Question

当我运行此命令时：

sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

我得到这个日志：

2017-06-16 11:29:42.305931: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-06-16 11:29:42.305950: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-06-16 11:29:42.305963: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-06-16 11:29:42.305975: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-06-16 11:29:42.305986: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-06-16 11:29:42.406689: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-06-16 11:29:42.406961: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties: 
name: GeForce GTX 1070
major: 6 minor: 1 memoryClockRate (GHz) 1.7715
pciBusID 0000:01:00.0
Total memory: 7.92GiB
Free memory: 248.75MiB
2017-06-16 11:29:42.406991: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 
2017-06-16 11:29:42.407010: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0:   Y 
2017-06-16 11:29:42.407021: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0)
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0
2017-06-16 11:29:42.408087: I tensorflow/core/common_runtime/direct_session.cc:257] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0

这是否向我保证tensorflow代码将使用GPU？我有一个先前版本的tensorflow，消息很明显，它使用了GPU。现在我升级后，消息不同而且令人困惑。我可以看到它发现了我的GPU，但它是肯定使用它还是仍在使用CPU？如何从代码中检查这一点以确保使用的设备是GPU？

我很担心，因为我有：

import keras
Using TensorFlow backend

它表明keras正在使用CPU版本！

Answer 1

使用设备范围如下：

with tf.device('/gpu:0'):
    a = tf.constant(0)
sess = tf.Session()
sess.run(a)

如果它没有抱怨它无法将设备分配给节点，那么您正在使用GPU。

您可以更进一步分析通过log_device_placement分配每个节点的位置。

当我运行tensorflow-gpu时发出警告。它是使用GPU吗？

1 个答案: