1 files changed, 67 insertions, 11 deletions
diff --git a/tensorflow/contrib/tfprof/README.md b/tensorflow/contrib/tfprof/README.md
index 5bfa0247a5..d891ecdc9a 100644
--- a/tensorflow/contrib/tfprof/README.md
+++ b/tensorflow/contrib/tfprof/README.md
@@ -2,25 +2,81 @@
 
 # Full Docment in tensorflow/tools/tfprof/README.md
 
-Author: Xin Pan (xpan@google.com, github: panyx0718), Jon Shlens, Yao Zhang
+Author: Xin Pan (xpan@google.com, github: panyx0718)
 
 Consultants: Jon Shlens, Pete Warden
 
 ###Major Features
 
 1.  Measure model parameters, float operations, tensor shapes.
-2.  Profile op execution times, requested memory size and device placement.
+2.  Measure op execution times, requested memory size and device placement.
 3.  Inspect checkpoint tensors' shapes and their values.
-4.  Selectively group, filter, account and order ops.
+4.  3 ways to view and explore TensorFlow model profiles
 
-####tfprof supports 3 views to organize TensorFlow model profiles
+    *  Organize by Python code call stack.
+    *  Organize by TensorFlow operation name scope hierarchies.
+    *  Organize by TensorFlow operation inputs/outputs graph.
 
-    *  code view: Stats are associated your Python codes and organized as call stacks.
-    *  scope view: Stats are organized as name scope hierarchies.
-    *  graph view: Stats are organized as Tensorflow Op graph.
+5.  Selectively grouping/filtering/accounting/ordering ops.
 
-####For each view, there are 3 ways to display outputs:
+tfprof can be used as Python API, Interactive CLI and One-shot Script.
 
-    *  stdout: Results are written to stdout.
-    *  timeline: Visualized in chrome browser as time series.
-    *  file: Results are dumped to file.
+## Python API Tutorials
+
+tfprof is part of TensorFlow core. Simply ```import tensorflow as tf```.
+
+### Examine the shapes and sizes of all trainiable Variables.
+```python
+# Print trainable variable parameter statistics to stdout.
+param_stats = tf.contrib.tfprof.model_analyzer.print_model_analysis(
+    tf.get_default_graph(),
+    tfprof_options=tf.contrib.tfprof.model_analyzer.
+        TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
+
+# param_stats is tensorflow.tfprof.TFGraphNodeProto proto.
+# It organize the statistics
+# of each graph node in tree scructure. Let's print the root below.
+sys.stdout.write('total_params: %d\n' % param_stats.total_parameters)
+```
+
+### Examine the number of floating point operations
+``` python
+# Print to stdout an analysis of the number of floating point operations in the
+# model broken down by individual operations.
+#
+# Note: Only Ops with RegisterStatistics('flops') defined have flop stats. It
+# also requires complete shape information. It is common that shape is unknown
+# statically. To complete the shape, provide run-time shape information with
+# tf.RunMetadata to the API (See next example on how to provide RunMetadata).
+tf.contrib.tfprof.model_analyzer.print_model_analysis(
+    tf.get_default_graph(),
+    tfprof_options=tf.contrib.tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
+```
+
+### Examine the timing and memory usage
+You will first need to run the following set up in your model in order to
+compute the memory and timing statistics.
+
+```python
+# Generate the meta information for the model that contains the memory usage
+# and timing information.
+run_metadata = tf.RunMetadata()
+with tf.Session() as sess:
+  _ = sess.run(train_op,
+               options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),
+               run_metadata=run_metadata)
+```
+
+Finally, you may run `print_model_analysis` to explore the timing and memory
+demands of the model.
+
+``` python
+# Print to stdout an analysis of the memory usage and the timing information
+# from running the graph broken down by operations.
+tf.contrib.tfprof.model_analyzer.print_model_analysis(
+    tf.get_default_graph(),
+    run_meta=run_metadata,
+    tfprof_options=tf.contrib.tfprof.model_analyzer.PRINT_ALL_TIMING_MEMORY)
+```
+
+Users can change ```tfprof_options``` to fully leverage tfprof's power.