Nvidia-tensorflow

Major Features And Improvements

* The `tf.lite` runtime now supports `complex64`.
* Initial [Google Cloud Bigtable integration](https://github.com/tensorflow/tensorflow/tree/r1.10/tensorflow/contrib/bigtable) 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: [TensorFlow GPU support](https://www.tensorflow.org/install/gpu) and [Build TensorFlow from source](https://www.tensorflow.org/install/source).
* 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, karlkubx.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

Major Features And Improvements
* Updated docs for `tf.keras`: New Keras-based [get started](http://tensorflow.org/versions/r1.9/get_started),
and [programmers guide page](http://tensorflow.org/versions/r1.9/programmers_guide/keras).
* 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/r1/boosted_trees).
* The [python interface](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/lite)
for the [TFLite Optimizing Converter](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/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.
* Improved data-loading and text processing with:
* [`tf.decode_compressed`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/decode_compressed)
* [`tf.string_strip`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/string_strip)
* [`tf.strings.regex_full_match`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/strings/regex_full_match)
* Added experimental support for new pre-made Estimators:
* [`tf.contrib.estimator.BaselineEstimator`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/estimator/BaselineEstimator)
* [`tf.contrib.estimator.RNNClassifier`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/estimator/RNNEstimator)
* [`tf.contrib.estimator.RNNEstimator`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/estimator/RNNClassifier)
* 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 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.

Bug Fixes and Other Changes

* `tfe.Network` is deprecated. Please inherit from `tf.keras.Model`.
* Layered variable names have changed in the following conditions:
* Using `tf.keras.layers` with custom variable scopes.
* 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`:
* `Dataset.from_generator()` now accepts an `args` list, in order to
create nested generators.
* `Dataset.list_files()` now produces deterministic 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.
* 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`:
* `tf.contrib.framework.zero_initializer` supports ResourceVariable.
* Adding "constrained_optimization" to tensorflow/contrib.
* Other:
* Add GCS Configuration Ops.
* Changing signature of `MakeIterator` to enable propagating error status.
* KL divergence for two Dirichlet distributions.
* 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.
* 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.
* Making ids unique in `nn.embedding_lookup_sparse`. This helps to reduce
RPC calls for looking up the embeddings when there are repeated ids in
the batch.
* Support indicator column in boosted trees.
* Prevent `tf.gradients()` from backpropagating through integer tensors.
* 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.
* 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.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Abdullah Alrasheed, Achal Shah, Ad-530, ADiegoCAlonso, Aditya Yogi, Ag Ramesh, akindyakov, Andy Kernahan, Anya Petrova, Aurelien Geron, Ben, Ben Barsdell, Bhavani-Subramanian, braincodercn, Brett Koonce, Brian Nemsick, Brian Zier, Bryan Heden, candy.dc, cclauss, Clayne Robison, ctiijima, Dalmo Cirne, David Norman, David T.H. Kao, DosLin, ekelsen, Elson Rodriguez, Erik Smistad, Felix Abecassis, Fergal Cotter, fo40225, foo0x29a, Freedom" Koan-Sin Tan, FréDéRic Branchaud-Charron, gdh1995, Geoffrey Irving, Giuseppe, gracehoney, Guido Zuidhof, Guillaume Klein, Guozhong Zhuang, Haggai, Harald Husum, imsheridan, Ivan Zhang, Jan Zikes, Jayaram Bobba, Jesse Benson, Jesse Gumz, Jiajia Li, Jie, jinghuangintel, Jingwen, jjsjann123, Joe Yearsley, Joel Hestness, Joel Shor, josephyearsley, Junpeng Lao, Karol M. Langner, Kb Sriram, krantideep95, Krish Ravindranath, Letian Feng, Loo Rong Jie, Lukas Geiger, Maciej, Mahmoud Abuzaina, ManHyuk, Mark Ryan, mbhuiyan, Michal Turek, Mostafa Alaa, Myungsung Kwak, Nand Dalal, Nehal J Wani, Neil Tenenholtz, ngc92, Nicholas Nadeau, P.Eng., Avs, Niranjan Hasabnis, P-Hidringer, Paul Van Eck, Peng Yu, Qing Zhao, Qingying Chen, Quanlong, Rajendra Arora, Rholais Lii, rmanyari, Robin Richtsfeld, Russell Klopfer, Sagi, Sam Sendelbach, Sandeep N Gupta, Sandip Giri, Sarah Edkins, Scott Tseng, Sdalbsoo, Sergii Khomenko, Seungwoo Choi (Biggie), Seyed Majid Azimi, Shaoning Zeng, shengfuintel, Siu Kei, Muk, Smit Shilu, soonson, Stefan Schweter, Sukhwan Kim, Sunitha Kambhampati, Taehoon Lee, tamimaddari82, Tang, Wenyi, Ted Chang, u2takey, Utkarsh Upadhyay, Vadim Markovtsev, voegtlel, Wai Hon Law, wangsiyu, Wenhao Hu, wenhao.hu, William D. Irons, Yan Facai (颜发才), Yanbo Liang, Yihong Wang, Yilei (Dolee) Yang, Yong Tang, Yuan (Terry) Tang

