Upstream a number of changes to git.

Changes:
- Updates to README / os_setup for common installation issues
  by ebrevdo, vrv, wicke, anelia, dga

- Add a test for backwards compatibility checks by Josh

- Simplifications to tiling/padding of scalars by bsteiner

- Improve listing of devices when no devices are known by Matthieu

Base CL: 107607137

9 files changed, 512 insertions, 15 deletions

diff --git a/README.md b/README.md
index 1a53403d85..c581442e79 100644
--- a/README.md
+++ b/README.md
@@ -31,7 +31,9 @@ installing from source, GPU-enabled support, etc., see
 
 ## Binary Installation
 
-The TensorFlow Python API requires Python 2.7.
+The TensorFlow Python API currently requires Python 2.7: we are
+[working](https://github.com/tensorflow/tensorflow/issues/1) on adding support
+for Python 3.0.
 
 The simplest way to install TensorFlow is using
 [pip](https://pypi.python.org/pypi/pip) for both Linux and Mac.
@@ -40,7 +42,7 @@ For the GPU-enabled version, or if you encounter installation errors, or for
 more detailed installation instructions, see
 [here](tensorflow/g3doc/get_started/os_setup.md#detailed_install).
 
-### Ubuntu/Linux
+### Ubuntu/Linux 64-bit
 
 ```bash
 # For CPU-only version
diff --git a/tensorflow/core/common_runtime/local_session.cc b/tensorflow/core/common_runtime/local_session.cc
index ab6993b8a2..4c41e5b3b3 100644
--- a/tensorflow/core/common_runtime/local_session.cc
+++ b/tensorflow/core/common_runtime/local_session.cc
@@ -103,7 +103,11 @@ LocalSession::LocalSession(const SessionOptions& options,
   int devices_added = 0;
   if (options.config.log_device_placement()) {
     const string mapping_str = device_mgr_->DeviceMappingString();
-    printf("Device mapping:\n%s", mapping_str.c_str());
+    if (mapping_str.empty()) {
+      printf("Device mapping: no known devices.\n");
+    } else {
+      printf("Device mapping:\n%s", mapping_str.c_str());
+    }
     LOG(INFO) << "Device mapping:\n" << mapping_str;
   }
   for (auto d : device_mgr_->ListDevices()) {
diff --git a/tensorflow/core/framework/op_compatibility_test.cc b/tensorflow/core/framework/op_compatibility_test.cc
new file mode 100644
index 0000000000..068130136b
--- /dev/null
+++ b/tensorflow/core/framework/op_compatibility_test.cc
@@ -0,0 +1,422 @@
+// Test that verifies that various changes to an OpDef are
+// backwards-compatible.
+
+#include "tensorflow/core/framework/fake_input.h"
+#include <gtest/gtest.h>
+#include "tensorflow/core/framework/node_def_builder.h"
+#include "tensorflow/core/framework/node_def_util.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/kernels/ops_testutil.h"
+
+namespace tensorflow {
+namespace {
+
+class TestKernel : public OpKernel {
+ public:
+  explicit TestKernel(OpKernelConstruction* context) : OpKernel(context) {}
+  void Compute(OpKernelContext* context) override {
+    Tensor* out_tensor = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output("ndef", TensorShape({}),
+                                                     &out_tensor));
+    out_tensor->scalar<string>()() = SummarizeNodeDef(def());
+  }
+};
+
+class OpCompatibilityTest : public OpsTestBase {
+ protected:
+  const OpDef* RegisteredOpDef() {
+    Status status;
+    const OpDef* new_op_def =
+        OpRegistry::Global()->LookUp(node_def()->op(), &status);
+    TF_CHECK_OK(status);
+    return new_op_def;
+  }
+
+  void Run(const OpDef& old_op_def) {
+    // Record the original signature before we change *node_def().
+    DataTypeVector old_in_types, old_out_types;
+    ASSERT_OK(InOutTypesForNode(*node_def(), old_op_def, &old_in_types,
+                                &old_out_types));
