Merge changes from github.

PiperOrigin-RevId: 167401527

6 files changed, 20 insertions, 20 deletions

diff --git a/tensorflow/core/profiler/g3doc/advise.md b/tensorflow/core/profiler/g3doc/advise.md
index d87b0d8603..d0de8317f6 100644
--- a/tensorflow/core/profiler/g3doc/advise.md
+++ b/tensorflow/core/profiler/g3doc/advise.md
@@ -86,7 +86,7 @@ For example:
 *   Checks RecvTensor RPC latency and bandwidth.
 *   Checks CPU/Memory utilization of the job.
 
-####AcceleratorUtilization Checker
+#### AcceleratorUtilization Checker
 * Checks what percentage of time the accelerator spends on computation.
 
 #### OperationChecker
@@ -100,7 +100,7 @@ For example:
 *   Checks the most expensive graph nodes.
 *   Checks the most expensive graph-building Python codes.
 
-####Contribute Your Checker
+#### Contribute Your Checker
 
 Follow examples of accelerator_utilization_checker.h
 
diff --git a/tensorflow/core/profiler/g3doc/command_line.md b/tensorflow/core/profiler/g3doc/command_line.md
index 857b5e6459..e2839a682f 100644
--- a/tensorflow/core/profiler/g3doc/command_line.md
+++ b/tensorflow/core/profiler/g3doc/command_line.md
@@ -51,7 +51,7 @@ It defines _checkpoint_variable op type. It also provides checkpointed tensors'
 Note: this feature is not well maintained now.
 
 
-###Start `tfprof`
+### Start `tfprof`
 
 #### Build `tfprof`
 
@@ -140,9 +140,9 @@ tfprof>
 -output
 ```
 
-###Examples
+### Examples
 
-####Profile Python Time
+#### Profile Python Time
 ```shell
 # Requires --graph_path --op_log_path
 tfprof> code -max_depth 1000 -show_name_regexes .*model_analyzer.*py.* -select micros -account_type_regexes .* -order_by micros
diff --git a/tensorflow/core/profiler/g3doc/options.md b/tensorflow/core/profiler/g3doc/options.md
index 15712d04c2..ddee63ad42 100644
--- a/tensorflow/core/profiler/g3doc/options.md
+++ b/tensorflow/core/profiler/g3doc/options.md
@@ -1,6 +1,6 @@
-##Options
+## Options
 
-###Overview
+### Overview
 
 For all tfprof views, the profiles are processed with the following procedures
 
@@ -35,7 +35,7 @@ For all tfprof views, the profiles are processed with the following procedures
 4) Finally, the filtered data structure is output in a format depending
    on the `-output` option.
 
-####Option Semantics In Different View
+#### Option Semantics In Different View
 options usually have the same semantics in different views. However, some
 can vary. For example `-max_depth` in scope view means the depth of
 name scope <b>tree</b>. In op view, it means the length of operation <b>list</b>.
@@ -68,7 +68,7 @@ output_bytes: The memory output by the operation. It's not necessarily requested
               by the current operation. For example, it can be a tensor
               forwarded from input to output, with in-place mutation.
 
-###Docs
+### Docs
 
 `-max_depth`: Show nodes that are at most this number of hops from starting node in the data structure.
 
diff --git a/tensorflow/core/profiler/g3doc/profile_memory.md b/tensorflow/core/profiler/g3doc/profile_memory.md
index a00683d062..6eda5abdd9 100644
--- a/tensorflow/core/profiler/g3doc/profile_memory.md
+++ b/tensorflow/core/profiler/g3doc/profile_memory.md
@@ -1,4 +1,4 @@
-##Profile Memory
+## Profile Memory
 
 It is generally a good idea to visualize the memory usage in timeline.
 It allows you to see the memory consumption of each GPU over time.
diff --git a/tensorflow/core/profiler/g3doc/profile_model_architecture.md b/tensorflow/core/profiler/g3doc/profile_model_architecture.md
index a42b2e918d..61bb66bd21 100644
--- a/tensorflow/core/profiler/g3doc/profile_model_architecture.md
+++ b/tensorflow/core/profiler/g3doc/profile_model_architecture.md
@@ -1,9 +1,9 @@
-##Profile Model Architecture
+## Profile Model Architecture
 
 * [Profile Model Parameters](#profile-model-parameters)
 * [Profile Model Float Operations](#profile-model-float-operations)
 
-###Profile Model Parameters
+### Profile Model Parameters
 
 <b>Notes:</b>
 `VariableV2` operation type might contain variables created by TensorFlow
@@ -39,9 +39,9 @@ param_stats = tf.profiler.profile(
 sys.stdout.write('total_params: %d\n' % param_stats.total_parameters)
 ```
 
-###Profile Model Float Operations
+### Profile Model Float Operations
 
-####Caveats
+#### Caveats
 
 For an operation to have float operation statistics:
 
diff --git a/tensorflow/core/profiler/g3doc/profile_time.md b/tensorflow/core/profiler/g3doc/profile_time.md
index e11a75553b..4aafc697a9 100644
--- a/tensorflow/core/profiler/g3doc/profile_time.md
+++ b/tensorflow/core/profiler/g3doc/profile_time.md
@@ -1,4 +1,4 @@
-##Profile Time
+## Profile Time
 
 * [Times in TensorFlow and tfprof](#times-in-tensorflow-and-tfprof)
 * [Profile by Python Code](#profile-by-python-code)
@@ -7,7 +7,7 @@
 * [Profile by Name Scope](#profile-by-name-scope)
 
 
-###Times in TensorFlow and tfprof
+### Times in TensorFlow and tfprof
 When we run a model, Tensorflow schedules and runs the nodes (operations)
 in the graph. An operation can be placed on an accelerator or on CPU.
 
@@ -37,7 +37,7 @@ When an operation is placed on CPU, it will completely run on CPU. Hence,
 should be 0.
 
 
-###Profile by Python Code
+### Profile by Python Code
 ```python
 # In code view, the time of each line of Python code is the aggregated
 # times of all operations created by that line.
@@ -112,7 +112,7 @@ Set ```-output timeline:outfile=<filename>``` to generate timeline instead of st
 </left>
 
 
-###Profile by Operation Type
+### Profile by Operation Type
 ```python
 # In op view, you can view the aggregated time of each operation type.
 tfprof> op -select micros,occurrence -order_by micros
@@ -138,7 +138,7 @@ MatMul                        618.97ms (63.56%, 16.51%), |/job:worker/replica:0/
 ```
 
 
-###Profile by Graph
+### Profile by Graph
 
 Usually, use graph view to generate a timeline to visualize the result.
 
@@ -163,7 +163,7 @@ Open a Chrome browser, enter URL chrome://tracing and load the timeline file.
 ******************************************************
 ```
 
-###Profile by Name Scope
+### Profile by Name Scope
 
 Usually scope view allows you to pin point the problematic places if you
 have properly named your operations with tf.name_scope or tf.variable_scope.