Major Features And Improvements
* Can now pass `tf.contrib.distribute.MirroredStrategy()` to `tf.estimator.RunConfig()` to run an Estimator model on multiple GPUs on one machine.
* Add `tf.contrib.data.prefetch_to_device()`, which supports prefetching to GPU memory.
* Added Gradient Boosted Trees as pre-made Estimators: BoostedTreesClassifier, BoostedTreesRegressor.
* Add 3rd generation pipeline config for Cloud TPUs which improves performance and usability.
* `tf.contrib.bayesflow` is moving out to it's own repo.
* Added `tf.contrib.{proto,rpc}` to allow generic proto parsing and RPC communication^{[1](rpc-issue)}.

Bug Fixes and Other Changes
* `tf.data`:
* Add `tf.contrib.data.prefetch_to_device`, which enables prefetching dataset elements to GPU memory.
* Add `tf.contrib.data.AUTOTUNE`, which allows the tf.data runtime to automatically tune the prefetch buffer sizes based on your system and environment.
* Add `tf.contrib.data.make_csv_dataset` for building datasets of CSV files.
* Eager Execution:
* With eager execution Datasets can now be used as standard python iterators (`for batch in dataset:`). Both `Dataset.__iter__()` and `Dataset.make_one_shot_iterator()` can now be used to create iterators when eager execution is enabled.
* Automatic device placement has been enabled (i.e., use a GPU if available automatically, without requiring an explicit `with tf.device(“/gpu:0”)`) (Fixes 14133)
* `tf.GradientTape` has moved out of contrib.
* `tf.keras`:
* Added the fashion mnist dataset.
* New data preprocessing functions: `image/random_brightness`, `sequence/TimeseriesGenerator`, and `text/hashing_trick`.
* Accelerated Linear Algebra (XLA):
* Select and scatter in reference util and evaluator now use lexicographical order to break ties.
* TensorFlow Debugger (tfdbg) CLI:
* During tensor-filter operations, allow exclusion of nodes by regular expressions.
* Fix spurious background colors in some text terminals.
* `tf.contrib`:
* Add meta-distribution BatchReshape which reshapes batch dimensions.
* `tf.contrib.layers.recompute_grad` works for explicit gradient checkpointing on TPU.
* Add `tf.contrib.framework.argsort`.
* Allow `DNNBoostedTreeCombinedEstimator` to work with core versions of feature columns and losses.
* Add non-linear image warping ops: `tf.contrib.image.sparse_image_warp`, `tf.contrib.image.dense_image_warp`, and `tf.contrib.image.interpolate_spline`.
* Fix bug in `tf.contrib.opt.MultitaskOptimizerWrapper` where types of tensors were mismatched.
* Other:
* Low-level graph construction now calls the TensorFlow C API. This change should be invisible to most users, but can be disabled by setting the environment variable `TF_C_API_GRAPH_CONSTRUCTION=0` in this release. Future releases will remove the ability to disable this change. Please [file a bug](https://github.com/tensorflow/tensorflow/issues/new) if you find yourself using this escape hatch.
* Add description of shapes and a pointer to tutorial notebook in `tf.distributions.Distribution`.
* Update scatter operations:
* Add `tf.scatter_min` and `tf.scatter_max`
* Extend scatter operations to work with a scalar update parameter.
* Move cuDNN RNN ops to core for use in TensorFlow codebase only.
* Add `float64` support for `Conv2d`, `Conv2dBackpropInput`, and `Conv2dBackpropFilter`.
* Add `float64` support for `AvgPool`/`AvgPoolGrad`.
* Make graph name scope thread local so that they work correctly in multi-threaded environments.
* Update nsync synchronization library to avoid slow primitives on Linux.
* Removed need to put nsync/public on C include path when building custom ops.
* Add `tf.image.psnr`, `tf.image.ssim`, `tf.image.ssim_multiscale`, `tf.image.image_gradients`, `tf.image.sobel_edges`.
* Add links to https://js.tensorflow.org.
* Fix non-uniformity of orthogonal matrices.
* Fix bug where multi-image Estimator eval summaries were not displayed correctly.