+
+    // This should be all that is needed to get compatiblity.
+    const OpDef* new_op_def = RegisteredOpDef();
+    AddDefaultsToNodeDef(*new_op_def, node_def());
+
+    // Validate that it is indeed compatible.
+    ASSERT_OK(ValidateNodeDef(*node_def(), *new_op_def));
+    DataTypeVector new_in_types, new_out_types;
+    ASSERT_OK(InOutTypesForNode(*node_def(), *new_op_def, &new_in_types,
+                                &new_out_types));
+    ASSERT_EQ(new_in_types, old_in_types);
+    ASSERT_EQ(new_out_types, old_out_types);
+
+    // Verify the Op actually runs.  Result() will return the output.
+    ASSERT_OK(InitOp());
+    ASSERT_OK(RunOpKernel());
+  }
+
+  string Result() { return GetOutput(0)->scalar<string>()(); }
+};
+
+// Should be compatible if the Op hasn't changed (sanity check).
+REGISTER_OP("Same")
+    .Input("a: int32")
+    .Input("b: T")
+    .Input("c: N * int32")
+    .Input("d: N * T")
+    .Input("e: TList")
+    .Output("ndef: string")
+    .Attr("T: type")
+    .Attr("N: int")
+    .Attr("TList: list(type)");
+REGISTER_KERNEL_BUILDER(Name("Same").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, Same) {
+  ASSERT_OK(NodeDefBuilder("same", "Same")
+                .Input(FakeInput())
+                .Input(FakeInput(DT_FLOAT))
+                .Input(FakeInput(3))
+                .Input(FakeInput(3, DT_FLOAT))
+                .Input(FakeInput(2, DT_BOOL))
+                .Finalize(node_def()));
+  Run(*RegisteredOpDef());
+  EXPECT_EQ(
+      "same = Same[N=3, T=DT_FLOAT, TList=[DT_BOOL, DT_BOOL]](a, b, c, c:1, "
+      "c:2, d, d:1, d:2, e, e:1)",
+      Result());
+}
+
+// Should be able to add an attr with a default.
+REGISTER_OP("AddAttr").Output("ndef: string").Attr("a: int = 42");
+REGISTER_KERNEL_BUILDER(Name("AddAttr").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, AddAttr) {
+  OpDef old_op_def;
+  ASSERT_OK(
+      OpDefBuilder("AddAttr").Output("ndef: string").Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("add_attr", &old_op_def).Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("add_attr = AddAttr[a=42]()", Result());
+}
+
+// Should be able to make an attr restriction less strict.
+REGISTER_OP("LessStrict").Output("ndef: string").Attr("a: {'A', 'B', 'C'}");
+REGISTER_KERNEL_BUILDER(Name("LessStrict").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, LessStrict) {
+  OpDef old_op_def;
+  ASSERT_OK(OpDefBuilder("LessStrict")
+                .Output("ndef: string")
+                .Attr("a: {'A', 'B'}")
+                .Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("less_strict", &old_op_def)
+                .Attr("a", "B")
+                .Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("less_strict = LessStrict[a=\"B\"]()", Result());
+}
+
+// Should be able to remove an attr restriction.
+REGISTER_OP("RemoveRestriction").Output("ndef: string").Attr("a: type");
+REGISTER_KERNEL_BUILDER(Name("RemoveRestriction").Device(DEVICE_CPU),
+                        TestKernel);
+
+TEST_F(OpCompatibilityTest, RemoveRestriction) {
+  OpDef old_op_def;
+  ASSERT_OK(OpDefBuilder("RemoveRestriction")
+                .Output("ndef: string")
+                .Attr("a: {int32, bool}")
+                .Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("remove_restriction", &old_op_def)
+                .Attr("a", DT_INT32)
+                .Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("remove_restriction = RemoveRestriction[a=DT_INT32]()", Result());
+}
+
+// Should be able to change the order of attrs.
+REGISTER_OP("AttrOrder").Output("ndef: string").Attr("a: int").Attr("b: bool");
