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NCCL 2.x Release notes section updated. #20895

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Jul 18, 2018
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NCCL 2.x Release notes section updated. #20895

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6 changes: 5 additions & 1 deletion RELEASE.md
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Expand Up @@ -19,6 +19,7 @@
* Added batch normalization to `DNNClassifier`, `DNNRegressor`, and `DNNEstimator`.
* Eager Execution
* `tf.losses.*` do not add to the global collection when executing eagerly (avoids leaking memory).
* Prebuilt binaries are now built against NCCL 2.2. TensorFlow usage with multiple GPUs and NCCL requires upgrade to NCCL 2.2
* Support different summary and checkpoint directories in `tf.train.MonitoredTrainingSession()`.
* Added IndRNN, IndyGRU, and IndyLSTM cells to `tf.contrib.rnn`.
* Add `synchronization` and `aggregation` args to the layer `add_weight()` API. These args will be used for distributed variables.
Expand All @@ -42,11 +43,14 @@

## Breaking Changes

* Prebuilt binaries are now (as of TF 1.10) built against NCCL 2.2 and no longer include NCCL in the binary install.
TensorFlow usage with multiple GPUs and NCCL requires upgrade to [NCCL 2.2](https://developer.nvidia.com/nccl).
See updated install guides: [Installing TensorFlow on Ubuntu](https://www.tensorflow.org/install/install_linux#tensorflow_gpu_support)
and [Install TensorFlow from Sources](https://www.tensorflow.org/install/install_sources#optional_install_tensorflow_for_gpu_prerequisites).
* Starting from TensorFlow 1.11, Windows builds will use Bazel. Therefore, we will drop official support for cmake.

## Bug Fixes and Other Changes

* Prebuilt binaries are now built against NCCL 2.2. TensorFlow usage with multiple GPUs requires upgrade to NCCL 2.2
* Java: Experimental wrapper classes to make graph generation easier. Thanks @karllessard and @kbsriram
* Replace `distribution_util.assert_close` with `tf.assert_near`.
* Fix minor typos in linear_operator.py docstrings.
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