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TensorFlow似乎没有使用GPU

pythongpupython-3.5tensorflow
6
我在Windows 8和Python 3.5上使用TensorFlow。我改变了this简短的例子，以查看是否支持GPU（Titan X）。不幸的是，在使用GPU（tf.device("/gpu:0"）和不使用GPU（tf.device("/cpu:0"）的情况下运行时间相同。Windows CPU监视器显示，在计算期间两种情况下CPU负载都约为100％。

这是代码示例：

import numpy as np
import tensorflow as tf
import datetime

#num of multiplications to perform
n = 100

# Create random large matrix
matrix_size = 1e3
A = np.random.rand(matrix_size, matrix_size).astype('float32')
B = np.random.rand(matrix_size, matrix_size).astype('float32')

# Creates a graph to store results
c1 = []

# Define matrix power
def matpow(M, n):
    if n < 1: #Abstract cases where n < 1
        return M
    else:
        return tf.matmul(M, matpow(M, n-1))

with tf.device("/gpu:0"):
    a = tf.constant(A)
    b = tf.constant(B)
    #compute A^n and B^n and store results in c1
    c1.append(matpow(a, n))
    c1.append(matpow(b, n))

    sum = tf.add_n(c1)

t1 = datetime.datetime.now()
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
    # Runs the op.
    sess.run(sum)
t2 = datetime.datetime.now()

print("computation time: " + str(t2-t1))

这是GPU机箱的输出：

I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cublas64_80.dll locally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cudnn64_5.dll locally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cufft64_80.dll locally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library nvcuda.dll locally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library curand64_80.dll locally
C:/Users/schlichting/.spyder-py3/temp.py:16: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  A = np.random.rand(matrix_size, matrix_size).astype('float32')
C:/Users/schlichting/.spyder-py3/temp.py:17: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  B = np.random.rand(matrix_size, matrix_size).astype('float32')
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:885] Found device 0 with properties: 
name: GeForce GTX TITAN X
major: 5 minor: 2 memoryClockRate (GHz) 1.076
pciBusID 0000:01:00.0
Total memory: 12.00GiB
Free memory: 2.40GiB
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:906] DMA: 0 
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:916] 0:   Y 
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:01:00.0)
D c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\direct_session.cc:255] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:01:00.0

Ievice mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:01:00.0

C:0/task:0/gpu:0
host/replica:0/task:0/gpu:0
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:827] MatMul_108: (MatMul)/job:localhost/replica:0/task:0/gpu:0
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:827] MatMul_109: (MatMul)/job:localhost/replica:0/task:0/gpu:0
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:827] MatMul_110: (MatMul)/job:localhost/replicacalhost/replica:0/task:0/gpu:0
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:827] MatMul_107: (MatMul)/job:localgpu:0
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:827] MatMul_103: (MatMul)/job:localhost/replica:0/task:0/gpu:0
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:827] MatMul_104: (MatMul)/job:localhost/replica:0/task:0/gpu:0
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:827] MatMul_105: (MatMul)/job:localhost/replica:0/task:0/gpu:0
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:827] MatMul_106: (MatMul)/job:lo c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:827] Const_1: (Const)/job:localhost/replica:0/task:0/gpu:0
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:827] MatMul_100: (MatMul)/job:localhost/replica:0/task:0/gpu:0
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:827] MatMul_101: (MatMul)/job:localhost/replica:0/task:0/gpu:0
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\simple_placer.cc:827] MatMul_102: (MatMul)/job:localhost/replica:0/task:0/Ionst_1: (Const): /job:localhost/replica:0/task:0/gpu:0


MatMul_100: (MatMul): /job:localhost/replica:0/task:0/gpu:0
MatMul_101: (MatMul): /job:localhost/replica:0/task:0/gpu:0
...
MatMul_198: (MatMul): /job:localhost/replica:0/task:0/gpu:0
MatMul_199: (MatMul): /job:localhost/replica:0/task:0/gpu:0
Const: (Const): /job:localhost/replica:0/task:0/gpu:0
MatMul: (MatMul): /job:localhost/replica:0/task:0/gpu:0
MatMul_1: (MatMul): /job:localhost/replica:0/task:0/gpu:0
MatMul_2: (MatMul): /job:localhost/replica:0/task:0/gpu:0
MatMul_3: (MatMul): /job:localhost/replica:0/task:0/gpu:0
...
MatMul_98: (MatMul): /job:localhost/replica:0/task:0/gpu:0
MatMul_99: (MatMul): /job:localhost/replica:0/task:0/gpu:0
AddN: (AddN): /job:localhost/replica:0/task:0/gpu:0
computation time: 0:00:05.066000

如果使用CPU，输出结果相同，只是将gpu:0替换为cpu:0。计算时间不会改变。即使我使用更多的操作，例如运行时间约为1分钟，GPU和CPU也是相等的。非常感谢！
2个回答
2
根据日志信息，特别是设备位置，您的代码正在使用GPU。运行时间相同。我的猜测是：
c1.append(matpow(a, n))
c1.append(matpow(b, n))

你的代码瓶颈在于不断将大型矩阵从GPU内存移动到RAM内存。你可以尝试以下方法来解决：

  • change the matrix size to 1e4 x 1e4

  • with tf.device("/gpu:0"):
  A = tf.random_normal([matrix_size, matrix_size])
  B = tf.random_normal([matrix_size, matrix_size])
  C = tf.matmul(A, B)
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
  t1 = datetime.datetime.now()
  sess.run(C)
  t2 = datetime.datetime.now()
谢谢！现在它可以工作了，GPU 大约比 CPU 快 20 倍。 - user3641158
似乎在创建矩阵时，需要使用TensorFlow方法tf.random_normal()，而不是使用np.random.rand()定义numpy矩阵。 - user3641158
2
比如说创建tensorflow会话需要4.9秒，实际的计算只需要0.1秒的CPU时间，这就给你带来了5.0秒的CPU时间。现在假设在GPU上创建会话同样需要4.9秒，但计算只需要0.01秒，时间为4.91秒，你几乎看不到差别。创建会话只是程序启动时的一次开销，不应计入计时中。此外，当你第一次调用sess.run时，tensorflow会进行一些编译/优化操作，导致第一次运行更慢。请试着按照以下方式计时。
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
    # Runs the op the first time.
    sess.run(sum)
    t1 = datetime.datetime.now()
    for i in range(1000):
        sess.run(sum)
    t2 = datetime.datetime.now()

如果这样做不能解决问题，那么可能是你的计算没有足够的并行性来让GPU真正超过CPU。增加矩阵大小可能会显示出差异。
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