+REGISTER_KERNEL_BUILDER(Name("AttrOrder").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, AttrOrder) {
+  OpDef old_op_def;
+  ASSERT_OK(OpDefBuilder("AttrOrder")
+                .Output("ndef: string")
+                .Attr("b: bool")
+                .Attr("a: int")
+                .Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("attr_order", &old_op_def)
+                .Attr("b", true)
+                .Attr("a", 7)
+                .Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("attr_order = AttrOrder[a=7, b=true]()", Result());
+}
+
+// Should be able to add a default to an attr.
+REGISTER_OP("AddDefault").Output("ndef: string").Attr("a: int = 1234");
+REGISTER_KERNEL_BUILDER(Name("AddDefault").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, AddDefault) {
+  OpDef old_op_def;
+  ASSERT_OK(OpDefBuilder("AddDefault")
+                .Output("ndef: string")
+                .Attr("a: int")
+                .Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("add_default", &old_op_def)
+                .Attr("a", 765)
+                .Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("add_default = AddDefault[a=765]()", Result());
+}
+
+// Should be able to remove a default from an attr, *as long as that
+// attr has always existed*.
+REGISTER_OP("RemoveDefault").Output("ndef: string").Attr("a: int");
+REGISTER_KERNEL_BUILDER(Name("RemoveDefault").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, RemoveDefault) {
+  OpDef old_op_def;
+  ASSERT_OK(OpDefBuilder("RemoveDefault")
+                .Output("ndef: string")
+                .Attr("a: int = 91")
+                .Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("remove_default", &old_op_def).Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("remove_default = RemoveDefault[a=91]()", Result());
+}
+
+// Should be able to make an input polymorphic.
+// Changing from int32 -> T (where T: type = DT_INT32 by default).
+REGISTER_OP("TypePolymorphic")
+    .Input("a: T")
+    .Output("ndef: string")
+    .Attr("T: type = DT_INT32");
+REGISTER_KERNEL_BUILDER(Name("TypePolymorphic").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, TypePolymorphic) {
+  OpDef old_op_def;
+  ASSERT_OK(OpDefBuilder("TypePolymorphic")
+                .Input("a: int32")
+                .Output("ndef: string")
+                .Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("type_polymorphic", &old_op_def)
+                .Input(FakeInput())
+                .Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("type_polymorphic = TypePolymorphic[T=DT_INT32](a)", Result());
+}
+
+// Should be able to make a single input into a list.
+// Changing from int32 -> N * int32 (where N: int = 1 by default).
+REGISTER_OP("MakeList")
+    .Input("a: N * int32")
+    .Output("ndef: string")
+    .Attr("N: int = 1");
+REGISTER_KERNEL_BUILDER(Name("MakeList").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, MakeList) {
+  OpDef old_op_def;
+  ASSERT_OK(OpDefBuilder("MakeList")
+                .Input("a: int32")
+                .Output("ndef: string")
+                .Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("make_list", &old_op_def)
+                .Input(FakeInput())
+                .Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("make_list = MakeList[N=1](a)", Result());
+}
+
+// Should be able to make a single input into a polymorphic list.
+// Changing from int32 -> N * T (where N: int = 1 by default and
+//                                     T: type = DT_INT32 by default).
+REGISTER_OP("MakePolyList")
+    .Input("a: N * T")
+    .Output("ndef: string")
+    .Attr("N: int = 1")