<a name="rpc-issue">¹</a> The cancellation logic of the RPC op contains a concurrency error. A fix has been submitted to master and will be part of the next release.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

4d55397500, Aghasy, Alan Du, Alan Lee, Alan Yee, Alex Wiltschko, Animesh Karnewar, Ankit Gupta, Anton Matosov, Aris L, Ben Barsdell, Brent Yi, Brett Koonce, Carl Thomé, cbockman, Chikanaga Tomoyuki, Chris Tava, CéDric Deltheil, Dahan Gong, Dalmo Cirne, Daniel Erenrich, David Norman, DavidNorman, Edd Wilder-James, Fanjin Zeng, Felix Abecassis, fo40225, George Sterpu, Giovanni Terlingen, Gor Baghdasaryan, Guillaume Klein, Hanchen Li, Ilya Polenov, Jakub Kolodziejczyk, Jason Sadler, Jayaram Bobba, Jerry Liu, jinghuangintel, Jiongyan Zhang (张炯衍), Joel Shor, Jong Wook Kim, Julian Eisenschlos, Karl Lessard, Krish Ravindranath, Loo Rong Jie, Lukas Geiger, Luke Iwanski, Mahmoud Abuzaina, ManHyuk, Marvin Richter, Maximilian Mitchell, Mohammad Ashraf Bhuiyan, msofka, Mustafa Kasap, Nathan Burnham, Nathan Luehr, Naveen Marri, ngc92, nio1814, Oleg Zabluda, Ou Changkun, Panos Ipeirotis, Paul Van Eck, Peter Lee, Piotr Czapla, qjivy, Rholais Lii, Rodrigo Formigone, Russell Klopfer, ryantimjohn, Sang Han, SebastiáN RamíRez, shengfuintel, Siby Jose Plathottam, Silver Chan, Stanislaw Antol, Taehoon Lee, Tarang Chugh, Ted Chang, Thomas Bastiani, Xian Xu, Xiaoming (Jason) Cui, Yan Facai (颜发才), yaox12, Yashal Shakti Kanungo, Yong Tang, Yuan (Terry) Tang, Yuxin Wu, Ziyue(Louis) Lu

Major Features And Improvements
* Eager mode is moving out of contrib, try `tf.enable_eager_execution()`.
* Graph rewrites emulating fixed-point quantization compatible with TensorFlow Lite, supported by new `tf.contrib.quantize` package.
* Easily customize gradient computation with `tf.custom_gradient`.
* [TensorBoard Debugger Plugin](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md), the graphical user interface (GUI) of TensorFlow Debugger (tfdbg), is now in alpha.
* Experimental support for reading a sqlite database as a `Dataset` with new `tf.contrib.data.SqlDataset`.
* Distributed Mutex / CriticalSection added to `tf.contrib.framework.CriticalSection`.
* Better text processing with `tf.regex_replace`.
* Easy, efficient sequence input with `tf.contrib.data.bucket_by_sequence_length`
* Initial support for `tf.contrib.tensorrt` that enables native TensorRT in
TensorFlow.

