Fixed #6950

Cherry-picking r0.10 fixes

Fixes #4261

* Update installation instructions to use cuDNN v5 across builds.

Documentation updates for r0.10(non-rc) release.

Removing deprecated module

Update the version string to 0.10.0

R0.10

Merge TensorBoard 28 into r0.10

* Clarify that Mac works with cuDNN5.

* Update os_setup.md

updating tutorials/code accordingly.
Change: 130785833

If the notification was set before waiting, the waiter would never wake up.

Added a test to cover this case. Fixes #3978.
Change: 131247138

* Add missing headers to gif BUILD definition.

% :.../gif/giflib-5.1.4/lib$ ls
dgif_lib.c  gif_err.c	gif_hash.h	   Makefile.am	 openbsd-reallocarray.c
egif_lib.c  gif_font.c	gif_lib.h	   Makefile.in	 quantize.c
gifalloc.c  gif_hash.c	gif_lib_private.h  Makefile.unx
Change: 131121931

* cuda_configure: Use env variables that match those used in configure script. (#4034)

Issue #4002

This change reimplements the CUDA autoconfiguration mechanism in Skylark,
providing a `cuda_configure` workspace rule. We keep the same user interface,
the ./configure script, but rather than modifying source files within the
source tree, `cuda_configure` generates a `@local_config_cuda` workspace
containing:

* Symlinks to the CUDA headers and libraries
* BUILD files generated with the correct CUDA and cuDNN versions
* CROSSTOOL config with CUDA include dirs populated
* crosstool_wrapper_driver_is_not_gcc wrapper script with compiler paths and
  CUDA compute capabilities set.
* cuda_config.h header file with CUDA versions and compute capabilities set,
  which can be `#include`d by source files.

This change also makes the following fixes to `Dockerfile.gpu`:
* Change the `CUDNN_INSTALL_PATH` to point to `/usr/lib/x86_64-linux-gnu`
  rather than `/usr/local/cuda` since NVIDIA's image installs `libcudnn.so`
  under `/usr/lib/x86_64-linux-gnu`.
* Add env variable to set the minimum compute capability to 3.0.

Fixes #2873

This change reimplements the CUDA autoconfiguration mechanism in Skylark,
providing a `cuda_configure` workspace rule. We keep the same user interface,
the ./configure script, but rather than modifying source files within the
source tree, `cuda_configure` generates a `@local_config_cuda` workspace
containing:

* Symlinks to the CUDA headers and libraries
* BUILD files generated with the correct CUDA and cuDNN versions
* CROSSTOOL config with CUDA include dirs populated
* crosstool_wrapper_driver_is_not_gcc wrapper script with compiler paths and
  CUDA compute capabilities set.
* cuda_config.h header file with CUDA versions and compute capabilities set,
  which can be `#include`d by source files.

This change also makes the following fixes to `Dockerfile.gpu`:
* Change the `CUDNN_INSTALL_PATH` to point to `/usr/lib/x86_64-linux-gnu`
  rather than `/usr/local/cuda` since NVIDIA's image installs `libcudnn.so`
  under `/usr/lib/x86_64-linux-gnu`.
* Add env variable to set the minimum compute capability to 3.0.

Fixes #2873

Cherrypicks to fix some issues in R0.10RC

Fixes #3752
Change: 130113000

Change: 129887348

update message to ask user to also install scipy

Following the word2vec tutorial instructions I tried "pip3 install sklearn matplotlib" but this didn't install the required "scipy". Adding that to the ImportError message.

>>> from sklearn.manifold import TSNE
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.5/site-packages/sklearn/__init__.py", line 57, in <module>
    from .base import clone
  File "/usr/local/lib/python3.5/site-packages/sklearn/base.py", line 9, in <module>
    from scipy import sparse
ImportError: No module named 'scipy'

Revert "Revert "Merge branch 'r0.10' of https://github.com/tensorflow/tensorf…"

Move TensorBoard 25 into r0.10