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Tensorflow:GPU加速只发生在第一次运行之后

nvidia gpu cuda tensorflow python

Felipe · 技术社区 · 7 年前

tensorflow-gpu .

使用的版本:

这是 nvidia-smi ,显示显卡配置。

| NVIDIA-SMI 410.78       Driver Version: 410.78       CUDA Version: 10.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 960M    On   | 00000000:02:00.0 Off |                  N/A |
| N/A   44C    P8    N/A /  N/A |    675MiB /  4046MiB |      0%   E. Process |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      1502      G   /usr/lib/xorg/Xorg                           363MiB |
|    0      3281      G   compiz                                        96MiB |
|    0      4375      G   ...uest-channel-token=14359313252217012722    69MiB |
|    0      5157      C   ...felipe/proj/venv/bin/python3.6            141MiB |
+-----------------------------------------------------------------------------+

device_lib.list_local_devices()

[name: "/device:CPU:0"
  device_type: "CPU"
  memory_limit: 268435456
  locality {
  }
  incarnation: 5096693727819965430, 
name: "/device:XLA_GPU:0"
  device_type: "XLA_GPU"
  memory_limit: 17179869184
  locality {
  }
  incarnation: 13415556283266501672
  physical_device_desc: "device: XLA_GPU device", 
name: "/device:XLA_CPU:0"
  device_type: "XLA_CPU"
  memory_limit: 17179869184
  locality {
  }
  incarnation: 14339781620792127180
  physical_device_desc: "device: XLA_CPU device", 
name: "/device:GPU:0"
  device_type: "GPU"
  memory_limit: 3464953856
  locality {
    bus_id: 1
    links {
    }
  }
  incarnation: 13743207545082600644
  physical_device_desc: "device: 0, name: GeForce GTX 960M, pci bus id: 0000:02:00.0, compute capability: 5.0"
]

运行一些虚拟矩阵乘法

shapes = [(50, 50), (100, 100), (500, 500), (1000, 1000), (10000,10000), (15000,15000)]

devices = ['/device:CPU:0', '/device:XLA_GPU:0']

for device in devices:
    for shape in shapes:
        with tf.device(device):
            random_matrix = tf.random_uniform(shape=shape, minval=0, maxval=1)
            dot_operation = tf.matmul(random_matrix, tf.transpose(random_matrix))
            sum_operation = tf.reduce_sum(dot_operation)

        # Time the actual runtime of the operations
        start_time = datetime.now()
        with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as session:
            result = session.run(sum_operation)
        elapsed_time = datetime.now() - start_time

        # PRINT ELAPSED TIME, SHAPE AND DEVICE USED

这是惊喜 . 第一次运行包含这段代码的单元格(我在jupyter笔记本上)时 GPU的计算时间比CPU长得多 :

# output of first run: CPU is faster
----------------------------------------
Input shape: (50, 50) using Device: /device:CPU:0 took: 0.01
Input shape: (100, 100) using Device: /device:CPU:0 took: 0.01
Input shape: (500, 500) using Device: /device:CPU:0 took: 0.01
Input shape: (1000, 1000) using Device: /device:CPU:0 took: 0.02
Input shape: (10000, 10000) using Device: /device:CPU:0 took: 6.22
Input shape: (15000, 15000) using Device: /device:CPU:0 took: 21.23
----------------------------------------
Input shape: (50, 50) using Device: /device:XLA_GPU:0 took: 2.82
Input shape: (100, 100) using Device: /device:XLA_GPU:0 took: 0.17
Input shape: (500, 500) using Device: /device:XLA_GPU:0 took: 0.18
Input shape: (1000, 1000) using Device: /device:XLA_GPU:0 took: 0.20
Input shape: (10000, 10000) using Device: /device:XLA_GPU:0 took: 28.36
Input shape: (15000, 15000) using Device: /device:XLA_GPU:0 took: 93.73
----------------------------------------

# output of reruns: GPU is faster
----------------------------------------
Input shape: (50, 50) using Device: /device:CPU:0 took: 0.02
Input shape: (100, 100) using Device: /device:CPU:0 took: 0.02
Input shape: (500, 500) using Device: /device:CPU:0 took: 0.02
Input shape: (1000, 1000) using Device: /device:CPU:0 took: 0.04
Input shape: (10000, 10000) using Device: /device:CPU:0 took: 6.78
Input shape: (15000, 15000) using Device: /device:CPU:0 took: 24.65
----------------------------------------
Input shape: (50, 50) using Device: /device:XLA_GPU:0 took: 0.14
Input shape: (100, 100) using Device: /device:XLA_GPU:0 took: 0.12
Input shape: (500, 500) using Device: /device:XLA_GPU:0 took: 0.13
Input shape: (1000, 1000) using Device: /device:XLA_GPU:0 took: 0.14
Input shape: (10000, 10000) using Device: /device:XLA_GPU:0 took: 1.64
Input shape: (15000, 15000) using Device: /device:XLA_GPU:0 took: 5.29
----------------------------------------

为什么只有在我运行一次代码之后GPU才会加速呢?

我可以看到GPU的设置是正确的(否则不会发生任何加速)。这是由于某种初始开销造成的吗?GPU需要吗热身

附笔。: 在两次运行中(即GPU速度较慢的一次运行和GPU速度较快的下一次运行),我都可以看到GPU的使用率是100%,所以它肯定被使用了。

附笔。: 捡起 . 如果我运行两次、三次或多次,第一次运行之后的所有运行都是成功的(即GPU计算速度更快)。

0 回复 | 直到 7 年前

Felipe 7 年前

robert-crovella's comment

原来GPU以两种方式映射到Tensorflow设备:XLA设备和普通GPU。

这就是为什么有两个装置,一个叫做 "/device:XLA_GPU:0" 另一个呢 "/device:GPU:0" .

我需要做的就是 是为了激活相反。现在GPU立即被Tensorflow接收。