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
tf.keras.metrics.AUC now support logit predictions.
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.
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.
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.
They are off by default. Enable them by setting the environment variable TF_ENABLE_ONEDNN_OPTS=1.
We do not recommend using them in GPU systems, as they have not been sufficiently tested with GPUs yet.
TensorFlow pip packages are now built with CUDA11.2 and cuDNN 8.1.0
Breaking Changes
The TF_CPP_MIN_VLOG_LEVEL environment variable has been renamed to to TF_CPP_MAX_VLOG_LEVEL which correctly describes its effect.
Bug Fixes and Other Changes
tf.keras:
Preprocessing layers API consistency changes:
StringLookup added output_mode, sparse, and pad_to_max_tokens arguments with same semantics as TextVectorization.
IntegerLookup added output_mode, sparse, and pad_to_max_tokens arguments with same semantics as TextVectorization. Renamed max_values, oov_value and mask_value to max_tokens, oov_token and mask_token to align with StringLookup and TextVectorization.
TextVectorization default for pad_to_max_tokens switched to False.
CategoryEncoding no longer supports adapt, IntegerLookup now supports equivalent functionality. max_tokens argument renamed to num_tokens.
Discretization added num_bins argument for learning bins boundaries through calling adapt on a dataset. Renamed bins argument to bin_boundaries for specifying bins without adapt.
Improvements to model saving/loading:
model.load_weights now accepts paths to saved models.
The TF_CPP_MIN_VLOG_LEVEL environment variable has been renamed to to
TF_CPP_MAX_VLOG_LEVEL which correctly describes its effect.
Known Caveats
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)
... (truncated)
Commits
a4dfb8d Merge pull request #49124 from tensorflow/mm-cherrypick-tf-data-segfault-fix-...
2107b1d Merge pull request #49116 from tensorflow-jenkins/version-numbers-2.5.0-17609
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dependabot[bot]
commented
3 years ago
Bumps tensorflow from 2.2.2 to 2.5.0.
Release notes
Sourced from tensorflow's releases.
... (truncated)
Changelog
Sourced from tensorflow's changelog.
... (truncated)
Commits
a4dfb8dMerge pull request #49124 from tensorflow/mm-cherrypick-tf-data-segfault-fix-...2107b1dMerge pull request #49116 from tensorflow-jenkins/version-numbers-2.5.0-1760916b8139Update snapshot_dataset_op.cc86a0d86Merge pull request #49126 from geetachavan1/cherrypicks_X9ZNY9436ae6Merge pull request #49128 from geetachavan1/cherrypicks_D73J56b2bf99Validate that a and b are proper sparse tensorsc03ad1aEnsure validation sticks in banded_triangular_solve_op12a6eadMerge pull request #49120 from geetachavan1/cherrypicks_KJ5M9b67f5b8Merge pull request #49118 from geetachavan1/cherrypicks_BIDTRa13c0ad[tf.data][cherrypick] Fix snapshot segfault when using repeat and prefecthDependabot 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](https://github.com/leaf-ai/covid-xprize/network/alerts).