TensorFlow 1.8.0-rc0 发布,此版本包括很多性能改进和bug修复。主要特性和改进包括:
主要特性和改进
- Can now pass
tf.contrib.distribute.MirroredStrategy()totf.estimator.RunConfig()to run an Estimator model on multiple GPUs on one machine. - Add
tf.contrib.data.prefetch_to_device(), which supports prefetching to GPU memory. - Added Gradient Boosted Trees as pre-made Estimators: BoostedTreesClassifier, BoostedTreesRegressor.
- Add 3rd generation pipeline config for Cloud TPUs which improves performance and usability.
tf.contrib.bayesflowis moving out to it's own repo.- Added
tf.contrib.{proto,rpc}to allow generic proto parsing and RPC communication.
Bug 修复和其他改变
tf.data:- Add
tf.contrib.data.prefetch_to_device, which enables prefetching dataset elements to GPU memory. - Add
tf.contrib.data.AUTOTUNE, which allows the tf.data runtime to automatically tune the prefetch buffer sizes d on your system and environment. - Add
tf.contrib.data.make_csv_datasetfor building datasets of CSV files.
- Add
- Eager Execution:
- With eager execution Datasets can now be used as standard python iterators (
for batch in dataset:). BothDataset.__iter__()andDataset.make_one_shot_iterator()can now be used to create iterators when eager execution is enabled. - Automatic device placement has been enabled (i.e., use a GPU if available automatically, without requiring an explicit
with tf.device(“/gpu:0”)) (Fixes #14133) tf.GradientTapehas moved out of contrib.
- With eager execution Datasets can now be used as standard python iterators (
完整内容请查看发布主页。
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