Fixes for Multi-Gpu_basics No Longer hangs on CPU

examples/5_MultiGPU/multigpu_basics.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 '''
 Basic Multi GPU computation example using TensorFlow library.
 
@@ -12,7 +13,7 @@
 "/gpu:1": The second GPU of your machine
 '''
 
-from __future__ import print_function
+
 
 import numpy as np
 import tensorflow as tf
@@ -31,8 +32,8 @@
  * Multi GPU computation time: 0:00:07.131701
 '''
 # Create random large matrix
-A = np.random.rand(1e4, 1e4).astype('float32')
-B = np.random.rand(1e4, 1e4).astype('float32')
+A = np.random.rand(10000, 10000).astype('float32')
+B = np.random.rand(10000, 10000).astype('float32')
 
 # Create a graph to store results
 c1 = []
@@ -48,8 +49,8 @@ def matpow(M, n):
 Single GPU computing
 '''
 with tf.device('/gpu:0'):
-    a = tf.constant(A)
-    b = tf.constant(B)
+    a = tf.placeholder(tf.float32, [10000, 10000])
+    b = tf.placeholder(tf.float32, [10000, 10000])
     # Compute A^n and B^n and store results in c1
     c1.append(matpow(a, n))
     c1.append(matpow(b, n))
@@ -60,7 +61,7 @@ def matpow(M, n):
 t1_1 = datetime.datetime.now()
 with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:
     # Run the op.
-    sess.run(sum)
+    sess.run(sum, {a:A, b:B})
 t2_1 = datetime.datetime.now()
 
 
@@ -70,13 +71,13 @@ def matpow(M, n):
 # GPU:0 computes A^n
 with tf.device('/gpu:0'):
     # Compute A^n and store result in c2
-    a = tf.constant(A)
+    a = tf.placeholder(tf.float32, [10000, 10000])
     c2.append(matpow(a, n))
 
 # GPU:1 computes B^n
 with tf.device('/gpu:1'):
     # Compute B^n and store result in c2
-    b = tf.constant(B)
+    b = tf.placeholder(tf.float32, [10000, 10000])
     c2.append(matpow(b, n))
 
 with tf.device('/cpu:0'):
@@ -85,7 +86,7 @@ def matpow(M, n):
 t1_2 = datetime.datetime.now()
 with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:
     # Run the op.
-    sess.run(sum)
+    sess.run(sum, {a:A, b:B})
 t2_2 = datetime.datetime.now()