-1
- 视窗7 64
- 的Python 3.5.2
- CUDA工具包8.0.61
- Tensorflow包：tensorflow-GPU- 1.2.0rc0
- cudnn 8.0（用于CUDA 8.0工具包）
测试：Tensorflow-GPU，CUDA和cudnn安装，然而GPU设备被发现，但不利用
# Creates a graph.
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print(sess.run(c))
结果：
2017-05-30 13:50:33.021124: I C:\...\gpu_device.cc:906] Found device 0 with properties:
name: NVS 5200M
major: 2 minor: 1 memoryClockRate (GHz) 1.344
pciBusID 0000:01:00.0
Total memory: 1.00GiB
Free memory: 886.41MiB
2017-05-30 13:50:33.022124: I C:\...\gpu_device.cc:927] DMA: 0
2017-05-30 13:50:33.022124: I C:\...\gpu_device.cc:937] 0: Y
2017-05-30 13:50:33.022124: I C:\...\gpu_device.cc:969] Ignoring visible gpu device (device: 0, name: NVS 5200M, pci bus id: 0000:01:00.0) with Cuda compute capability 2.1. The minimum required Cuda capability is 3.0.
Device mapping: no known devices.
2017-05-30 13:50:33.024124: I C:\...\direct_session.cc:265] Device mappin
g:
MatMul: (MatMul): /job:localhost/replica:0/task:0/cpu:0
2017-05-30 13:50:33.026124: I C:\...\simple_placer.cc:847] MatMul: (MatMul)/job:localhost/replica:0/task:0/cpu:0
b: (Const): /job:localhost/replica:0/task:0/cpu:0
2017-05-30 13:50:33.027124: I C:\...\simple_placer.cc:847] b: (Const)/job:localhost/replica:0/task:0/cpu:0
a: (Const): /job:localhost/replica:0/task:0/cpu:0
2017-05-30 13:50:33.027124: I C:\...\simple_placer.cc:847] a: (Const)/job:localhost/replica:0/task:0/cpu:0
[[ 22. 28.]
[ 49. 64.]]
我想我的问题是“忽略与CUDA计算能力2.1可见GPU设备。 CUDA 2.1的最低要求是3.0。“因此，我的硬件似乎只限于CUDA 2.1，但尚不清楚3.0的要求是来自CUDA工具包还是tensorflow库？
TF最初发布时需要计算能力3.0（用于GPU加速）。我无法为您提供TF文档的确切链接。此外，CUDA GPU上的大部分TF DNN加速都是通过cudnn库实现的，该库专门将所需的GPU称为开普勒或更新版本（https://developer.nvidia.com/cudnn），以及cc2 .x器件是费米器件。费米GPU特别不被cudnn支持。 –