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flatironinstitute / DeepFRI

Deep functional residue identification
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Bump tensorflow-gpu from 2.2.0 to 2.3.1 #8

Closed dependabot[bot] closed 3 years ago

dependabot[bot] commented 3 years ago

Bumps tensorflow-gpu from 2.2.0 to 2.3.1.

Release notes

Sourced from tensorflow-gpu's releases.

TensorFlow 2.3.1

Release 2.3.1

Bug Fixes and Other Changes

TensorFlow 2.3.0

Release 2.3.0

Major Features and Improvements

  • tf.data adds two new mechanisms to solve input pipeline bottlenecks and save resources:

In addition checkout the detailed guide for analyzing input pipeline performance with TF Profiler.

  • tf.distribute.TPUStrategy is now a stable API and no longer considered experimental for TensorFlow. (earlier tf.distribute.experimental.TPUStrategy).

  • TF Profiler introduces two new tools: a memory profiler to visualize your model’s memory usage over time and a python tracer which allows you to trace python function calls in your model. Usability improvements include better diagnostic messages and profile options to customize the host and device trace verbosity level.

  • Introduces experimental support for Keras Preprocessing Layers API (tf.keras.layers.experimental.preprocessing.*) to handle data preprocessing operations, with support for composite tensor inputs. Please see below for additional details on these layers.

  • TFLite now properly supports dynamic shapes during conversion and inference. We’ve also added opt-in support on Android and iOS for XNNPACK, a highly optimized set of CPU kernels, as well as opt-in support for executing quantized models on the GPU.

  • Libtensorflow packages are available in GCS starting this release. We have also started to release a nightly version of these packages.

  • The experimental Python API tf.debugging.experimental.enable_dump_debug_info() now allows you to instrument a TensorFlow program and dump debugging information to a directory on the file system. The directory can be read and visualized by a new interactive dashboard in TensorBoard 2.3 called Debugger V2, which reveals the details of the TensorFlow program including graph structures, history of op executions at the Python (eager) and intra-graph levels, the runtime dtype, shape, and numerical composistion of tensors, as well as their code locations.

Breaking Changes

  • Increases the minimum bazel version required to build TF to 3.1.0.
  • tf.data
    • Makes the following (breaking) changes to the tf.data.
    • C++ API: - IteratorBase::RestoreInternal, IteratorBase::SaveInternal, and DatasetBase::CheckExternalState become pure-virtual and subclasses are now expected to provide an implementation.
    • The deprecated DatasetBase::IsStateful method is removed in favor of DatasetBase::CheckExternalState.
    • Deprecated overrides of DatasetBase::MakeIterator and MakeIteratorFromInputElement are removed.

... (truncated)

Changelog

Sourced from tensorflow-gpu's changelog.

Release 2.3.1

Bug Fixes and Other Changes

Release 2.2.1

... (truncated)

Commits
  • fcc4b96 Merge pull request #43446 from tensorflow-jenkins/version-numbers-2.3.1-16251
  • 4cf2230 Update version numbers to 2.3.1
  • eee8224 Merge pull request #43441 from tensorflow-jenkins/relnotes-2.3.1-24672
  • 0d41b1d Update RELEASE.md
  • d99bd63 Insert release notes place-fill
  • d71d3ce Merge pull request #43414 from tensorflow/mihaimaruseac-patch-1-1
  • 9c91596 Fix missing import
  • f9f12f6 Merge pull request #43391 from tensorflow/mihaimaruseac-patch-4
  • 3ed271b Solve leftover from merge conflict
  • 9cf3773 Merge pull request #43358 from tensorflow/mm-patch-r2.3
  • Additional commits viewable in compare view


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dependabot[bot] commented 3 years ago

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