Bug Fixes and Other Changes
* Accelerated Linear Algebra (XLA):
* Add `MaxPoolGradGrad` support for XLA
* CSE pass from Tensorflow is now disabled in XLA.
* `tf.data`:
* `tf.data.Dataset`
* Add support for building C++ Dataset op kernels as external libraries, using the `tf.load_op_library()` mechanism.
* `Dataset.list_files()` now shuffles its output by default.
* `Dataset.shuffle(..., seed=tf.constant(0, dtype=tf.int64))` now yields the same sequence of elements as `Dataset.shuffle(..., seed=0)`.
* Add `num_parallel_reads` argument to `tf.data.TFRecordDataset`.
* `tf.contrib`:
* `tf.contrib.bayesflow.halton_sequence` now supports randomization.
* Add support for scalars in `tf.contrib.all_reduce`.
* Add `effective_sample_size` to `tf.contrib.bayesflow.mcmc_diagnostics`.
* Add `potential_scale_reduction` to `tf.contrib.bayesflow.mcmc_diagnostics`.
* Add `BatchNormalization`, `Kumaraswamy` bijectors.
* Deprecate `tf.contrib.learn`. Please check contrib/learn/README.md for instructions on how to convert existing code.
* `tf.contrib.data`
* Remove deprecated `tf.contrib.data.Dataset`, `tf.contrib.data.Iterator`, `tf.contrib.data.FixedLengthRecordDataset`, `tf.contrib.data.TextLineDataset`, and `tf.contrib.data.TFRecordDataset` classes.
* Added `bucket_by_sequence_length`, `sliding_window_batch`, and `make_batched_features_dataset`
* Remove unmaintained `tf.contrib.ndlstm`. You can find it externally at https://github.com/tmbarchive/tfndlstm.
* Moved most of `tf.contrib.bayesflow` to its own repo: `tfp`
* Other:
* tf.py_func now reports the full stack trace if an exception occurs.
* Integrate `TPUClusterResolver` with GKE's integration for Cloud TPUs.
* Add a library for statistical testing of samplers.
* Add Helpers to stream data from the GCE VM to a Cloud TPU.
* Integrate ClusterResolvers with TPUEstimator.
* Unify metropolis_hastings interface with HMC kernel.
* Move LIBXSMM convolutions to a separate --define flag so that they are disabled by default.
* Fix `MomentumOptimizer` lambda.
* Reduce `tfp.layers` boilerplate via programmable docstrings.
* Add `auc_with_confidence_intervals`, a method for computing the AUC and confidence interval with linearithmic time complexity.
* `regression_head` now accepts customized link function, to satisfy the usage that user can define their own link function if the `array_ops.identity` does not meet the requirement.
* Fix `initialized_value` and `initial_value` behaviors for `ResourceVariables` created from `VariableDef` protos.
* Add TensorSpec to represent the specification of Tensors.
* Constant folding pass is now deterministic.
* Support `float16` `dtype` in `tf.linalg.*`.
* Add `tf.estimator.export.TensorServingInputReceiver` that allows `tf.estimator.Estimator.export_savedmodel` to pass raw tensors to model functions.

Deprecations

* TensorFlow 1.7 may be the last time we support Cuda versions below 8.0.
Starting with TensorFlow 1.8 release, 8.0 will be the minimum supported
version.
* TensorFlow 1.7 may be the last time we support cuDNN versions below 6.0.
Starting with TensorFlow 1.8 release, 6.0 will be the minimum supported
version.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

4d55397500, Abe, Alistair Low, Andy Kernahan, Appledore, Ben, Ben Barsdell, Boris Pfahringer, Brad Wannow, Brett Koonce, Carl Thomé, cclauss, Chengzhi Chen, Chris Drake, Christopher Yeh, Clayne Robison, Codrut Grosu, Daniel Trebbien, Danny Goodman, David Goodwin, David Norman, Deron Eriksson, Donggeon Lim, Donny Viszneki, DosLin, DylanDmitri, Francisco Guerrero, Fred Reiss, gdh1995, Giuseppe, Glenn Weidner, gracehoney, Guozhong Zhuang, Haichen "Hc" Li, Harald Husum, harumitsu.nobuta, Henry Spivey, hsm207, Jekyll Song, Jerome, Jiongyan Zhang, jjsjann123, John Sungjin Park, Johnson145, JoshVarty, Julian Wolff, Jun Wang, June-One, Kamil Sindi, Kb Sriram, Kdavis-Mozilla, Kenji, lazypanda1, Liang-Chi Hsieh, Loo Rong Jie, Mahesh Bhosale, MandarJKulkarni, ManHyuk, Marcus Ong, Marshal Hayes, Martin Pool, matthieudelaro, mdfaijul, mholzel, Michael Zhou, Ming Li, Minmin Sun, Myungjoo Ham, MyungsungKwak, Naman Kamra, Peng Yu, Penghao Cen, Phil, Raghuraman-K, resec, Rohin Mohanadas, Sandeep N Gupta, Scott Tseng, seaotterman, Seo Sanghyeon, Sergei Lebedev, Ted Chang, terrytangyuan, Tim H, tkunic, Tod, vihanjain, Yan Facai (颜发才), Yin Li, Yong Tang, Yukun Chen, Yusuke Yamada

Breaking Changes
* Prebuilt binaries are now built against CUDA 9.0 and cuDNN 7.
* Prebuilt binaries will use AVX instructions. This may break TF on older CPUs.

