Avijit/18.1-rc0

README.md

@@ -88,7 +88,7 @@ Once TensorFlow's dependencies are installed, clone the `ngraph-bridge` repo:
 
         git clone https://github.com/tensorflow/ngraph-bridge.git
         cd ngraph-bridge
-        git checkout v0.18.0
+        git checkout v0.18.1-rc0
 
 Run the following Python script to build TensorFlow, nGraph, and the bridge. Use Python 3.5:
 
@@ -127,8 +127,8 @@ Verify that `ngraph-bridge` installed correctly:
     This will produce something like this:
 
         TensorFlow version:  <1.14.0>
-        nGraph bridge version: <b'0.18.0'>
-        nGraph version used for this build: b'0.25.0+005c118'
+        nGraph bridge version: <version number>
+        nGraph version used for this build: b'Version number'
         TensorFlow version used for this build: <v1.14.0-...>
         CXX11_ABI flag used for this build: 0
         nGraph bridge built with Grappler: False

ngraph_bridge/ngraph_freshness_tracker.cc

@@ -26,7 +26,7 @@ namespace ngraph_bridge {
 
 void NGraphFreshnessTracker::MarkFresh(
     const void* base_pointer,
-    std::shared_ptr<ngraph::runtime::Executable> user) {
+    const std::shared_ptr<ngraph::runtime::Executable>& user) {
   mutex_lock l(mu_);
   auto it = freshness_map_.find(base_pointer);
   if (it != freshness_map_.end()) {
@@ -36,7 +36,7 @@ void NGraphFreshnessTracker::MarkFresh(
 
 bool NGraphFreshnessTracker::IsFresh(
     const void* base_pointer,
-    std::shared_ptr<ngraph::runtime::Executable> user) {
+    const std::shared_ptr<ngraph::runtime::Executable>& user) {
   mutex_lock l(mu_);
   auto it = freshness_map_.find(base_pointer);
   if (it == freshness_map_.end()) {
@@ -69,9 +69,9 @@ void NGraphFreshnessTracker::RemoveTensor(const void* base_pointer) {
 }
 
 void NGraphFreshnessTracker::RemoveUser(
-    std::shared_ptr<ngraph::runtime::Executable> user) {
+    const std::shared_ptr<ngraph::runtime::Executable>& user) {
   mutex_lock l(mu_);
-  for (auto kv : freshness_map_) {
+  for (auto& kv : freshness_map_) {
     kv.second.erase(user);
   }
 }

ngraph_bridge/ngraph_freshness_tracker.h

@@ -95,12 +95,12 @@ class NGraphFreshnessTracker : public ResourceBase {
   // If freshness_map_ has the base_pointer, then inserts the user function into
   // its set of user functions
   void MarkFresh(const void* base_pointer,
-                 std::shared_ptr<ngraph::runtime::Executable> user);
+                 const std::shared_ptr<ngraph::runtime::Executable>& user);
 
   // Checks if the freshness_map_ has the user function for base_pointer, else
   // returns false
   bool IsFresh(const void* base_pointer,
-               std::shared_ptr<ngraph::runtime::Executable> user);
+               const std::shared_ptr<ngraph::runtime::Executable>& user);
 
   // Removes all the functions for the base_pointer in the freshness_map_, i.e.
   // sets the set<ng::Function> for base_pointer to empty
@@ -114,7 +114,7 @@ class NGraphFreshnessTracker : public ResourceBase {
   void RemoveTensor(const void* base_pointer);
 
   // Removes the user function from the freshness_map_
-  void RemoveUser(std::shared_ptr<ngraph::runtime::Executable> user);
+  void RemoveUser(const std::shared_ptr<ngraph::runtime::Executable>& user);
 
  private:
   // mutex protecting the freshness_map_

ngraph_bridge/version.cc

@@ -25,14 +25,14 @@
 
 #define NG_TF_MAJOR_VERSION 0
 #define NG_TF_MINOR_VERSION 18
-#define NG_TF_PATCH_VERSION 0
+#define NG_TF_PATCH_VERSION 1
 
 // The version suffix is used for pre-release version numbers
 // For example before v0.7.0 we may do a pre-release i.e., a release
 // candidate such as v0.7.0-rc0
 // The code in master will always have the last released version number
 // with a suffix of '-master'
-#define NG_TF_VERSION_SUFFIX ""
+#define NG_TF_VERSION_SUFFIX "rc0"
 
 #define VERSION_STR_HELPER(x) #x
 #define VERSION_STR(x) VERSION_STR_HELPER(x)

python/setup.in.py

@@ -59,7 +59,7 @@ def get_tag(self):
 
 setup( 
     name='ngraph_tensorflow_bridge',
-    version='0.18.0',
+    version='0.18.1rc0',
     description='Intel nGraph compiler and runtime for TensorFlow',
     long_description=long_description,
     long_description_content_type="text/markdown",