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diff --git a/RELEASE.md b/RELEASE.md index 7e6325af14..ae41d56e14 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,3 +1,68 @@ +# Release 1.10.0 + +## Major Features And Improvements + +* The `tf.lite` runtime now supports `complex64`. +* Initial Bigtable integration for `tf.data`. +* Improved local run behavior in `tf.estimator.train_and_evaluate` which does not reload checkpoints for evaluation. +* `RunConfig` now sets device_filters to restrict how workers and PS can communicate. This can speed up training and ensure clean shutdowns in some situations. But if you have jobs that require communication between workers, you will have to set custom session_options in your `RunConfig`. +* Moved Distributions and Bijectors from `tf.contrib.distributions` to [Tensorflow Probability (TFP)](https://github.com/tensorflow/probability). `tf.contrib.distributions` is now deprecated and will be removed by the end of 2018. +* Adding new endpoints for existing tensorflow symbols. These endpoints are going to be the preferred endpoints going forward and may replace some of the existing endpoints in the future. See below for the complete list. New symbols have been added to the following modules: [`tf.debugging`](https://www.tensorflow.org/versions/master/api_docs/python/tf/debugging), [`tf.dtypes`](https://www.tensorflow.org/versions/master/api_docs/python/tf/dtypes), [`tf.image`](https://www.tensorflow.org/versions/master/api_docs/python/tf/image), [`tf.io`](https://www.tensorflow.org/versions/master/api_docs/python/tf/io), [`tf.linalg`](https://www.tensorflow.org/versions/master/api_docs/python/tf/linalg), [`tf.manip`](https://www.tensorflow.org/versions/master/api_docs/python/tf/manip), [`tf.math`](https://www.tensorflow.org/versions/master/api_docs/python/tf/math), [`tf.quantization`](https://www.tensorflow.org/versions/master/api_docs/python/tf/quantization), [`tf.strings`](https://www.tensorflow.org/versions/master/api_docs/python/tf/strings) + +## Breaking Changes + +* Prebuilt binaries are now (as of TensorFlow 1.10) built against NCCL 2.2 and no longer include NCCL in the binary install. TensorFlow usage with multiple GPUs and NCCL requires upgrade to [NCCL 2.2](https://developer.nvidia.com/nccl). See updated install guides: [Installing TensorFlow on Ubuntu](https://www.tensorflow.org/install/install_linux#tensorflow_gpu_support) and [Install TensorFlow from Sources](https://www.tensorflow.org/install/install_sources#optional_install_tensorflow_for_gpu_prerequisites). +* Starting from TensorFlow 1.11, Windows builds will use Bazel. Therefore, we will drop official support for cmake. + +## Bug Fixes and Other Changes + +* `tf.data`: + * `tf.contrib.data.group_by_reducer()` is now available via the public API. + * `tf.contrib.data.choose_from_datasets()` is now available via the public API. + * Adding `drop_remainder` argument to `tf.data.Dataset.batch()` and `tf.data.Dataset.padded_batch()`, deprecating `tf.contrib.data.batch_and_drop_remainder()` and `tf.contrib.data.padded_batch_and_drop_remainder()`. +* `tf.estimator`: + * `Estimator`s now use custom savers included in `EstimatorSpec` scaffolds for saving SavedModels during export. + * `EstimatorSpec` will now add a default prediction output for export if no `export_output` is provided, eliminating the need to explicitly include a `PredictOutput` object in the `model_fn` for simple use-cases. + * Support sparse_combiner in canned Linear Estimators. + * Added batch normalization to `DNNClassifier`, `DNNRegressor`, and `DNNEstimator`. + * Adding ranking support for boosted trees. + * Adding center bias option for boosted trees. +* Add `synchronization` and `aggregation` args to get_variable(). These args will be used for distributed variables. +* Add `synchronization` and `aggregation` args to the layer `add_weight()` API. These args will be used for distributed variables. +* `tf.losses.