Update zlib URL in r0.9 branch (#6952)

See also: #6950 and #6865

RELEASE.md

@@ -43,7 +43,7 @@
 
 This release contains contributions from many people at Google, as well as:
 
-Aaron Schumacher, Aidan Dang, Akihiko ITOH, Aki Sukegawa, Arbit Chen, Aziz Alto, Danijar Hafner, Erik Erwitt, Fabrizio Milo, Felix Maximilian Möller, Henry Saputra, Sung Kim, Igor Babuschkin, Jan Zikes, Jesper Steen Møller, Johannes Mayer, Justin Harris, Kashif Rasul, Kevin Robinson, Loo Rong Jie, Lucas Moura, Łukasz Bieniasz-Krzywiec, Mario Cho, Maxim Grechkin, Michael Heilman, Mostafa Rahmani, Mourad Mourafiq, @ninotoshi, Orion Reblitz-Richardson, Yuncheng Li, @raoqiyu, Robert DiPietro, Sam Abrahams, Sebastian Raschka, Siddharth Agrawal, @snakecharmer1024, Stephen Roller, Sung Kim, SunYeop Lee, Thijs Vogels, Till Hoffmann, Victor Melo, Ville Kallioniemi, Waleed Abdulla, Wenjian Huang, Yaroslav Bulatov, Yeison Rodriguez, Yuan (Terry) Tang, Yuxin Wu, @zhongzyd, Ziming Dong, Zohar Jackson
+Aaron Schumacher, Aidan Dang, Akihiko ITOH, Aki Sukegawa, Arbit Chen, Aziz Alto, Danijar Hafner, Erik Erwitt, Fabrizio Milo, Felix Maximilian Möller, Henry Saputra, Sung Kim, Igor Babuschkin, Jan Zikes, Jeremy Barnes, Jesper Steen Møller, Johannes Mayer, Justin Harris, Kashif Rasul, Kevin Robinson, Loo Rong Jie, Lucas Moura, Łukasz Bieniasz-Krzywiec, Mario Cho, Maxim Grechkin, Michael Heilman, Mostafa Rahmani, Mourad Mourafiq, @ninotoshi, Orion Reblitz-Richardson, Yuncheng Li, @raoqiyu, Robert DiPietro, Sam Abrahams, Sebastian Raschka, Siddharth Agrawal, @snakecharmer1024, Stephen Roller, Sung Kim, SunYeop Lee, Thijs Vogels, Till Hoffmann, Victor Melo, Ville Kallioniemi, Waleed Abdulla, Wenjian Huang, Yaroslav Bulatov, Yeison Rodriguez, Yuan (Terry) Tang, Yuxin Wu, @zhongzyd, Ziming Dong, Zohar Jackson
 
 We are also grateful to all who filed issues or helped resolve them, asked and 
 answered questions, and were part of inspiring discussions. 

tensorflow/g3doc/api_docs/python/control_flow_ops.md

@@ -47,7 +47,7 @@ argument tensors can be computed in parallel, but the values of any tensor
 returned by `tuple` are only available after all the parallel computations
 are done.
 
-See also `group` and `with_dependencies`.
+See also [`tf.group()`](#group) and [`tf.control_dependencies()`](../../api_docs/python/framework.md#control_dependencies).
 
 ##### Args:
 
@@ -77,7 +77,7 @@ Create an op that groups multiple operations.
 When this op finishes, all ops in `input` have finished. This op has no
 output.
 
-See also `tuple` and `with_dependencies`.
+See also [`tf.tuple()`](#tuple) and [`tf.control_dependencies()`](../../api_docs/python/framework.md#control_dependencies).
 
 ##### Args:
 
@@ -745,12 +745,14 @@ Asserts that the given condition is true.
 If `condition` evaluates to false, print the list of tensors in `data`.
 `summarize` determines how many entries of the tensors to print.
 
-NOTE: To ensure that Assert executes, one usually attaches a dependency:
+NOTE: To ensure that Assert executes, one usually attaches a control
+dependency:
 
 ```python
  # Ensure maximum element of x is smaller or equal to 1
 assert_op = tf.Assert(tf.less_equal(tf.reduce_max(x), 1.), [x])
-x = tf.with_dependencies([assert_op], x)
+with tf.control_dependencies([assert_op]):
+  x = tf.identity(x)
 ```
 
 ##### Args:

tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.tuple.md

@@ -14,7 +14,7 @@ argument tensors can be computed in parallel, but the values of any tensor
 returned by `tuple` are only available after all the parallel computations
 are done.
 
-See also `group` and `with_dependencies`.
+See also [`tf.group()`](#group) and [`tf.control_dependencies()`](../../api_docs/python/framework.md#control_dependencies).
 
 ##### Args:
 

tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.group.md

@@ -5,7 +5,7 @@ Create an op that groups multiple operations.
 When this op finishes, all ops in `input` have finished. This op has no
 output.
 
-See also `tuple` and `with_dependencies`.
+See also [`tf.tuple()`](#tuple) and [`tf.control_dependencies()`](../../api_docs/python/framework.md#control_dependencies).
 
 ##### Args:
 

tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.Assert.md

@@ -5,12 +5,14 @@ Asserts that the given condition is true.
 If `condition` evaluates to false, print the list of tensors in `data`.
 `summarize` determines how many entries of the tensors to print.
 
-NOTE: To ensure that Assert executes, one usually attaches a dependency:
+NOTE: To ensure that Assert executes, one usually attaches a control
+dependency:
 
 ```python
  # Ensure maximum element of x is smaller or equal to 1
 assert_op = tf.Assert(tf.less_equal(tf.reduce_max(x), 1.), [x])
-x = tf.with_dependencies([assert_op], x)
+with tf.control_dependencies([assert_op]):
+  x = tf.identity(x)
 ```
 
 ##### Args:

tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md

@@ -201,4 +201,4 @@ For in depth information on how to use the *graph* tab to visualize your graph,
 see [TensorBoard: Graph Visualization](../../how_tos/graph_viz/index.md).
 