+    .Attr("T: type = DT_INT32");
+REGISTER_KERNEL_BUILDER(Name("MakePolyList").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, MakePolyList) {
+  OpDef old_op_def;
+  ASSERT_OK(OpDefBuilder("MakePolyList")
+                .Input("a: int32")
+                .Output("ndef: string")
+                .Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("make_poly_list", &old_op_def)
+                .Input(FakeInput())
+                .Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("make_poly_list = MakePolyList[N=1, T=DT_INT32](a)", Result());
+}
+
+// Should be able to make a single input into an arbitrary list.
+// Changing from int32 -> T (where T: list(type) = [DT_INT32] by default).
+REGISTER_OP("MakeAnyList")
+    .Input("a: T")
+    .Output("ndef: string")
+    .Attr("T: list(type) = [DT_INT32]");
+REGISTER_KERNEL_BUILDER(Name("MakeAnyList").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, MakeAnyList) {
+  OpDef old_op_def;
+  ASSERT_OK(OpDefBuilder("MakeAnyList")
+                .Input("a: int32")
+                .Output("ndef: string")
+                .Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("make_any_list", &old_op_def)
+                .Input(FakeInput())
+                .Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("make_any_list = MakeAnyList[T=[DT_INT32]](a)", Result());
+}
+
+// Should be able to make a single polymorphic input into a list of
+// the same type.  Changing from T -> N * T (where N: int = 1 by default).
+REGISTER_OP("PolyIntoList")
+    .Input("a: N * T")
+    .Output("ndef: string")
+    .Attr("N: int = 1")
+    .Attr("T: type");
+REGISTER_KERNEL_BUILDER(Name("PolyIntoList").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, PolyIntoList) {
+  OpDef old_op_def;
+  ASSERT_OK(OpDefBuilder("PolyIntoList")
+                .Input("a: T")
+                .Output("ndef: string")
+                .Attr("T: type")
+                .Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("poly_into_list", &old_op_def)
+                .Input(FakeInput(DT_INT32))
+                .Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("poly_into_list = PolyIntoList[N=1, T=DT_INT32](a)", Result());
+}
+
+// Should be able to change the name of an input.
+REGISTER_OP("ChangeName").Input("y: int32").Output("ndef: string");
+REGISTER_KERNEL_BUILDER(Name("ChangeName").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, ChangeName) {
+  OpDef old_op_def;
+  ASSERT_OK(OpDefBuilder("ChangeName")
+                .Input("x: int32")
+                .Output("ndef: string")
+                .Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("change_name", &old_op_def)
+                .Input(FakeInput())
+                .Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("change_name = ChangeName[](a)", Result());
+}
+
+// Should be able to add an input of type
+// N * int32 (where N: int = 0 by default).
+REGISTER_OP("AddNInts")
+    .Input("a: N * int32")
+    .Output("ndef: string")
+    .Attr("N: int >= 0 = 0");
+REGISTER_KERNEL_BUILDER(Name("AddNInts").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, AddNInts) {
+  OpDef old_op_def;
+  ASSERT_OK(
+      OpDefBuilder("AddNInts").Output("ndef: string").Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("add_n_ints", &old_op_def).Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("add_n_ints = AddNInts[N=0]()", Result());
+}
+
+// Should be able to add an input of type N * T
+// (where N: int = 0 by default, and T: type = any valid default).
+REGISTER_OP("AddNSame")
+    .Input("a: N * T")
+    .Output("ndef: string")
+    .Attr("N: int >= 0 = 0")
