Release 1.6.0

Breaking Changes

• Prebuilt binaries are now built against CUDA 9.0 and cuDNN 7.
• Prebuilt binaries will use AVX instructions. This may break TF on older CPUs.

Major Features And Improvements

• New Optimizer internal API for non-slot variables. Descendants of AdamOptimizer that access _beta[12]_power will need to be updated.
• tf.estimator.{FinalExporter,LatestExporter} now export stripped SavedModels. This improves forward compatibility of the SavedModel.
• FFT support added to XLA CPU/GPU.
• Android TF can now be built with CUDA acceleration on compatible Tegra devices (see [contrib/makefile/README.md](contrib/makefile/README.md) for more information)

Bug Fixes and Other Changes

• Documentation updates:
  • Added a second version of Getting Started, which is aimed at ML newcomers.
  • Clarified documentation on resize_images.align_corners parameter.
  • Additional documentation for TPUs.
• Google Cloud Storage (GCS):
  • Add client-side throttle.
  • Add a FlushCaches() method to the FileSystem interface, with an implementation for GcsFileSystem.
• Other:
  • Add tf.contrib.distributions.Kumaraswamy.
  • RetryingFileSystem::FlushCaches() calls the base FileSystem's FlushCaches().
  • Add auto_correlation to distributions.
  • Add tf.contrib.distributions.Autoregressive.
  • Add SeparableConv1D layer.
  • Add convolutional Flipout layers.
  • When both inputs of tf.matmul are bfloat16, it returns bfloat16, instead of float32.
  • Added tf.contrib.image.connected_components.
  • Add tf.contrib.framework.CriticalSection that allows atomic variable access.
  • Output variance over trees predictions for classifications tasks.
  • For pt and eval commands, allow writing tensor values to filesystem as numpy files.
  • gRPC: Propagate truncated errors (instead of returning gRPC internal error).
  • Augment parallel_interleave to support 2 kinds of prefetching.
  • Improved XLA support for C64-related ops log, pow, atan2, tanh.
  • Add probabilistic convolutional layers.

API Changes

• Introducing prepare_variance boolean with default setting to False for backward compatibility.
• Move layers_dense_variational_impl.py to layers_dense_variational.py.

Known Bugs

• Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results and/or CUDA_ILLEGAL_ADDRESS failures.

Google discovered in mid-December 2017 that the PTX-to-SASS compiler in CUDA 9 and CUDA 9.1 sometimes does not properly compute the carry bit when decomposing 64-bit address calculations with large offsets (e.g. load [x + large_constant]) into 32-bit arithmetic in SASS.

As a result, these versions of ptxas miscompile most XLA programs which use more than 4GB of temp memory. This results in garbage results and/or CUDA_ERROR_ILLEGAL_ADDRESS failures.

A fix in CUDA 9.1.121 is expected in late February 2018. We do not expect a fix for CUDA 9.0.x. Until the fix is available, the only workaround is to downgrade to CUDA 8.0.x or disable XLA:GPU.

TensorFlow will print a warning if you use XLA:GPU with a known-bad version of CUDA; see e00ba24c4038e7644da417ddc639169b6ea59122. * The tensorboard command or module may appear to be missing after certain upgrade flows. This is due to pip package conflicts as a result of changing the TensorBoard package name. See the TensorBoard 1.6.0 release notes for a fix.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

4d55397500, Ag Ramesh, Aiden Scandella, Akimasa Kimura, Alex Rothberg, Allen Goodman, amilioto, Andrei Costinescu, Andrei Nigmatulin, Anjum Sayed, Anthony Platanios, Anush Elangovan, Armando Fandango, Ashish Kumar Ram, Ashwini Shukla, Ben, Bhavani Subramanian, Brett Koonce, Carl Thomé, cclauss, Cesc, Changming Sun, Christoph Boeddeker, Clayne Robison, Clemens Schulz, Clint (Woonhyuk Baek), codrut3, Cole Gerdemann, Colin Raffel, Daniel Trebbien, Daniel Ylitalo, Daniel Zhang, Daniyar, Darjan Salaj, Dave Maclachlan, David Norman, Dong--Jian, dongsamb, dssgsra, Edward H, eladweiss, elilienstein, Eric Lilienstein, error.d, Eunji Jeong, fanlu, Florian Courtial, fo40225, Fred, Gregg Helt, Guozhong Zhuang, Hanchen Li, hsm207, hyunyoung2, ImSheridan, Ishant Mrinal Haloi, Jacky Ko, Jay Young, Jean Flaherty, Jerome, JerrikEph, Jesse Kinkead, jfaath, Jian Lin, jinghuangintel, Jiongyan Zhang, Joel Hestness, Joel Shor, Johnny Chan, Julian Niedermeier, Julian Wolff, JxKing, K-W-W, Karl Lessard, Kasper Marstal, Keiji Ariyama, Koan-Sin Tan, Loki Der Quaeler, Loo Rong Jie, Luke Schaefer, Lynn Jackson, ManHyuk, Matt Basta, Matt Smith, Matthew Schulkind, Michael, michaelkhan3, Miguel Piedrafita, Mikalai Drabovich, Mike Knapp, mjwen, mktozk, Mohamed Aly, Mohammad Ashraf Bhuiyan, Myungjoo Ham, Naman Bhalla, Namrata-Ibm, Nathan Luehr, nathansilberman, Netzeband, Niranjan Hasabnis, Omar Aflak, Ozge Yalcinkaya, Parth P Panchal, patrickzzy, Patryk Chrabaszcz, Paul Van Eck, Paweł Kapica, Peng Yu, Philip Yang, Pierre Blondeau, Po-Hsien Chu, powderluv, Puyu Wang, Rajendra Arora, Rasmus, Renat Idrisov, resec, Robin Richtsfeld, Ronald Eddy Jr, Sahil Singh, Sam Matzek, Sami Kama, sandipmgiri, Santiago Castro, Sayed Hadi Hashemi, Scott Tseng, Sergii Khomenko, Shahid, Shengpeng Liu, Shreyash Sharma, Shrinidhi Kl, Simone Cirillo, simsicon, Stanislav Levental, starsblinking, Stephen Lumenta, Steven Hickson, Su Tang, Taehoon Lee, Takuya Wakisaka, Ted Chang, Ted Ying, Tijmen Verhulsdonck, Timofey Kondrashov, vade, vaibhav, Valentin Khrulkov, vchigrin, Victor Costan, Viraj Navkal, Vivek Rane, wagonhelm, Yan Facai (颜发才), Yanbo Liang, Yaroslav Bulatov, yegord, Yong Tang, Yoni Tsafir, yordun, Yuan (Terry) Tang, Yuxin Wu, zhengdi, Zhengsheng Wei, 田传武