Release 2.1.0

TensorFlow 2.1 will be the last TF release supporting Python 2. Python 2 support officially ends an January 1, 2020. As announced earlier, TensorFlow will also stop supporting Python 2 starting January 1, 2020, and no more releases are expected in 2019.

Major Features and Improvements

• The tensorflow pip package now includes GPU support by default (same as tensorflow-gpu) for both Linux and Windows. This runs on machines with and without NVIDIA GPUs. tensorflow-gpu is still available, and CPU-only packages can be downloaded at tensorflow-cpu for users who are concerned about package size.
• tf.keras
  • Model.fit_generator, Model.evaluate_generator, Model.predict_generator, Model.train_on_batch, Model.test_on_batch, and Model.predict_on_batch methods now respect the run_eagerly property, and will correctly run using tf.function by default.
  • Model.fit_generator, Model.evaluate_generator, and Model.predict_generator are deprecated endpoints. They are subsumed by Model.fit, Model.evaluate, and Model.predict which now support generators and Sequences.
  • Keras .compile .fit .evaluate and .predict are allowed to be outside of the DistributionStrategy scope, as long as the model was constructed inside of a scope.
  • Keras model.load_weights now accepts skip_mismatch as an argument. This was available in external Keras, and has now been copied over to tf.keras.
  • Introduced the TextVectorization layer, which takes as input raw strings and takes care of text standardization, tokenization, n-gram generation, and vocabulary indexing. See this end-to-end text classification example.
  • Experimental support for Keras .compile, .fit, .evaluate, and .predict is available for Cloud TPU Pods.
  • Automatic outside compilation is now enabled for Cloud TPUs. This allows tf.summary to be used more conveniently with Cloud TPUs.
  • Dynamic batch sizes with DistributionStrategy and Keras are supported on Cloud TPUs.
  • Experimental support for mixed precision is available on GPUs and Cloud TPUs.
  • Keras reference implementations for many popular models are available in the TensorFlow Model Garden.
• tf.data
  • Changes rebatching for tf.data datasets + distribution strategies for better performance. Note that the dataset also behaves slightly differently, in that the rebatched dataset cardinality will always be a multiple of the number of replicas.
• TensorRT
  • TensorRT 6.0 is now supported and enabled by default. This adds support for more TensorFlow ops including Conv3D, Conv3DBackpropInputV2, AvgPool3D, MaxPool3D, ResizeBilinear, and ResizeNearestNeighbor. In addition, the TensorFlow-TensorRT python conversion API is exported as tf.experimental.tensorrt.Converter.

Known issues

Because of issues with building on windows, we turned off eigen strong inlining for the Windows builds. Windows binaries are expected to be slightly slower until the build issues are resolved.

Breaking Changes

• Deletes Operation.traceback_with_start_lines for which we know of no usages.
• Removed id from tf.Tensor.__repr__() as id is not useful other than internal debugging.
• Some tf.assert_* methods now raise assertions at operation creation time if the input tensors' values are known at that time, not during the session.run(). This only changes behavior when the graph execution would have resulted in an error. When this happens, a noop is returned and the input tensors are marked non-feedable. In other words, if they are used as keys in feed_dict argument to session.run(), an error will be raised. Also, because some assert ops don't make it into the graph, the graph structure changes. A different graph can result in different per-op random seeds when they are not given explicitly (most often).
• The following APIs are not longer experimental: tf.config.list_logical_devices, tf.config.list_physical_devices, tf.config.get_visible_devices, tf.config.set_visible_devices, tf.config.get_logical_device_configuration, tf.config.set_logical_device_configuration.
• tf.config.experimentalVirtualDeviceConfiguration has been renamed to tf.config.LogicalDeviceConfiguration.
• tf.config.experimental_list_devices has been removed, please use tf.config.list_logical_devices.

Bug Fixes and Other Changes

• tf.data
  • Fixes concurrency issue with tf.data.experimental.parallel_interleave with sloppy=True.
  • Add tf.data.experimental.dense_to_ragged_batch().
  • Extend tf.data parsing ops to support RaggedTensors.
• tf.distribute
  • Fix issue where GRU would crash or give incorrect output when a tf.distribute.Strategy was used.
• tf.estimator
  • Added option in tf.estimator.CheckpointSaverHook to not save the GraphDef.
• tf.keras
  • Export depthwise_conv2d in tf.keras.backend.
  • In Keras Layers and Models, Variables in trainable_weights, non_trainable_weights, and weights are explicitly deduplicated.
  • Fix the incorrect stateful behavior of Keras convolutional layers.
• tf.lite
  • Legalization for NMS ops in TFLite.
  • add narrow_range and axis to quantize_v2 and dequantize ops.
  • Added support for FusedBatchNormV3 in converter.
  • Add an errno-like field to NNAPI delegate for detecting NNAPI errors for fallback behaviour.
  • Refactors NNAPI Delegate to support detailed reason why an operation is not accelerated.
  • Converts hardswish subgraphs into atomic ops.
• Other
  • Add RaggedTensor.merge_dims().
  • Added new uniform_row_length row-partitioning tensor to RaggedTensor.
  • Add shape arg to RaggedTensor.to_tensor; Improve speed of RaggedTensor.to_tensor.
  • tf.io.parse_sequence_example and tf.io.parse_single_sequence_example now support ragged features.
  • Fix while_v2 with variables in custom gradient.
  • Support taking gradients of V2 tf.cond and tf.while_loop using LookupTable.
  • Fix bug where vectorized_map failed on inputs with unknown static shape.
  • Add preliminary support for sparse CSR matrices.
  • Tensor equality with None now behaves as expected.
  • Make calls to tf.function(f)(), tf.function(f).get_concrete_function and tf.function(f).get_initialization_function thread-safe.
  • Add tf.debugging.enable_check_numerics() and tf.debugging.disable_check_numerics() to facilitate debugging of numeric instability (Infinitys and NaNs) under eager mode and tf.functions.
  • Extend tf.identity to work with CompositeTensors (such as SparseTensor)
  • Added more dtypes and zero-sized inputs to Einsum Op and improved its performance
  • Enable multi-worker NCCL all-reduce inside functions executing eagerly.
  • Added complex128 support to RFFT, RFFT2D, RFFT3D, IRFFT, IRFFT2D, and IRFFT3D.
  • Add pfor converter for SelfAdjointEigV2.
  • Add tf.math.ndtri and tf.math.erfinv.
  • Add tf.config.experimental.enable_mlir_bridge to allow using MLIR compiler bridge in eager model.
  • Added support for MatrixSolve on Cloud TPU / XLA.
  • Added tf.autodiff.ForwardAccumulator for forward-mode autodiff
  • Add LinearOperatorPermutation.
  • A few performance optimizations on tf.reduce_logsumexp.
  • Added multilabel handling to AUC metric
  • Optimization on zeros_like.
  • Dimension constructor now requires None or types with an __index__ method.
  • Add tf.random.uniform microbenchmark.
  • Use _protogen suffix for proto library targets instead of _cc_protogen suffix.
  • Moving the checkpoint reader from swig to pybind11.
  • tf.device & MirroredStrategy now supports passing in a tf.config.LogicalDevice
  • If you're building Tensorflow from source, consider using bazelisk to automatically download and use the correct Bazel version. Bazelisk reads the .bazelversion file at the root of the project directory.

Thanks to our Contributors

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

8bitmp3, Aaron Ma, AbdüLhamit Yilmaz, Abhai Kollara, aflc, Ag Ramesh, Albert Z. Guo, Alex Torres, amoitra, Andrii Prymostka, angeliand, Anshuman Tripathy, Anthony Barbier, Anton Kachatkou, Anubh-V, Anuja Jakhade, Artem Ryabov, autoih, Bairen Yi, Bas Aarts, Basit Ayantunde, Ben Barsdell, Bhavani Subramanian, Brett Koonce, candy.dc, Captain-Pool, caster, cathy, Chong Yan, Choong Yin Thong, Clayne Robison, Colle, Dan Ganea, David Norman, David Refaeli, dengziming, Diego Caballero, Divyanshu, djshen, Douman, Duncan Riach, EFanZh, Elena Zhelezina, Eric Schweitz, Evgenii Zheltonozhskii, Fei Hu, fo40225, Fred Reiss, Frederic Bastien, Fredrik Knutsson, fsx950223, fwcore, George Grzegorz Pawelczak, George Sterpu, Gian Marco Iodice, Giorgio Arena, giuros01, Gomathi Ramamurthy, Guozhong Zhuang, Haifeng Jin, Haoyu Wu, HarikrishnanBalagopal, HJYOO, Huang Chen-Yi, Ilham Firdausi Putra, Imran Salam, Jared Nielsen, Jason Zaman, Jasper Vicenti, Jeff Daily, Jeff Poznanovic, Jens Elofsson, Jerry Shih, jerryyin, Jesper Dramsch, jim.meyer, Jongwon Lee, Jun Wan, Junyuan Xie, Kaixi Hou, kamalkraj, Kan Chen, Karthik Muthuraman, Keiji Ariyama, Kevin Rose, Kevin Wang, Koan-Sin Tan, kstuedem, Kwabena W. Agyeman, Lakshay Tokas, latyas, Leslie-Fang-Intel, Li, Guizi, Luciano Resende, Lukas Folle, Lukas Geiger, Mahmoud Abuzaina, Manuel Freiberger, Mark Ryan, Martin Mlostek, Masaki Kozuki, Matthew Bentham, Matthew Denton, mbhuiyan, mdfaijul, Muhwan Kim, Nagy Mostafa, nammbash, Nathan Luehr, Nathan Wells, Niranjan Hasabnis, Oleksii Volkovskyi, Olivier Moindrot, olramde, Ouyang Jin, OverLordGoldDragon, Pallavi G, Paul Andrey, Paul Wais, pkanwar23, Pooya Davoodi, Prabindh Sundareson, Rajeshwar Reddy T, Ralovich, Kristof, Refraction-Ray, Richard Barnes, richardbrks, Robert Herbig, Romeo Kienzler, Ryan Mccormick, saishruthi, Saket Khandelwal, Sami Kama, Sana Damani, Satoshi Tanaka, Sergey Mironov, Sergii Khomenko, Shahid, Shawn Presser, ShengYang1, Siddhartha Bagaria, Simon Plovyt, skeydan, srinivasan.narayanamoorthy, Stephen Mugisha, sunway513, Takeshi Watanabe, Taylor Jakobson, TengLu, TheMindVirus, ThisIsIsaac, Tim Gates, Timothy Liu, Tomer Gafner, Trent Lo, Trevor Hickey, Trevor Morris, vcarpani, Wei Wang, Wen-Heng (Jack) Chung, wenshuai, Wenshuai-Xiaomi, wenxizhu, william, William D. Irons, Xinan Jiang, Yannic, Yasir Modak, Yasuhiro Matsumoto, Yong Tang, Yongfeng Gu, Youwei Song, Zaccharie Ramzi, Zhang, Zhenyu Guo, 王振华 (Zhenhua Wang), 韩董, 이중건 Isaac Lee