 For more usage information on TensorBoard in general, see the [TensorBoard
-Readme](../../../tensorboard/README.md).
+README](https://www.tensorflow.org/code/tensorflow/tensorboard/README.md).

tensorflow/g3doc/tutorials/linear/overview.md

@@ -11,7 +11,7 @@ tools. It explains:
    deep learning to get the advantages of both.
 
 Read this overview to decide whether the tf.learn linear model tools might be
-useful to you. Then do the [Linear Models tutorial](wide/) to
+useful to you. Then do the [Linear Models tutorial](../wide/) to
 give it a try. This overview uses code samples from the tutorial, but the
 tutorial walks through the code in greater detail.
 

tensorflow/g3doc/tutorials/tflearn/index.md

@@ -1,4 +1,4 @@
-## tf.contrib.learn Quickstart
+# tf.contrib.learn Quickstart
 
 TensorFlow’s high-level machine learning API (tf.contrib.learn) makes it easy
 to configure, train, and evaluate a variety of machine learning models. In

tensorflow/g3doc/tutorials/wide/index.md

@@ -16,7 +16,7 @@ To try the code for this tutorial:
 already.
 
 2.  Download [the tutorial code](
-https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py).
+https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/learn/wide_n_deep_tutorial.py).
 
 3.  Install the pandas data analysis library. tf.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
     1. Get `pip`:
@@ -392,7 +392,7 @@ transformations and see if you can do even better!
 
 If you'd like to see a working end-to-end example, you can download our [example
 code]
-(https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py)
+(https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/learn/wide_n_deep_tutorial.py)
 and set the `model_type` flag to `wide`.
 
 ## Adding Regularization to Prevent Overfitting

tensorflow/g3doc/tutorials/wide_and_deep/index.md

@@ -42,7 +42,7 @@ To try the code for this tutorial:
 already.
 
 2.  Download [the tutorial code](
-https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py).
+https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/learn/wide_n_deep_tutorial.py).
 
 3.  Install the pandas data analysis library. tf.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas:
     1. Get `pip`:
@@ -134,7 +134,7 @@ of a neural network in the forward pass. The embedding values are initialized
 randomly, and are trained along with all other model parameters to minimize the
 training loss. If you're interested in learning more about embeddings, check out
 the TensorFlow tutorial on [Vector Representations of Words]
-(https://www.tensorflow.org/versions/r0.9/tutorials/word2vec/index.html), or
+(https://www.tensorflow.org/tutorials/word2vec/index.html), or
 [Word Embedding](https://en.wikipedia.org/wiki/Word_embedding) on Wikipedia.
 
 We'll configure the embeddings for the categorical columns using

tensorflow/python/ops/control_flow_ops.py

@@ -1974,7 +1974,7 @@ def group(*inputs, **kwargs):
   When this op finishes, all ops in `input` have finished. This op has no
   output.
 
-  See also `tuple` and `with_dependencies`.
+  See also [`tf.tuple()`](#tuple) and [`tf.control_dependencies()`](../../api_docs/python/framework.md#control_dependencies).
 
   Args:
     *inputs: Zero or more tensors to group.
@@ -2037,7 +2037,7 @@ def tuple(tensors, name=None, control_inputs=None):
   returned by `tuple` are only available after all the parallel computations
   are done.
 
-  See also `group` and `with_dependencies`.
+  See also [`tf.group()`](#group) and [`tf.control_dependencies()`](../../api_docs/python/framework.md#control_dependencies).
 
   Args:
     tensors: A list of `Tensor`s or `IndexedSlices`, some entries can be `None`.

tensorflow/python/ops/logging_ops.py

@@ -37,12 +37,14 @@ def Assert(condition, data, summarize=None, name=None):
   If `condition` evaluates to false, print the list of tensors in `data`.
   `summarize` determines how many entries of the tensors to print.
 
-  NOTE: To ensure that Assert executes, one usually attaches a dependency:
+  NOTE: To ensure that Assert executes, one usually attaches a control
+  dependency:
 
   ```python
    # Ensure maximum element of x is smaller or equal to 1
   assert_op = tf.Assert(tf.less_equal(tf.reduce_max(x), 1.), [x])
-  x = tf.with_dependencies([assert_op], x)
+  with tf.control_dependencies([assert_op]):
+    x = tf.identity(x)
   ```
 
   Args:

tensorflow/stream_executor/dso_loader.cc

@@ -72,7 +72,7 @@ string GetCudnnVersion() { return ""; }
 }
 
 /* static */ port::Status DsoLoader::GetLibcudaDsoHandle(void** dso_handle) {
-  return GetDsoHandle(FindDsoPath(tensorflow::internal::FormatLibraryFileName("cuda", ""),
+  return GetDsoHandle(FindDsoPath(tensorflow::internal::FormatLibraryFileName("cuda", "1"),
                                   GetCudaDriverLibraryPath()),
                       dso_handle);
 }

tensorflow/workspace.bzl

@@ -187,7 +187,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
 
   native.new_http_archive(
     name = "zlib_archive",
-    url = "http://zlib.net/zlib-1.2.8.tar.gz",
+    url = "http://zlib.net/fossils/zlib-1.2.8.tar.gz",
     sha256 = "36658cb768a54c1d4dec43c3116c27ed893e88b02ecfcb44f2166f9c0b7f2a0d",
     build_file = path_prefix + "zlib.BUILD",
   )