+    .Attr("T: type = DT_BOOL");
+REGISTER_KERNEL_BUILDER(Name("AddNSame").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, AddNSame) {
+  OpDef old_op_def;
+  ASSERT_OK(
+      OpDefBuilder("AddNSame").Output("ndef: string").Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("add_n_same", &old_op_def).Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("add_n_same = AddNSame[N=0, T=DT_BOOL]()", Result());
+}
+
+// Should be able to add an input of type T
+// (where T: list(type) = [] by default).
+REGISTER_OP("AddAnyList")
+    .Input("a: T")
+    .Output("ndef: string")
+    .Attr("T: list(type) >= 0 = []");
+REGISTER_KERNEL_BUILDER(Name("AddAnyList").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, AddAnyList) {
+  OpDef old_op_def;
+  ASSERT_OK(
+      OpDefBuilder("AddAnyList").Output("ndef: string").Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("add_any_list", &old_op_def).Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("add_any_list = AddAnyList[T=[]]()", Result());
+}
+
+// Should be able to allow shorter lists.
+REGISTER_OP("ShorterAnyList")
+    .Input("a: T")
+    .Output("ndef: string")
+    .Attr("T: list(type) >= 1");
+REGISTER_KERNEL_BUILDER(Name("ShorterAnyList").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, ShorterAnyList) {
+  OpDef old_op_def;
+  ASSERT_OK(OpDefBuilder("ShorterAnyList")
+                .Input("a: T")
+                .Output("ndef: string")
+                .Attr("T: list(type) >= 2")
+                .Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("shorter_any_list", &old_op_def)
+                .Input(FakeInput(2, DT_BOOL))
+                .Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("shorter_any_list = ShorterAnyList[T=[DT_BOOL, DT_BOOL]](a, a:1)",
+            Result());
+}
+
+REGISTER_OP("ShorterSameList")
+    .Input("a: N * int32")
+    .Output("ndef: string")
+    .Attr("N: int >= 1");
+REGISTER_KERNEL_BUILDER(Name("ShorterSameList").Device(DEVICE_CPU), TestKernel);
+
+TEST_F(OpCompatibilityTest, ShorterSameList) {
+  OpDef old_op_def;
+  ASSERT_OK(OpDefBuilder("ShorterSameList")
+                .Input("a: N * int32")
+                .Output("ndef: string")
+                .Attr("N: int >= 2")
+                .Finalize(&old_op_def));
+  ASSERT_OK(NodeDefBuilder("shorter_same_list", &old_op_def)
+                .Input(FakeInput(2))
+                .Finalize(node_def()));
+  Run(old_op_def);
+  EXPECT_EQ("shorter_same_list = ShorterSameList[N=2](a, a:1)", Result());
+}
+
+// TODO(josh11b): Negative tests?
+// * add attr w/out default
+// * add non-list input/output
+// * add list input/output with non-empty default
+// * change type of input/output
+// * change order of input/output
+// * change type of attr
+// * Input("foo: T").Attr("T: type") -> Input("foo: T").Attr("T: list(type)")
+
+// What about changing an attr's default? Not technically illegal, but
+// likely should be forbidden since it likely affects semantics.
+
+}  // namespace
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/pad_op.h b/tensorflow/core/kernels/pad_op.h
index c4f8a4abda..204a2a13a4 100644
--- a/tensorflow/core/kernels/pad_op.h
+++ b/tensorflow/core/kernels/pad_op.h
@@ -21,6 +21,15 @@ struct Pad {
   }
 };
 
+template <typename Device, typename T>
+struct Pad<Device, T, 0> {
+  // In the scalar case we simply copy the input.
+  void operator()(const Device& d, typename TTypes<T, 0>::Tensor output,
+                  typename TTypes<T, 0>::ConstTensor input,
+                  Eigen::array<std::pair<int32, int32>, 0>) {
+    output.device(d) = input;
+  }
+};
 }  // namespace functor
