This is the last 1.x release for TensorFlow. We do not expect to update the 1.x branch with features, although we will issue patch releases to fix vulnerabilities for at least one year.
Major Features and Improvements
As announced, tensorflow pip package will by default include GPU support (same as tensorflow-gpu now) for the platforms we currently have GPU support (Linux and Windows). It will work on machines with and without Nvidia GPUs. tensorflow-gpu will still be available, and CPU-only packages can be downloaded at tensorflow-cpu for users who are concerned about package size.
TensorFlow 1.15 contains a complete implementation of the 2.0 API in its compat.v2 module. It contains a copy of the 1.15 main module (without contrib) in the compat.v1 module. TensorFlow 1.15 is able to emulate 2.0 behavior using the enable_v2_behavior() function.
This enables writing forward compatible code: by explicitly importing either tensorflow.compat.v1 or tensorflow.compat.v2, you can ensure that your code works without modifications against an installation of 1.15 or 2.0.
EagerTensor now supports numpy buffer interface for tensors.
Add toggles tf.enable_control_flow_v2() and tf.disable_control_flow_v2() for enabling/disabling v2 control flow.
Enable v2 control flow as part of tf.enable_v2_behavior() and TF2_BEHAVIOR=1.
AutoGraph translates Python control flow into TensorFlow expressions, allowing users to write regular Python inside tf.function-decorated functions. AutoGraph is also applied in functions used with tf.data, tf.distribute and tf.keras APIS.
Adds enable_tensor_equality(), which switches the behavior such that:
Tensors are no longer hashable.
Tensors can be compared with == and !=, yielding a Boolean Tensor with element-wise comparison results. This will be the default behavior in 2.0.
Auto Mixed-Precision graph optimizer simplifies converting models to float16 for acceleration on Volta and Turing Tensor Cores. This feature can be enabled by wrapping an optimizer class with tf.train.experimental.enable_mixed_precision_graph_rewrite().
Add environment variable TF_CUDNN_DETERMINISTIC. Setting to "true" or "1" forces the selection of deterministic cuDNN convolution and max-pooling algorithms. When this is enabled, the algorithm selection procedure itself is also deterministic.
TensorRT
Migrate TensorRT conversion sources from contrib to compiler directory in preparation for TF 2.0.
Add additional, user friendly TrtGraphConverter API for TensorRT conversion.
Expand support for TensorFlow operators in TensorRT conversion (e.g.
Gather, Slice, Pack, Unpack, ArgMin, ArgMax,DepthSpaceShuffle).
Support TensorFlow operator CombinedNonMaxSuppression in TensorRT conversion which
significantly accelerates object detection models.
Breaking Changes
Tensorflow code now produces 2 different pip packages: tensorflow_core containing all the code (in the future it will contain only the private implementation) and tensorflow which is a virtual pip package doing forwarding to tensorflow_core (and in the future will contain only the public API of tensorflow). We don't expect this to be breaking, unless you were importing directly from the implementation.
TensorFlow 1.15 is built using devtoolset7 (GCC7) on Ubuntu 16. This may lead to ABI incompatibilities with extensions built against earlier versions of TensorFlow.
Deprecated the use of constraint= and .constraint with ResourceVariable.
tf.keras:
OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.
tf.keras.model.save_model and model.save now defaults to saving a TensorFlow SavedModel.
keras.backend.resize_images (and consequently, keras.layers.Upsampling2D) behavior has changed, a bug in the resizing implementation was fixed.
Layers now default to float32, and automatically cast their inputs to the layer's dtype. If you had a model that used float64, it will probably silently use float32 in TensorFlow2, and a warning will be issued that starts with Layer "layer-name" is casting an input tensor from dtype float64 to the layer's dtype of float32. To fix, either set the default dtype to float64 with tf.keras.backend.set_floatx('float64'), or pass dtype='float64' to each of the Layer constructors. See tf.keras.layers.Layer for more information.
Some tf.assert_* methods now raise assertions at operation creation time (i.e. when this Python line executes) if the input tensors' values are known at that time, not during the session.run(). 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).
