Merge branch 'master' into r0.13

Author: avijit-nervana

diagnostics/model_test/verify_model.py

@@ -77,9 +77,11 @@ def calculate_output(param_dict, select_device, input_example):
 
     output_tensor_name = param_dict["output_tensor_name"]
 
-    config = tf.ConfigProto(inter_op_parallelism_threads=1)
+    config = tf.ConfigProto(
+        inter_op_parallelism_threads=1, allow_soft_placement=True)
+    config_ngraph_enabled = ngraph_bridge.update_config(config)
 
-    sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
+    sess = tf.Session(config=config_ngraph_enabled)
     set_os_env(select_device)
 
     # if checkpoint, then load checkpoint

examples/axpy.py

@@ -50,9 +50,10 @@
     allow_soft_placement=True,
     log_device_placement=False,
     inter_op_parallelism_threads=1)
+config_ngraph_enabled = ngraph_bridge.update_config(config)
 
 # Create session and run
-with tf.Session(config=config) as sess:
+with tf.Session(config=config_ngraph_enabled) as sess:
     print("Python: Running with Session")
     options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
     run_metadata = tf.RunMetadata()

examples/axpy_matmul_variable_updated.py

@@ -61,9 +61,10 @@
             do_constant_folding=False,
             do_function_inlining=False,
         )))
+config_ngraph_enabled = ngraph_bridge.update_config(config)
 
 # Create session and run
-with tf.Session(config=config) as sess:
+with tf.Session(config=config_ngraph_enabled) as sess:
     print("Python: Running with Session")
     options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
     run_metadata = tf.RunMetadata()

examples/axpy_variable_updated.py

@@ -61,9 +61,10 @@
             do_constant_folding=False,
             do_function_inlining=False,
         )))
+config_ngraph_enabled = ngraph_bridge.update_config(config)
 
 # Create session and run
-with tf.Session(config=config) as sess:
+with tf.Session(config=config_ngraph_enabled) as sess:
     print("Python: Running with Session")
     options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
     run_metadata = tf.RunMetadata()

examples/mnist/mnist_cnn_inference.py

@@ -130,6 +130,7 @@ def train_mnist_cnn(FLAGS):
         allow_soft_placement=True,
         log_device_placement=True,
         inter_op_parallelism_threads=4)
+    config_ngraph_enabled = ngraph_bridge.update_config(config)
 
     # Note: Additional configuration option to boost performance is to set the
     # following environment for the run:
@@ -164,7 +165,7 @@ def train_mnist_cnn(FLAGS):
     train_writer = tf.summary.FileWriter(graph_location)
     train_writer.add_graph(tf.get_default_graph())
     saver = tf.train.Saver()
-    with tf.Session(config=config) as sess:
+    with tf.Session(config=config_ngraph_enabled) as sess:
         saver.restore(sess, FLAGS.model_dir)
         #sess.run(tf.global_variables_initializer())
         test_accuracy_final = 0

examples/mnist/mnist_deep_simplified_distributed.py

@@ -148,6 +148,7 @@ def train_mnist_cnn(FLAGS):
         allow_soft_placement=True,
         log_device_placement=False,
         inter_op_parallelism_threads=1)
+    config_ngraph_enabled = ngraph_bridge.update_config(config)
 
     # Note: Additional configuration option to boost performance is to set the
     # following environment for the run:
@@ -207,7 +208,8 @@ def train_mnist_cnn(FLAGS):
         tf.train.StopAtStepHook(train_loops)
     ]
 
-    with tf.train.MonitoredTrainingSession(hooks=hooks, config=config) as sess:
+    with tf.train.MonitoredTrainingSession(
+            hooks=hooks, config=config_ngraph_enabled) as sess:
 
         step = 0
         start = time.time()

examples/mnist/mnist_fprop_only.py

@@ -68,8 +68,9 @@ def run_mnist(_):
         allow_soft_placement=True,
         log_device_placement=True,
         inter_op_parallelism_threads=1)
+    config_ngraph_enabled = ngraph_bridge.update_config(config)
 
-    sess = tf.Session(config=config)
+    sess = tf.Session(config=config_ngraph_enabled)
     tf.global_variables_initializer().run(session=sess)
     # Train
     train_loops = FLAGS.train_loop_count

examples/mnist/mnist_softmax_distributed.py

@@ -97,6 +97,7 @@ def run_mnist(_):
         allow_soft_placement=True,
         log_device_placement=True,
         inter_op_parallelism_threads=1)
+    config_ngraph_enabled = ngraph_bridge.update_config(config)
 
     #config.graph_options.optimizer_options.global_jit_level = jit_level
     run_metadata = tf.RunMetadata()
@@ -106,7 +107,7 @@ def run_mnist(_):
 
     # The MonitoredTrainingSession takes care of session initialization
     with tf.train.MonitoredTrainingSession(
-            hooks=hooks, config=config) as mon_sess:
+            hooks=hooks, config=config_ngraph_enabled) as mon_sess:
         start = time.time()
         train_writer = tf.summary.FileWriter(FLAGS.log_dir, mon_sess.graph)
         while not mon_sess.should_stop():