Data Science for Software Engineering (ds4se) is an academic initiative to perform exploratory and causal inference analysis on software engineering artifacts and metadata. Data Management, Analysis, and Benchmarking for DL and Traceability.
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.
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.
*
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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.
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Commits
a8b6d5f Merge pull request #48222 from tensorflow/mm-fix-fileystem-on-r2.5
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Bumps tensorflow-gpu from 2.2.0rc1 to 2.5.0rc0.
Release notes
Sourced from tensorflow-gpu's releases.
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Changelog
Sourced from tensorflow-gpu's changelog.
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Commits
a8b6d5fMerge pull request #48222 from tensorflow/mm-fix-fileystem-on-r2.5b9e31e6Fix typo/logic bug in modular plugins.158505eSwitch TF filesystems to keep backwards compatibility.96dfa5cMerge pull request #48107 from tensorflow/mihaimaruseac-patch-15f7fd89Fix typo in setup.pyf8b5b9bMerge pull request #48093 from tensorflow/mihaimaruseac-patch-1b84dac5Update setup.pyb42047dMerge pull request #48091 from tensorflow-jenkins/version-numbers-2.5.0rc0-301141d4885bUpdate version numbers to 2.5.0-rc06af4297Merge pull request #48082 from njeffrie:f1_depthwiseDependabot 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
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