Major Features And Improvements
* New Optimizer internal API for non-slot variables. Descendants of AdamOptimizer that access _beta[12]_power will need to be updated.
* `tf.estimator.{FinalExporter,LatestExporter}` now export stripped SavedModels. This improves forward compatibility of the SavedModel.
* FFT support added to XLA CPU/GPU.

Bug Fixes and Other Changes
* Documentation updates:
* Added a second version of Getting Started, which is aimed at ML
newcomers.
* Clarified documentation on `resize_images.align_corners` parameter.
* Additional documentation for TPUs.
* Google Cloud Storage (GCS):
* Add client-side throttle.
* Add a `FlushCaches()` method to the FileSystem interface, with an implementation for GcsFileSystem.
* Other:
* Add `tf.contrib.distributions.Kumaraswamy`.
* `RetryingFileSystem::FlushCaches()` calls the base FileSystem's `FlushCaches()`.
* Add `auto_correlation` to distributions.
* Add `tf.contrib.distributions.Autoregressive`.
* Add SeparableConv1D layer.
* Add convolutional Flipout layers.
* When both inputs of `tf.matmul` are bfloat16, it returns bfloat16, instead of float32.
* Added `tf.contrib.image.connected_components`.
* Add `tf.contrib.framework.CriticalSection` that allows atomic variable access.
* Output variance over trees predictions for classifications tasks.
* For `pt` and `eval` commands, allow writing tensor values to filesystem as numpy files.
* gRPC: Propagate truncated errors (instead of returning gRPC internal error).
* Augment `parallel_interleave` to support 2 kinds of prefetching.
* Improved XLA support for C64-related ops log, pow, atan2, tanh.
* Add probabilistic convolutional layers.

API Changes
* Introducing `prepare_variance` boolean with default setting to False for backward compatibility.
* Move `layers_dense_variational_impl.py` to `layers_dense_variational.py`.

Known Bugs
* Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results and/or
`CUDA_ILLEGAL_ADDRESS` failures.

Google discovered in mid-December 2017 that the PTX-to-SASS compiler in CUDA 9
and CUDA 9.1 sometimes does not properly compute the carry bit when
decomposing 64-bit address calculations with large offsets (e.g. `load [x +
large_constant]`) into 32-bit arithmetic in SASS.

As a result, these versions of `ptxas` miscompile most XLA programs which use
more than 4GB of temp memory. This results in garbage results and/or
`CUDA_ERROR_ILLEGAL_ADDRESS` failures.

A fix in CUDA 9.1.121 is expected in late February 2018. We do not expect a
fix for CUDA 9.0.x. Until the fix is available, the only workaround is to
[downgrade](https://developer.nvidia.com/cuda-toolkit-archive) to CUDA 8.0.x
or disable XLA:GPU.

TensorFlow will print a warning if you use XLA:GPU with a known-bad version of
CUDA; see e00ba24c4038e7644da417ddc639169b6ea59122.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

4d55397500, Ag Ramesh, Aiden Scandella, Akimasa Kimura, Alex Rothberg, Allen Goodman,
amilioto, Andrei Costinescu, Andrei Nigmatulin, Anjum Sayed, Anthony Platanios,
Anush Elangovan, Armando Fandango, Ashish Kumar Ram, Ashwini Shukla, Ben, Bhavani Subramanian,
Brett Koonce, Carl Thomé, cclauss, Cesc, Changming Sun, Christoph Boeddeker, Clayne Robison,
Clemens Schulz, Clint (Woonhyuk Baek), codrut3, Cole Gerdemann, Colin Raffel, Daniel Trebbien,
Daniel Ylitalo, Daniel Zhang, Daniyar, Darjan Salaj, Dave Maclachlan, David Norman, Dong--Jian,
dongsamb, dssgsra, Edward H, eladweiss, elilienstein, Eric Lilienstein, error.d, Eunji Jeong, fanlu,
Florian Courtial, fo40225, Fred, Gregg Helt, Guozhong Zhuang, Hanchen Li, hsm207, hyunyoung2,
ImSheridan, Ishant Mrinal Haloi, Jacky Ko, Jay Young, Jean Flaherty, Jerome, JerrikEph, Jesse
Kinkead, jfaath, Jian Lin, jinghuangintel, Jiongyan Zhang, Joel Hestness, Joel Shor, Johnny Chan,
Julian Niedermeier, Julian Wolff, JxKing, K-W-W, Karl Lessard, Kasper Marstal, Keiji Ariyama,
Koan-Sin Tan, Loki Der Quaeler, Loo Rong Jie, Luke Schaefer, Lynn Jackson, ManHyuk, Matt Basta,
Matt Smith, Matthew Schulkind, Michael, michaelkhan3, Miguel Piedrafita, Mikalai Drabovich,
Mike Knapp, mjwen, mktozk, Mohamed Aly, Mohammad Ashraf Bhuiyan, Myungjoo Ham, Naman Bhalla,
Namrata-Ibm, Nathan Luehr, nathansilberman, Netzeband, Niranjan Hasabnis, Omar Aflak, Ozge
Yalcinkaya, Parth P Panchal, patrickzzy, Patryk Chrabaszcz, Paul Van Eck, Paweł Kapica, Peng Yu,
Philip Yang, Pierre Blondeau, Po-Hsien Chu, powderluv, Puyu Wang, Rajendra Arora, Rasmus, Renat
Idrisov, resec, Robin Richtsfeld, Ronald Eddy Jr, Sahil Singh, Sam Matzek, Sami Kama, sandipmgiri,
Santiago Castro, Sayed Hadi Hashemi, Scott Tseng, Sergii Khomenko, Shahid, Shengpeng Liu, Shreyash
Sharma, Shrinidhi Kl, Simone Cirillo, simsicon, Stanislav Levental, starsblinking, Stephen Lumenta,
Steven Hickson, Su Tang, Taehoon Lee, Takuya Wakisaka, Ted Chang, Ted Ying, Tijmen Verhulsdonck,
Timofey Kondrashov, vade, vaibhav, Valentin Khrulkov, vchigrin, Victor Costan, Viraj Navkal,
Vivek Rane, wagonhelm, Yan Facai (颜发才), Yanbo Liang, Yaroslav Bulatov, yegord, Yong Tang,
Yoni Tsafir, yordun, Yuan (Terry) Tang, Yuxin Wu, zhengdi, Zhengsheng Wei, 田传武

Breaking Changes
* Prebuilt binaries are now built against CUDA 9.0 and cuDNN 7.
* Starting from 1.6 release, our prebuilt binaries will use AVX instructions.
This may break TF on older CPUs.

Major Features And Improvements
* [Eager execution](https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/contrib/eager)
preview version is now available.
* [TensorFlow Lite](https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflow/lite)
dev preview is now available.
* CUDA 9.0 and cuDNN 7 support.
* Accelerated Linear Algebra (XLA):
* Add `complex64` support to XLA compiler.
* `bfloat` support is now added to XLA infrastructure.
* Make `ClusterSpec` propagation work with XLA devices.
* Use a deterministic executor to generate XLA graph.
* `tf.contrib`:
* `tf.contrib.distributions`:
* Add `tf.contrib.distributions.Autoregressive`.
* Make `tf.contrib.distributions` QuadratureCompound classes support batch
* Infer `tf.contrib.distributions.RelaxedOneHotCategorical` `dtype` from arguments.
* Make `tf.contrib.distributions` quadrature family parameterized by
`quadrature_grid_and_prob` vs `quadrature_degree`.
* `auto_correlation` added to `tf.contrib.distributions`
* Add `tf.contrib.bayesflow.layers`, a collection of probabilistic (neural) layers.
* Add `tf.contrib.bayesflow.halton_sequence`.
* Add `tf.contrib.data.make_saveable_from_iterator.`
* Add `tf.contrib.data.shuffle_and_repeat`.
* Add new custom transformation: `tf.contrib.data.scan()`.
* `tf.contrib.distributions.bijectors`:
* Add `tf.contrib.distributions.bijectors.MaskedAutoregressiveFlow`.
* Add `tf.contrib.distributions.bijectors.Permute`.
* Add `tf.contrib.distributions.bijectors.Gumbel`.
* Add `tf.contrib.distributions.bijectors.Reshape`.
* Support shape inference (i.e., shapes containing -1) in the Reshape bijector.
* Add `streaming_precision_recall_at_equal_thresholds,` a method for computing
streaming precision and recall with `O(num_thresholds + size of predictions)`
time and space complexity.
* Change `RunConfig` default behavior to not set a random seed, making random
behavior independently random on distributed workers. We expect this to
generally improve training performance. Models that do rely on determinism
should set a random seed explicitly.
* Replaced the implementation of `tf.flags` with `absl.flags`.
* Add support for `CUBLAS_TENSOR_OP_MATH` in fp16 GEMM
* Add support for CUDA on NVIDIA Tegra devices

