我们已经阅读了TensorFlow关于调度的论文。它可能会预先执行Graph并找到“正确的”设备来放置操作。
但是我们已经测试过使用tf.Session(config=tf.ConfigProto(log_device_placement=True))，并且未指定要运行的任何设备。我们发现所有操作都放置在第一个GPU中。
日志看起来像这样。
Adam/epsilon: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Adam/epsilon: /job:localhost/replica:0/task:0/gpu:0
Adam/beta2: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Adam/beta2: /job:localhost/replica:0/task:0/gpu:0
Adam/beta1: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Adam/beta1: /job:localhost/replica:0/task:0/gpu:0
Adam/learning_rate: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Adam/learning_rate: /job:localhost/replica:0/task:0/gpu:0
Variable_3/Adam_1: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Variable_3/Adam_1: /job:localhost/replica:0/task:0/gpu:0
Variable_3/Adam_1/read: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Variable_3/Adam_1/read: /job:localhost/replica:0/task:0/gpu:0
Variable_3/Adam_1/Assign: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Variable_3/Adam_1/Assign: /job:localhost/replica:0/task:0/gpu:0
Variable_3/Adam: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Variable_3/Adam: /job:localhost/replica:0/task:0/gpu:0
Variable_3/Adam/read: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Variable_3/Adam/read: /job:localhost/replica:0/task:0/gpu:0
Variable_3/Adam/Assign: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Variable_3/Adam/Assign: /job:localhost/replica:0/task:0/gpu:0
Variable_2/Adam_1: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Variable_2/Adam_1: /job:localhost/replica:0/task:0/gpu:0
Variable_2/Adam_1/read: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Variable_2/Adam_1/read: /job:localhost/replica:0/task:0/gpu:0
Variable_2/Adam_1/Assign: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Variable_2/Adam_1/Assign: /job:localhost/replica:0/task:0/gpu:0
Variable_2/Adam: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Variable_2/Adam: /job:localhost/replica:0/task:0/gpu:0
Variable_2/Adam/read: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Variable_2/Adam/read: /job:localhost/replica:0/task:0/gpu:0
Variable_2/Adam/Assign: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Variable_2/Adam/Assign: /job:localhost/replica:0/task:0/gpu:0
Variable_1/Adam_1: /job:localhost/replica:0/task:0/gpu:0
I tensorflow/core/common_runtime/simple_placer.cc:818] Variable_1/Adam_1: /job:localhost/replica:0/task:0/gpu:0
Variable也放置在GPU中。我保证调度程序现在还不够好，对于用户来说，最佳实践是我们应该指定使用CPU或GPU的操作，尤其是当我们有多个GPU时。那正确吗？ 最佳答案
从v0.9开始，TensorFlow将所有操作都放置在您拥有的第一个GPU上。因此，您所观察到的是100％预期的。现在，如果您的问题是``TensorFlow是否可以在没有我干预的情况下自动在4个GPU上分配我的图形？''，那么截至2016年8月的答案是否定的。
如果您试图利用本地计算机上所有可用GPU的功能，请查看此variation of the cifar10 tutorial。下一个级别是replicated training with distributed tensorflow，但这对于您尝试执行的操作可能会过大。
而且随着当今所有虚拟化的进行，将某个操作分配给哪个设备的问题可能很快就会变得无关紧要。