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mustafamerttunali / deep-learning-training-gui

Train and predict your model on pre-trained deep learning models through the GUI (web app). No more many parameters, no more data preprocessing.
MIT License
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Bump tensorflow-gpu from 2.0.0 to 2.0.1 #4

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](https://github.com/tensorflow/tensorflow/releases).* > ## TensorFlow 2.0.1 > # Release 2.0.1 > > ## Bug Fixes and Other Changes > * Fixes a security vulnerability where converting a Python string to a `tf.float16` value produces a segmentation fault ([CVE-2020-5215](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-5215)) > * Updates `curl` to `7.66.0` to handle [CVE-2019-5482](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5482) and [CVE-2019-5481](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5481) > * Updates `sqlite3` to `3.30.01` to handle [CVE-2019-19646](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19646), [CVE-2019-19645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19645) and [CVE-2019-16168](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-16168)
Changelog *Sourced from [tensorflow-gpu's changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md).* > # Release 2.0.1 > > ## Bug Fixes and Other Changes > * Fixes a security vulnerability where converting a Python string to a `tf.float16` value produces a segmentation fault ([CVE-2020-5215](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-5215)) > * Updates `curl` to `7.66.0` to handle [CVE-2019-5482](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5482) and [CVE-2019-5481](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5481) > * Updates `sqlite3` to `3.30.01` to handle [CVE-2019-19646](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19646), [CVE-2019-19645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19645) and [CVE-2019-16168](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-16168) > > > # Release 1.15.2 > > ## Bug Fixes and Other Changes > * Fixes a security vulnerability where converting a Python string to a `tf.float16` value produces a segmentation fault ([CVE-2020-5215](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-5215)) > * Updates `curl` to `7.66.0` to handle [CVE-2019-5482](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5482) and [CVE-2019-5481](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-5481) > * Updates `sqlite3` to `3.30.01` to handle [CVE-2019-19646](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19646), [CVE-2019-19645](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-19645) and [CVE-2019-16168](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-16168) > > > # 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](https://www.python.org/dev/peps/pep-0373/#update). [As announced earlier](https://groups.google.com/a/tensorflow.org/d/msg/announce/gVwS5RC8mds/dCt1ka2XAAAJ), 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](https://support.microsoft.com/help/2977003/the-latest-supported-visual-c-downloads). > * 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](https://www.tensorflow.org/guide/keras/mixed_precision). > * 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](https://colab.research.google.com/drive/1RvCnR7h0_l4Ekn5vINWToI9TNJdpUZB3). > * 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](https://github.com/tensorflow/models/tree/master/official). > * `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 `NaN`s. > * `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](https://developer.nvidia.com/tensorrt#tensorrt-whats-new) 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`](https://github.com/tensorflow/tensorflow/commit/765ac8d16eff6d6ff997ee73809b402d8b1194ae) Merge pull request [#35913](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/35913) from tensorflow-jenkins/relnotes-2.0.1-6767 - [`0bcb99b`](https://github.com/tensorflow/tensorflow/commit/0bcb99b37577332ba7ee3f7dd06a4ac4801d3ec2) Add CVE number for main patch - [`a093c7e`](https://github.com/tensorflow/tensorflow/commit/a093c7ebd4377560d8b544cd30449dcceb572091) Merge pull request [#36085](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/36085) from tensorflow/mm-r2.0-fix-release-builds-pt4 - [`63aedd7`](https://github.com/tensorflow/tensorflow/commit/63aedd7d84370f31d549d8b508a52a7a79db49b8) Disable test that times out on mac non pip builds - [`619c578`](https://github.com/tensorflow/tensorflow/commit/619c5785813d87e785423a9767218e767e1fd516) Disable the gpu on cpu tests as they were added for 2.1 - [`1a617d6`](https://github.com/tensorflow/tensorflow/commit/1a617d66fe5c55be353a1c99b1458e3ead244efb) Merge pull request [#36047](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/36047) from tensorflow/mm-r2.0-fix-release-builds-pt3 - [`32d9138`](https://github.com/tensorflow/tensorflow/commit/32d9138b2ea3f6e4aca070b3933ec03f677eb1ed) Cleanup the windows builds - [`dd1ebd7`](https://github.com/tensorflow/tensorflow/commit/dd1ebd75426f3b6f378975e71665892cf68d495b) Cleanup macos builds - [`3b93059`](https://github.com/tensorflow/tensorflow/commit/3b9305981c825ce456e7ec1154f5f6724280caca) Remove py2 macos scripts - [`606596f`](https://github.com/tensorflow/tensorflow/commit/606596f080495601a16570012449c6467a91f2aa) Remove builds which are not needed for the release - Additional commits viewable in [compare view](https://github.com/tensorflow/tensorflow/compare/v2.0.0...v2.0.1)


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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.