 }  // namespace tensorflow
 
diff --git a/tensorflow/core/kernels/tile_ops.h b/tensorflow/core/kernels/tile_ops.h
index b3cc6165e0..41c2deb42d 100644
--- a/tensorflow/core/kernels/tile_ops.h
+++ b/tensorflow/core/kernels/tile_ops.h
@@ -17,6 +17,16 @@ struct Tile {
   }
 };
 
+template <typename Device, typename T>
+struct Tile<Device, T, 0> {
+  void operator()(const Device& d, typename TTypes<T, 0>::Tensor out,
+                  typename TTypes<T, 0>::ConstTensor in,
+                  const Eigen::array<int32, 0>&) const {
+    // In the scalar case we simply copy the input.
+    out.device(d) = in;
+  }
+};
+
 template <typename Device, typename T, int NDIM>
 struct TileGrad {
   void operator()(const Device& d, typename TTypes<T, NDIM>::Tensor out,
@@ -32,6 +42,20 @@ struct TileGrad {
   }
 };
 
+template <typename Device, typename T>
+struct TileGrad<Device, T, 0> {
+  void operator()(const Device& d, typename TTypes<T, 0>::Tensor out,
+                  typename TTypes<T, 0>::ConstTensor in,
+                  const Eigen::DSizes<ptrdiff_t, 0>&,
+                  const Eigen::DSizes<ptrdiff_t, 0>&, bool first) const {
+    if (first) {
+      out.device(d) = in;
+    } else {
+      out.device(d) += in;
+    }
+  }
+};
+
 template <typename Device, typename T, int NDIM, int REDUCEDNDIM>
 struct ReduceAndReshape {
   void operator()(const Device& d, typename TTypes<T, NDIM>::Tensor out,
diff --git a/tensorflow/g3doc/api_docs/python/io_ops.md b/tensorflow/g3doc/api_docs/python/io_ops.md
index 9426cce788..a728ba579e 100644
--- a/tensorflow/g3doc/api_docs/python/io_ops.md
+++ b/tensorflow/g3doc/api_docs/python/io_ops.md
@@ -1223,9 +1223,10 @@ And the expected output is:
 ##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a>
 
 
-*  <b>`serialized`</b>: A list of strings, a batch of binary serialized `Example`
-    protos.
-*  <b>`names`</b>: A list of strings, the names of the serialized protos.
+*  <b>`serialized`</b>: A vector (1-D Tensor) of strings, a batch of binary
+    serialized `Example` protos.
+*  <b>`names`</b>: A vector (1-D Tensor) of strings (optional), the names of
+    the serialized protos.
 *  <b>`sparse_keys`</b>: A list of string keys in the examples' features.
     The results for these keys will be returned as `SparseTensor` objects.
 *  <b>`sparse_types`</b>: A list of `DTypes` of the same length as `sparse_keys`.
@@ -1275,9 +1276,9 @@ See also `parse_example`.
 ##### Args: <a class="md-anchor" id="AUTOGENERATED-args-"></a>
 
 
-*  <b>`serialized`</b>: A scalar string, a single serialized Example.
+*  <b>`serialized`</b>: A scalar string Tensor, a single serialized Example.
     See parse_example documentation for more details.
-*  <b>`names`</b>: (Optional) A scalar string, the associated name.
+*  <b>`names`</b>: (Optional) A scalar string Tensor, the associated name.
     See parse_example documentation for more details.
 *  <b>`sparse_keys`</b>: See parse_example documentation for more details.
 *  <b>`sparse_types`</b>: See parse_example documentation for more details.
diff --git a/tensorflow/g3doc/get_started/basic_usage.md b/tensorflow/g3doc/get_started/basic_usage.md
index df4f769983..3799fc2ffc 100644
--- a/tensorflow/g3doc/get_started/basic_usage.md
+++ b/tensorflow/g3doc/get_started/basic_usage.md
@@ -239,7 +239,7 @@ training graph repeatedly.
 
 To fetch the outputs of operations, execute the graph with a `run()` call on
 the `Session` object and pass in the tensors to retrieve. In the previous
-example we fetched the single node `var`, but you can also fetch multiple
+example we fetched the single node `state`, but you can also fetch multiple
 tensors:
 
 ```python
@@ -249,7 +249,7 @@ input3 = tf.constant(5.0)
 intermed = tf.add(input2, input3)
 mul = tf.mul(input1, intermed)
 
-with tf.Session():
+with tf.Session() as sess:
   result = sess.run([mul, intermed])
   print result
 
diff --git a/tensorflow/g3doc/get_started/os_setup.md b/tensorflow/g3doc/get_started/os_setup.md
index 1b08c6b89f..713606b29f 100644
--- a/tensorflow/g3doc/get_started/os_setup.md
+++ b/tensorflow/g3doc/get_started/os_setup.md
@@ -4,7 +4,9 @@ You can install TensorFlow using our provided binary packages or from source.
 
 ## Binary Installation <a class="md-anchor" id="AUTOGENERATED-binary-installation"></a>
 
-The TensorFlow Python API requires Python 2.7.
+The TensorFlow Python API currently requires Python 2.7: we are
+[working](https://github.com/tensorflow/tensorflow/issues/1) on adding support
+for Python 3.0.
 
 The simplest way to install TensorFlow is using
 [pip](https://pypi.python.org/pypi/pip) for both Linux and Mac.
@@ -14,7 +16,7 @@ If you encounter installation errors, see
 installation, please consider using our virtualenv-based instructions
 [here](#virtualenv_install).
 
-### Ubuntu/Linux <a class="md-anchor" id="AUTOGENERATED-ubuntu-linux"></a>
+### Ubuntu/Linux 64-bit <a class="md-anchor" id="AUTOGENERATED-ubuntu-linux-64-bit"></a>
 
 ```bash
 # For CPU-only version
@@ -96,10 +98,18 @@ $ source bin/activate.csh  # If using csh
 Inside the virtualenv, install TensorFlow:
 
 ```bash
-(tensorflow)$ pip install --upgrade <$url_to_binary.whl>
+# For CPU-only linux x86_64 version
+(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
+
+# For GPU-enabled linux x86_64 version
+(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
+
+# For Mac CPU-only version
+(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
 ```
 
-You can then run your TensorFlow program like:
+Make sure you have downloaded the source code for TensorFlow, and then you can
+then run an example TensorFlow program like:
 
 ```bash
 (tensorflow)$ cd tensorflow/models/image/mnist
@@ -343,6 +353,30 @@ ImportError: libcudart.so.7.0: cannot open shared object file: No such file or d
 
 Make sure you followed the the GPU installation [instructions](#install_cuda).
 
+### Pip installation issues <a class="md-anchor" id="AUTOGENERATED-pip-installation-issues"></a>
+
+#### Can't find setup.py <a class="md-anchor" id="AUTOGENERATED-can-t-find-setup.py"></a>
+
+If, during pip install, you encounter an error like:
+
+```bash
+...
+IOError: [Errno 2] No such file or directory: '/tmp/pip-o6Tpui-build/setup.py'
+```
+
+Solution: upgrade your version of pip.
+
+#### SSLError: SSL_VERIFY_FAILED <a class="md-anchor" id="AUTOGENERATED-sslerror--ssl_verify_failed"></a>
+
+If, during pip install from a URL, you encounter an error like:
+
+```bash
+...
+SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed
+```
+
+Solution: Download the wheel manually via curl or wget, and pip install locally.
+
 ### On Linux <a class="md-anchor" id="AUTOGENERATED-on-linux"></a>
 
 If you encounter:
diff --git a/tensorflow/g3doc/tutorials/mnist/mnist_softmax.py b/tensorflow/g3doc/tutorials/mnist/mnist_softmax.py
index a90f1ea685..c766af474c 100644
--- a/tensorflow/g3doc/tutorials/mnist/mnist_softmax.py
+++ b/tensorflow/g3doc/tutorials/mnist/mnist_softmax.py
@@ -1,6 +1,7 @@
 """A very simple MNIST classifer.
 
-See extensive documentation at ??????? (insert public URL)
+See extensive documentation at
+http://tensorflow.org/tutorials/mnist/beginners/index.md
 """
 from __future__ import print_function