*` do not add to the global collection when executing eagerly (to avoid leaking memory). +* Support different summary and checkpoint directories in `tf.train.MonitoredTrainingSession()`. +* Added IndRNN, IndyGRU, and IndyLSTM cells to `tf.contrib.rnn`. +* Add safe static factory functions for SparseTensor and convert all CHECKs to DCHECKs. Using the constructor directly is unsafe and deprecated. +* Make the Bigtable client connection pool configurable & increase the default # of connections for performance. +* Added derivative of `tf.random_gamma` with respect to the alpha parameter. +* Added derivative of `tf.igamma(a, x)` and `tf.igammac(a, x)` with respect to a. +* Modified Bessel functions of order zero and one. +* Add FillTriangular Bijector to create triangular matrices. +* Added support for Type III DCT, and `tf.spectral.idct(type=2|3)`. +* Correctly handle CuDNN RNN weight loaded when nest in `TimeDistributed`. +* Adding per-element weight support for `WALSComputePartialLhsAndRhsOp`. +* ZerosLike and OnesLike ops treated as constants by Graph Transform Tool. +* Gamma distribution and the derived distributions (Beta, Dirichlet, Student's t, inverse Gamma) now fully reparameterized. +* Java: Experimental wrapper classes to make graph generation easier. Thanks @karllessard and @kbsriram +* Build & link in secure gRPC components (switch from the insecure grpc dependency to secure grpc dependency). +* Adding new endpoints for existing tensorflow symbols. These endpoints are going to be the preferred endpoints going forward and may replace some of the existing endpoints in the future. List of new endpoints: + * New endpoints in `tf.image` namespace: `tf.image.extract_image_patches` + * New endpoints in `tf.debugging` namespace: `tf.debugging.check_numerics`, `tf.debugging.is_finite`, `tf.debugging.is_inf`, `tf.debugging.is_nan`. + * New endpoints in `tf.dtypes` namespace: `tf.dtypes.as_string`. + * New endpoints in `tf.io` namespace: `tf.io.decode_base64`, `tf.io.decode_compressed`, `tf.io.decode_json_example`, `tf.io.decode_raw`, `tf.io.encode_base64`, `tf.io.matching_files`, `tf.io.parse_tensor`, `tf.io.read_file, `tf.io.write_file`. + * New endpoints in tf.linalg namespace: `tf.linalg.cross`, `tf.linalg.tensor_diag` (corresponds to `tf.diag`), `tf.linalg.tensor_diag_part` (corresponds to `tf.diag_part`). + * New endpoints in tf.manip namespace: `tf.manip.batch_to_space_nd`, `tf.manip.gather_nd`, `tf.manip.reshape`, `tf.manip.reverse`, `tf.manip.scatter_nd`, `tf.manip.space_to_batch_nd`, `tf.manip.tile` + * New endpoints in tf.math namespace: `tf.math.acos`, `tf.math.acosh`, `tf.math.add`, `tf.math.asin`, `tf.math.asinh`, `tf.math.atan`, `tf.math.atan2`, `tf.math.atanh`, `tf.math.betainc`, `tf.math.ceil`, `tf.math.cos`, `tf.math.cosh`, `tf.math.digamma`, `tf.math.equal`, `tf.math.erfc`, `tf.math.exp`, `tf.math.expm1`, `tf.math.floor`, `tf.math.greater`, `tf.math.greater_equal`, `tf.math.igamma`, `tf.math.igammac`, `tf.math.invert_permutation`, `tf.math.less`, `tf.math.less_equal`, `tf.math.lgamma`, `tf.math.log`, `tf.math.log1p`, `tf.math.logical_and`, `tf.math.logical_not`, `tf.math.logical_or`, `tf.math.maximum`, `tf.math.minimum`, `tf.math.not_equal`, `tf.math.polygamma`, `tf.math.reciprocal`, `tf.math.rint`, `tf.math.rsqrt`, `tf.math.segment_max`, `tf.math.segment_mean`, `tf.math.segment_min`, `tf.math.segment_prod`, `tf.math.segment_sum`, `tf.math.sin`, `tf.math.sinh`, `tf.math.softplus`, `tf.math.softsign`, `tf.math.squared_difference`, `tf.math.tan`, `tf.math.unsorted_segment_max`, `tf.math.unsorted_segment_min`, `tf.math.unsorted_segment_prod`, `tf.math.unsorted_segment_sum`, `tf.math.zeta`. + * New endpoints in `tf.quantization` namespace: `tf.quantization.dequantize`, `tf.quantization.fake_quant_with_min_max_args`, `tf.quantization.fake_quant_with_min_max_args_gradient`, `tf.quantization.fake_quant_with_min_max_vars`, `tf.quantization.fake_quant_with_min_max_vars_gradient`, `tf.quantization.fake_quant_with_min_max_vars_per_channel`, `tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient`. + * New endpoints in tf.strings namespace: `tf.strings.join` (corresponds to `tf.string_join`), `tf.strings.regex_replace`, `tf.strings.to_number` (corresponds to `tf.string_to_number`), `tf.strings.strip` (corresponds to `tf.string_strip`), `tf.strings.substr`, `tf.strings.to_hash_bucket` (corresponds to `tf.string_to_hash_bucket`), `tf.strings.to_hash_bucket_fast` (corresponds to `tf.string_to_hash_bucket_fast`), `tf.strings.to_hash_bucket_strong` (corresponds to `tf.string_to_hash_bucket_strong`). + + +## Thanks to our Contributors + +This release contains contributions from many people at Google, as well as: + +Ag Ramesh, Alex Wiltschko, Alexander Pantyukhin, Amogh Mannekote, An Jiaoyang, Andrei Nigmatulin, Andrew Ginns, BjøRn Moholt, Brett Koonce, Chengzhi Chen, Chinmay Das, Christian Ertler, Christoph Boeddeker, Clayne Robison, Courtial Florian, ctiijima, Dan Douthit, Dan J, Dan Ringwalt, EFanZh, Emanuele Ballarin, eqy, Evgeniy Zheltonozhskiy, Freedom" Koan-Sin Tan, FréDéRic Branchaud-Charron, G K, gracehoney, Guillaume Klein, Guozhong Zhuang, Hsien-Yang Li, hsm207, ImSheridan, Jayaram Bobba, Jiandong Ruan, Jie, Joel Shor, Jonas Rauber, Jongmin Baek, jsawruk, Karan Kaw, Karl Lessard, karl@kubx.ca, Kb Sriram, KinmanLam, leiiwang, Li, Yiqiang, Loo Rong Jie, Mahmoud Abuzaina, Mahmoud Aslan, ManHyuk, Martin Patz, Martin Zeitler, mktozk, Mohammad Ashraf Bhuiyan, mrTsjolder, Naman Bhalla, Nick Felt, Nicolas Lopez, Niranjan Hasabnis, Nishidha Panpaliya, Nitish, nrstott, Nutti, Parag Jain, PeterLee, Philipp Jund, Rach L, Rafal Wojdyla, Roland Zimmermann, Sergei Lebedev, SneakyFish5, Soila Kavulya, Sriram Veturi, Steven Schmatz, Taehoon Lee, Tang, Wenyi, Taras Sereda, Ted Chang, Tim Zaman, Tristan Rice, tucan, vchigrin, Vikram Tiwari, Vincent, WeberXie, William D. Irons, Yan Facai (颜发才), Yong Tang, Yu Yi, Yuxin Wu, Zé ViníCius + # Release 1.9.0 ## Major Features And Improvements @@ -6,7 +71,7 @@ * Update `tf.keras` to the Keras 2.1.6 API. * Added [`tf.keras.layers.CuDNNGRU`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/keras/layers/CuDNNGRU) and [`tf.keras.layers.CuDNNLSTM`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/keras/layers/CuDNNLSTM) layers. [Try it](https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb?linkId=53292082). * Adding support of core [feature columns](https://www.tensorflow.org/get_started/feature_columns) and [losses](https://www.tensorflow.org/api_docs/python/tf/losses) to [gradient boosted trees estimators](https://github.com/tensorflow/models/tree/master/official/boosted_trees). -* The [python interface](https://tensorflow-dot-devsite.googleplex.com/versions/r1.9/api_docs/python/tf/contrib/lite) +* The [python interface](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/lite) for the [TFLite Optimizing Converter](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/toco/README.md) has been expanded, and the command line interface (AKA: `toco`, `tflite_convert`) is once again included in the standard `pip` installation. @@ -21,7 +86,7 @@ * The [distributions.Bijector](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/distributions/bijectors/Bijector) API supports broadcasting for Bijectors with new API changes. -## Breaking Chances +## Breaking Changes * If you're opening empty variable scopes; replace `variable_scope('', ...)` by `variable_scope(tf.get_variable_scope(), ...)`. * Headers used for building custom ops have been moved from site-packages/external into site-packages/tensorflow/include/external. @@ -34,18 +99,22 @@ * Using `tf.layers` in a subclassed `tf.keras.Model` class. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/layers) for more details * `tf.data`: - * The `DatasetBase::DebugString()` method is now `const`. - * Added the `tf.contrib.data.sample_from_datasets()` API for randomly sampling from multiple datasets. + * `Dataset.from_generator()` now accepts an `args` list, in order to create nested generators. + * `Dataset.list_files()` now produces determinstic results when `shuffle=False` or a `seed` is passed. + * `tf.contrib.data.sample_from_datasets()` and `tf.contrib.data.choose_from_datasets()` make it easier to sample or deterministically choose elements from multiple datasets. + * `tf.contrib.data.make_csv_dataset()` now supports line breaks in quoted strings, and two infrequently used arguments removed. + * (C++) `DatasetBase::DebugString()` is now `const`. + * (C++) `DatasetBase::MakeIterator()` has been renamed to `DatasetBase::MakeIteratorInternal()`. + * (C++) `IteratorBase::Initialize()` method was added to support raising errors during iterator construction. * Eager Execution: + * Added the ability to pause recording operations for gradient computation via `tf.GradientTape.stop_recording`. + * Updated documentation, introductory notebooks. * `tf.keras`: * Move Keras code out of _impl folder and remove API files. * `tf.keras.Model.save_weights` now saves in TensorFlow format by default. * Enable dataset iterators to be passed to `tf.keras.Model` training/eval methods. -* Accelerated Linear Algebra (XLA): -* TensorFlow Debugger (tfdbg): fix an issue in which the TensorBoard Debugger Plugin could not handle total source file size exceeding gRPC message size limit (4 MB). +* TensorFlow Debugger (tfdbg) CLI: fix an issue in which the TensorBoard Debugger Plugin could not handle total source file size exceeding gRPC message size limit (4 MB). * `tf.contrib`: - * Add `tf.contrib.data.choose_from_datasets()`. - * `tf.contrib.data.make_csv_dataset()` now supports line breaks in quoted strings. Two arguments were removed from `make_csv_dataset`. * `tf.contrib.framework.zero_initializer` supports ResourceVariable. * Adding "constrained_optimization" to tensorflow/contrib. * Other: @@ -55,7 +124,6 @@ * More consistent GcsFileSystem behavior for certain reads past EOF. * Update benchmark for tf.scan to match ranges across eager and graph modes. * Fixed bug in `tf.reduce_prod gradient` for complex dtypes. - * Add optional `args` argument to `Dataset.from_generator()`. * Allow the use of '.' in variables (e.g. "hparams.parse('a.b=1.0')"), which would previously raise an error. This will correspond to an attribute name with an embedded '.' symbol (e.g. 'a.b'), which can only be accessed indirectly (e.g. through getattr and setattr). To set this up the user will first need to explicitly add the variable to the hparam object (e.g. "hparams.add_hparam(name='a.b', value=0.0)"). * Benchmark for tf.scan in graph and eager modes. * Added complex128 support to FFT, FFT2D, FFT3D, IFFT, IFFT2D, and IFFT3D. @@ -65,7 +133,6 @@ * LinearOperator[1D,2D,3D]Circulant added to `tensorflow.linalg`. * Conv3D, Conv3DBackpropInput, Conv3DBackpropFilter now supports arbitrary. * Added `tf.train.Checkpoint` for reading/writing object-based checkpoints. - * `Dataset.list_files()` now produces determinstic results when `shuffle=False` or a `seed` is passed. * Added LinearOperatorKronecker, a dense-free implementation of the Kronecker Product. * Allow LinearOperator to broadcast. * SavedModelBuilder will now deduplicate asset names that point to files with the same basename and the same contents. Note that this may result in new asset files included in SavedModels in cases where assets with the same name but different contents were previously overwriting each other. |