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irjensen / mandelbrot-deep-zoom

A python routine to generate an animation of a mandelbrot zoom.
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Bump tensorflow-gpu from 2.0.0 to 2.0.1 #3

Closed dependabot[bot] closed 4 years ago

dependabot[bot] commented 4 years ago

Bumps tensorflow-gpu from 2.0.0 to 2.0.1.

Release notes

Sourced from tensorflow-gpu's releases.

TensorFlow 2.0.1

Release 2.0.1

Bug Fixes and Other Changes

Changelog

Sourced from tensorflow-gpu's changelog.

Release 2.0.1

Bug Fixes and Other Changes

Release 1.15.2

Bug Fixes and Other Changes

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.
  • 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.
... (truncated)
Commits
  • 765ac8d Merge pull request #35913 from tensorflow-jenkins/relnotes-2.0.1-6767
  • 0bcb99b Add CVE number for main patch
  • a093c7e Merge pull request #36085 from tensorflow/mm-r2.0-fix-release-builds-pt4
  • 63aedd7 Disable test that times out on mac non pip builds
  • 619c578 Disable the gpu on cpu tests as they were added for 2.1
  • 1a617d6 Merge pull request #36047 from tensorflow/mm-r2.0-fix-release-builds-pt3
  • 32d9138 Cleanup the windows builds
  • dd1ebd7 Cleanup macos builds
  • 3b93059 Remove py2 macos scripts
  • 606596f Remove builds which are not needed for the release
  • Additional commits viewable in compare view


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dependabot[bot] commented 4 years ago

Looks like tensorflow-gpu is up-to-date now, so this is no longer needed.