search

willgdjones / HistoVAE

This repository contains work towards HistoVAE: creating meaningful representations of Histopathology images using Variational Autoencoders.
10 stars 3 forks source link

Bump tensorflow from 1.9.0 to 1.15.2 #16

Closed dependabot[bot] closed 4 years ago

dependabot[bot] commented 4 years ago

Bumps tensorflow from 1.9.0 to 1.15.2.

Release notes *Sourced from [tensorflow's releases](https://github.com/tensorflow/tensorflow/releases).* > ## TensorFlow 1.15.2 > # 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) > > ## TensorFlow 1.15.0 > # Release 1.15.0 > 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](https://groups.google.com/a/tensorflow.org/forum/#!topic/developers/iRCt5m4qUz0), `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`. > * Fix tests in canned estimators. > * Expose Head as public API. > * Fixes critical bugs that help with `DenseFeatures` usability in TF2 > ... (truncated)
Changelog *Sourced from [tensorflow's changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md).* > # 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. > * 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`](https://github.com/tensorflow/tensorflow/commit/5d80e1e8e6ee999be7db39461e0e79c90403a2e4) Merge pull request [#36215](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/36215) from tensorflow-jenkins/version-numbers-1.15.2-8214 - [`71e9d8f`](https://github.com/tensorflow/tensorflow/commit/71e9d8f8eddfe283943d62554d4c676bdaf79372) Update version numbers to 1.15.2 - [`e50120e`](https://github.com/tensorflow/tensorflow/commit/e50120ee34e1e29252f4cbc8ac4cd328e9a9840c) Merge pull request [#36214](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/36214) from tensorflow-jenkins/relnotes-1.15.2-2203 - [`1a7e9fb`](https://github.com/tensorflow/tensorflow/commit/1a7e9fbf670ef9d03b2f8fdf1ae2276b2d100fab) Releasing 1.15.2 instead of 1.15.1 - [`85f7aab`](https://github.com/tensorflow/tensorflow/commit/85f7aab93b65ed1fcc589f54d40793b1afb65bf4) Insert release notes place-fill - [`e75a6d6`](https://github.com/tensorflow/tensorflow/commit/e75a6d6e6e20df83f19e72e04c7984587d768bd3) Merge pull request [#36190](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/36190) from tensorflow/mm-r1.15-fix-v2-build - [`a6d8973`](https://github.com/tensorflow/tensorflow/commit/a6d897351e483dfd0418e5cad2900ad9ef24188c) Use `config=v1` as this is `r1.15` branch. - [`fdb8589`](https://github.com/tensorflow/tensorflow/commit/fdb85890df5df1e6b3867c842aabb44f561b446d) Merge pull request [#35912](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/35912) from tensorflow-jenkins/relnotes-1.15.1-31298 - [`a6051e8`](https://github.com/tensorflow/tensorflow/commit/a6051e8094c5e7d26ec9573a740246c92e4057a2) Add CVE number for main patch - [`360b2e3`](https://github.com/tensorflow/tensorflow/commit/360b2e318af2db59152e35be31c8aab1fb164088) Merge pull request [#34532](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/34532) from ROCmSoftwarePlatform/r1.15-rccl-upstream-patch - Additional commits viewable in [compare view](https://github.com/tensorflow/tensorflow/compare/v1.9.0...v1.15.2)


Dependabot compatibility score

Dependabot 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 @dependabot rebase.

Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot ignore this [patch|minor|major] version` will close this PR and stop Dependabot creating any more for this minor/major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) - `@dependabot use these labels` will set the current labels as the default for future PRs for this repo and language - `@dependabot use these reviewers` will set the current reviewers as the default for future PRs for this repo and language - `@dependabot use these assignees` will set the current assignees as the default for future PRs for this repo and language - `@dependabot use this milestone` will set the current milestone as the default for future PRs for this repo and language You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/willgdjones/HistoVAE/network/alerts).
dependabot[bot] commented 4 years ago

Superseded by #17.

dependabot[bot] commented 4 years ago

Superseded by #17.