Bug Fixes and Other Changes
tf.estimator:
tf.keras.estimator.model_to_estimator now supports exporting to tf.train.Checkpoint format, which allows the saved checkpoints to be compatible with model.load_weights.
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.
Windows users: Officially-released tensorflow Pip packages are now built with Visual Studio 2019 version 16.4 in order to take advantage of the new /d2ReducedOptimizeHugeFunctions compiler flag. To use these new packages, you must install "Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019", available from Microsoft's website here.
This does not change the minimum required version for building TensorFlow from source on Windows, but builds enabling EIGEN_STRONG_INLINE can take over 48 hours to compile without this flag. Refer to configure.py for more information about EIGEN_STRONG_INLINE and /d2ReducedOptimizeHugeFunctions.
If either of the required DLLs, msvcp140.dll (old) or msvcp140_1.dll (new), are missing on your machine, import tensorflow will print a warning message.
The tensorflow pip package is built with CUDA 10.1 and cuDNN 7.6.
tf.keras
Experimental support for mixed precision is available on GPUs and Cloud TPUs. See usage guide.
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.
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.
Experimental support for Keras .compile, .fit, .evaluate, and .predict is available for Cloud TPUs, Cloud TPU, for all types of Keras models (sequential, functional and subclassing models).
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.
Support for .fit, .evaluate, .predict on TPU using numpy data, in addition to tf.data.Dataset.
Keras reference implementations for many popular models are available in the TensorFlow Model Garden.
tf.data
Changes rebatching for tf.data datasets + DistributionStrategy 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.
tf.data.Dataset now supports automatic data distribution and sharding in distributed environments, including on TPU pods.
Distribution policies for tf.data.Dataset can now be tuned with 1. tf.data.experimental.AutoShardPolicy(OFF, AUTO, FILE, DATA) 2. tf.data.experimental.ExternalStatePolicy(WARN, IGNORE, FAIL)
tf.debugging
Add tf.debugging.enable_check_numerics() and tf.debugging.disable_check_numerics() to help debugging the root causes of issues involving infinities and NaNs.
tf.distribute
Custom training loop support on TPUs and TPU pods is avaiable through strategy.experimental_distribute_dataset, strategy.experimental_distribute_datasets_from_function, strategy.experimental_run_v2, strategy.reduce.
Support for a global distribution strategy through tf.distribute.experimental_set_strategy(), in addition to strategy.scope().
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.
Environment variable TF_DETERMINISTIC_OPS has been added. When set to "true" or "1", this environment variable makes tf.nn.bias_add operate deterministically (i.e. reproducibly), but currently only when XLA JIT compilation is not enabled. Setting TF_DETERMINISTIC_OPS to "true" or "1" also makes cuDNN convolution and max-pooling operate deterministically. This makes Keras Conv*D and MaxPool*D layers operate deterministically in both the forward and backward directions when running on a CUDA-enabled GPU.
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
... (truncated)
Commits
5d80e1e Merge pull request #36215 from tensorflow-jenkins/version-numbers-1.15.2-8214
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Bumps tensorflow from 1.12.2 to 1.15.2.
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Changelog
Sourced from tensorflow's changelog.
Commits
5d80e1eMerge pull request #36215 from tensorflow-jenkins/version-numbers-1.15.2-821471e9d8fUpdate version numbers to 1.15.2e50120eMerge pull request #36214 from tensorflow-jenkins/relnotes-1.15.2-22031a7e9fbReleasing 1.15.2 instead of 1.15.185f7aabInsert release notes place-fille75a6d6Merge pull request #36190 from tensorflow/mm-r1.15-fix-v2-builda6d8973Useconfig=v1as this isr1.15branch.fdb8589Merge pull request #35912 from tensorflow-jenkins/relnotes-1.15.1-31298a6051e8Add CVE number for main patch360b2e3Merge pull request #34532 from ROCmSoftwarePlatform/r1.15-rccl-upstream-patchDependabot 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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