Bug Fixes and Other Changes
* Documentation updates:
* Clarified that you can only install TensorFlow on 64-bit machines.
* Added a short doc explaining how `Estimator`s save checkpoints.
* Add documentation for ops supported by the `tf2xla` bridge.
* Fix minor typos in the doc of `SpaceToDepth` and `DepthToSpace`.
* Updated documentation comments in `mfcc_mel_filterbank.h` and `mfcc.h` to
clarify that the input domain is squared magnitude spectra and the weighting
is done on linear magnitude spectra (sqrt of inputs).
* Change `tf.contrib.distributions` docstring examples to use `tfd` alias
rather than `ds`, `bs`.
* Fix docstring typos in `tf.distributions.bijectors.Bijector`.
* `tf.assert_equal` no longer raises `ValueError.` It now raises
`InvalidArgumentError,` as documented.
* Update Getting Started docs and API intro.
* Google Cloud Storage (GCS):
* Add userspace DNS caching for the GCS client.
* Customize request timeouts for the GCS filesystem.
* Improve GCS filesystem caching.
* Bug Fixes:
* Fix bug where partitioned integer variables got their wrong shapes. Before
* Fix correctness bug in CPU and GPU implementations of Adadelta.
* Fix a bug in `import_meta_graph`'s handling of partitioned variables when
importing into a scope. WARNING: This may break loading checkpoints of
graphs with partitioned variables saved after using `import_meta_graph` with
a non-empty `import_scope` argument.
* Fix bug in offline debugger which prevented viewing events.
* Added the `WorkerService.DeleteWorkerSession` method to the gRPC interface,
to fix a memory leak. Ensure that your master and worker servers are running
the same version of TensorFlow to avoid compatibility issues.
* Fix bug in peephole implementation of BlockLSTM cell.
* Fix bug by casting dtype of `log_det_jacobian` to match `log_prob` in
`TransformedDistribution`.
* Fix a bug in `import_meta_graph`'s handling of partitioned variables when
* Ensure `tf.distributions.Multinomial` doesn't underflow in `log_prob`.
Before this change, all partitions of an integer variable were initialized
with the shape of the unpartitioned variable; after this change they are
initialized correctly.
* Other:
* Add necessary shape util support for bfloat16.
* Add a way to run ops using a step function to MonitoredSession.
* Add `DenseFlipout` probabilistic layer.
* A new flag `ignore_live_threads` is available on train. If set to `True`, it
will ignore threads that remain running when tearing down infrastructure
after successfully completing training, instead of throwing a RuntimeError.
* Restandardize `DenseVariational` as simpler template for other probabilistic
layers.
* `tf.data` now supports `tf.SparseTensor` components in dataset elements.
* It is now possible to iterate over `Tensor`s.
* Allow `SparseSegmentReduction` ops to have missing segment IDs.
* Modify custom export strategy to account for multidimensional sparse float
splits.
* `Conv2D`, `Conv2DBackpropInput`, `Conv2DBackpropFilter` now supports arbitrary
dilations with GPU and cuDNNv6 support.
* `Estimator` now supports `Dataset`: `input_fn` can return a `Dataset`
instead of `Tensor`s.
* Add `RevBlock`, a memory-efficient implementation of reversible residual layers.
* Reduce BFCAllocator internal fragmentation.
* Add `cross_entropy` and `kl_divergence` to `tf.distributions.Distribution`.
* Add `tf.nn.softmax_cross_entropy_with_logits_v2` which enables backprop
w.r.t. the labels.
* GPU back-end now uses `ptxas` to compile generated PTX.
* `BufferAssignment`'s protocol buffer dump is now deterministic.
* Change embedding op to use parallel version of `DynamicStitch`.
* Add support for sparse multidimensional feature columns.
* Speed up the case for sparse float columns that have only 1 value.
* Allow sparse float splits to support multivalent feature columns.
* Add `quantile` to `tf.distributions.TransformedDistribution`.
* Add `NCHW_VECT_C` support for `tf.depth_to_space` on GPU.
* Add `NCHW_VECT_C` support for `tf.space_to_depth` on GPU.

API Changes
* Rename `SqueezeDims` attribute to `Axis` in C++ API for Squeeze op.
* `Stream::BlockHostUntilDone` now returns Status rather than bool.
* Minor refactor: move stats files from `stochastic` to `common` and remove
`stochastic`.

Known Bugs
* Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results and/or
`CUDA_ILLEGAL_ADDRESS` failures.

Google discovered in mid-December 2017 that the PTX-to-SASS compiler in CUDA 9
and CUDA 9.1 sometimes does not properly compute the carry bit when
decomposing 64-bit address calculations with large offsets (e.g. `load [x +
large_constant]`) into 32-bit arithmetic in SASS.

As a result, these versions of `ptxas` miscompile most XLA programs which use
more than 4GB of temp memory. This results in garbage results and/or
`CUDA_ERROR_ILLEGAL_ADDRESS` failures.

A fix in CUDA 9.1.121 is expected in late February 2018. We do not expect a
fix for CUDA 9.0.x. Until the fix is available, the only workaround is to
[downgrade](https://developer.nvidia.com/cuda-toolkit-archive) to CUDA 8.0.x
or disable XLA:GPU.

TensorFlow will print a warning if you use XLA:GPU with a known-bad version of
CUDA; see e00ba24c4038e7644da417ddc639169b6ea59122.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Adam Zahran, Ag Ramesh, Alan Lee, Alan Yee, Alex Sergeev, Alexander, Amir H. Jadidinejad,
Amy, Anastasios Doumoulakis, Andrei Costinescu, Andrei Nigmatulin, Anthony Platanios,
Anush Elangovan, arixlin, Armen Donigian, ArtëM Sobolev, Atlas7, Ben Barsdell, Bill Prin,
Bo Wang, Brett Koonce, Cameron Thomas, Carl Thomé, Cem Eteke, cglewis, Changming Sun,
Charles Shenton, Chi-Hung, Chris Donahue, Chris Filo Gorgolewski, Chris Hoyean Song,
Chris Tava, Christian Grail, Christoph Boeddeker, cinqS, Clayne Robison, codrut3, concerttttt,
CQY, Dan Becker, Dan Jarvis, Daniel Zhang, David Norman, dmaclach, Dmitry Trifonov,
Donggeon Lim, dongpilYu, Dr. Kashif Rasul, Edd Wilder-James, Eric Lv, fcharras, Felix Abecassis,
FirefoxMetzger, formath, FredZhang, Gaojin Cao, Gary Deer, Guenther Schmuelling, Hanchen Li,
Hanmin Qin, hannesa2, hyunyoung2, Ilya Edrenkin, Jackson Kontny, Jan, Javier Luraschi,
Jay Young, Jayaram Bobba, Jeff, Jeff Carpenter, Jeremy Sharpe, Jeroen BéDorf, Jimmy Jia,
Jinze Bai, Jiongyan Zhang, Joe Castagneri, Johan Ju, Josh Varty, Julian Niedermeier,
JxKing, Karl Lessard, Kb Sriram, Keven Wang, Koan-Sin Tan, Kyle Mills, lanhin, LevineHuang,
Loki Der Quaeler, Loo Rong Jie, Luke Iwanski, LáSzló Csomor, Mahdi Abavisani, Mahmoud Abuzaina,
ManHyuk, Marek ŠUppa, MathSquared, Mats Linander, Matt Wytock, Matthew Daley, Maximilian Bachl,
mdymczyk, melvyniandrag, Michael Case, Mike Traynor, miqlas, Namrata-Ibm, Nathan Luehr,
Nathan Van Doorn, Noa Ezra, Nolan Liu, Oleg Zabluda, opensourcemattress, Ouwen Huang,
Paul Van Eck, peisong, Peng Yu, PinkySan, pks, powderluv, Qiao Hai-Jun, Qiao Longfei,
Rajendra Arora, Ralph Tang, resec, Robin Richtsfeld, Rohan Varma, Ryohei Kuroki, SaintNazaire,
Samuel He, Sandeep Dcunha, sandipmgiri, Sang Han, scott, Scott Mudge, Se-Won Kim, Simon Perkins,
Simone Cirillo, Steffen Schmitz, Suvojit Manna, Sylvus, Taehoon Lee, Ted Chang, Thomas Deegan,
Till Hoffmann, Tim, Toni Kunic, Toon Verstraelen, Tristan Rice, Urs KöSter, Utkarsh Upadhyay,
Vish (Ishaya) Abrams, Winnie Tsang, Yan Chen, Yan Facai (颜发才), Yi Yang, Yong Tang,
Youssef Hesham, Yuan (Terry) Tang, Zhengsheng Wei, zxcqwe4906, 张志豪, 田传武

We are also grateful to all who filed issues or helped resolve them, asked and
answered questions, and were part of inspiring discussions.