b Update version numbers to 2.5.0-rc0
• 6af4297 Merge pull request #48082 from njeffrie:f1_depthwise
• 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)
• @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
• @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
• @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
• @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

You can disable automated security fix PRs for this repo from the Security Alerts page.

[dependabot]

Superseded by #11.