Question

查看代码：

datetime.datetime.now(pytz.timezone(data["timezone"]))

当你运行它时，你会得到结果。

import tensorflow as tf

with tf.device('/gpu:0'):
    with tf.device('/cpu:0'):
        x = tf.constant(0,name='x')
        x = x * 2
    y = x + 2
    config = tf.ConfigProto(log_device_placement=True) 
    with tf.Session(config=config) as sess:
        sess.run(y)

这意味着mul: (Mul): /job:localhost/replica:0/task:0/cpu:0 2017-08-11 21:38:23.953846: I c:\tf_jenkins\home\workspace\release-win\m\windows -gpu\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] mul: (Mul)/job:l ocalhost/replica:0/task:0/cpu:0 add: (Add): /job:localhost/replica:0/task:0/gpu:0 2017-08-11 21:38:23.954846: I c:\tf_jenkins\home\workspace\release-win\m\windows -gpu\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] add: (Add)/job:l ocalhost/replica:0/task:0/gpu:0 add/y: (Const): /job:localhost/replica:0/task:0/gpu:0 2017-08-11 21:38:23.954846: I c:\tf_jenkins\home\workspace\release-win\m\windows -gpu\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] add/y: (Const)/j ob:localhost/replica:0/task:0/gpu:0 mul/y: (Const): /job:localhost/replica:0/task:0/cpu:0 2017-08-11 21:38:23.954846: I c:\tf_jenkins\home\workspace\release-win\m\windows -gpu\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] mul/y: (Const)/j ob:localhost/replica:0/task:0/cpu:0 x: (Const): /job:localhost/replica:0/task:0/cpu:0 2017-08-11 21:38:23.954846: I c:\tf_jenkins\home\workspace\release-win\m\windows -gpu\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] x: (Const)/job:l ocalhost/replica:0/task:0/cpu:0上运行的mul和cpu上运行add。所以我得出gpu。

的结论

当我查看Inception时，我很困惑。

where does ops or tensors define where does ops or tensors run

tower_loss在cpu上定义，这意味着根据我的结论，每个gpu都会在cpu上运行。但我认为每个gpu都应该运行一个副本在gpu上。所以我误解了吗？

tensorflow操作在哪里运行？ cpu还是gpu？

0 个答案: