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GPU

查看GPU:nvidia-smi

nvidia-smi

The NVIDIA System Management Interface (nvidia-smi) is a command line utility, based on top of the NVIDIA Management Library (NVML), intended to aid in the management and monitoring of NVIDIA GPU devices.

训练是查看GPU信息

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.48                 Driver Version: 390.48                    |
|-------------------------------+----------------------+----------------------+
| 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 108...  Off  | 00000000:04:00.0 Off |                  N/A |
| 45%   77C    P2   102W / 250W |  10803MiB / 11178MiB |     97%      Default |
+-------------------------------+----------------------+----------------------+
|   1  GeForce GTX 108...  Off  | 00000000:05:00.0 Off |                  N/A |
| 23%   32C    P8     9W / 250W |  10631MiB / 11178MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   2  GeForce GTX 108...  Off  | 00000000:08:00.0 Off |                  N/A |
| 23%   33C    P8     8W / 250W |  10631MiB / 11178MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   3  GeForce GTX 108...  Off  | 00000000:09:00.0 Off |                  N/A |
| 23%   32C    P8     8W / 250W |  10631MiB / 11178MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   4  GeForce GTX 108...  Off  | 00000000:84:00.0 Off |                  N/A |
| 23%   32C    P8     8W / 250W |  10631MiB / 11178MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   5  GeForce GTX 108...  Off  | 00000000:85:00.0 Off |                  N/A |
| 23%   27C    P8     9W / 250W |  10631MiB / 11178MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   6  GeForce GTX 108...  Off  | 00000000:88:00.0 Off |                  N/A |
| 23%   27C    P8     8W / 250W |  10631MiB / 11178MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   7  GeForce GTX 108...  Off  | 00000000:89:00.0 Off |                  N/A |
| 23%   30C    P8     8W / 250W |  10631MiB / 11178MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0     45479      C   python                                     10791MiB |
|    1     45479      C   python                                     10619MiB |
|    2     45479      C   python                                     10619MiB |
|    3     45479      C   python                                     10619MiB |
|    4     45479      C   python                                     10619MiB |
|    5     45479      C   python                                     10619MiB |
|    6     45479      C   python                                     10619MiB |
|    7     45479      C   python                                     10619MiB |
+-----------------------------------------------------------------------------+

没有程序在运行时会显示下列信息：

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

发现问题，没想到是正常的

top命令发现CPU使用率过高，而GPU使用率很低。

  PID USER      PR  NI    VIRT    RES    SHR S  %CPU %MEM     TIME+ COMMAND 
63382 dlearni+  20   0  196.1g   6.5g   1.8g S 704.3  4.1  12:41.44 python

这是因为默认只是使用一个默认GPU啊！需要自己手动指定多GPU才行，使用nvidia smi -l（表示loop）可以看到动态信息不断更新。

Tensorflow by default uses memory power of all GPUs as it allocates maximum memory for your job but not processing speed. To utilize the processing power of all GPUs as well you need to specify tf.device statements where ever you want to do parallel processing in your code. In Tensorflow, you have to manually assign devices on your own and also calculate the overall gradients by collecting output from all devices on your own. But MXNET does this thing automatically and you just need to specify CONTEXT statement indicating list of GPUs available. You dont have to calculate the average loss of your model by yourself.

启动信息

当显示出显卡信息，则表示安装成功了。

其中major主版本，minor副版本：表示CUDA Capability version number 所表示的是你的 CUDA 設備所能支持的計算架構 。

而peer to peer matrix理解为：让同一系统中的多个 GPU 之间能够实现点对点 (P2P) 通信，避免了额外的 CPU 处理时间。

>>> import tensorflow as tf
>>> sess = tf.Session()
2018-06-26 14:47:50.580853: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-06-26 14:47:56.561769: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties: 
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:04:00.0
totalMemory: 10.92GiB freeMemory: 10.76GiB
2018-06-26 14:47:56.759054: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 1 with properties: 
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:05:00.0
totalMemory: 10.92GiB freeMemory: 10.76GiB
2018-06-26 14:47:56.948706: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 2 with properties: 
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:08:00.0
totalMemory: 10.92GiB freeMemory: 10.76GiB
2018-06-26 14:47:57.153998: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 3 with properties: 
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:09:00.0
totalMemory: 10.92GiB freeMemory: 10.76GiB
2018-06-26 14:47:57.373397: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 4 with properties: 
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:84:00.0
totalMemory: 10.92GiB freeMemory: 10.76GiB
2018-06-26 14:47:57.587687: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 5 with properties: 
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:85:00.0
totalMemory: 10.92GiB freeMemory: 10.76GiB
2018-06-26 14:47:57.807224: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 6 with properties: 
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:88:00.0
totalMemory: 10.92GiB freeMemory: 10.76GiB
2018-06-26 14:47:58.052574: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 7 with properties: 
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582
pciBusID: 0000:89:00.0
totalMemory: 10.92GiB freeMemory: 10.76GiB
2018-06-26 14:47:58.068368: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Device peer to peer matrix
2018-06-26 14:47:58.068947: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1051] DMA: 0 1 2 3 4 5 6 7 
2018-06-26 14:47:58.068975: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1061] 0:   Y Y Y Y N N N N 
2018-06-26 14:47:58.068990: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1061] 1:   Y Y Y Y N N N N 
2018-06-26 14:47:58.069005: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1061] 2:   Y Y Y Y N N N N 
2018-06-26 14:47:58.069019: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1061] 3:   Y Y Y Y N N N N 
2018-06-26 14:47:58.069035: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1061] 4:   N N N N Y Y Y Y 
2018-06-26 14:47:58.069049: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1061] 5:   N N N N Y Y Y Y 
2018-06-26 14:47:58.069087: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1061] 6:   N N N N Y Y Y Y 
2018-06-26 14:47:58.069110: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1061] 7:   N N N N Y Y Y Y 
2018-06-26 14:47:58.069173: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:04:00.0, compute capability: 6.1)
2018-06-26 14:47:58.069196: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:1) -> (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:05:00.0, compute capability: 6.1)
2018-06-26 14:47:58.069219: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:2) -> (device: 2, name: GeForce GTX 1080 Ti, pci bus id: 0000:08:00.0, compute capability: 6.1)
2018-06-26 14:47:58.069242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:3) -> (device: 3, name: GeForce GTX 1080 Ti, pci bus id: 0000:09:00.0, compute capability: 6.1)
2018-06-26 14:47:58.069261: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:4) -> (device: 4, name: GeForce GTX 1080 Ti, pci bus id: 0000:84:00.0, compute capability: 6.1)
2018-06-26 14:47:58.069280: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:5) -> (device: 5, name: GeForce GTX 1080 Ti, pci bus id: 0000:85:00.0, compute capability: 6.1)
2018-06-26 14:47:58.069298: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:6) -> (device: 6, name: GeForce GTX 1080 Ti, pci bus id: 0000:88:00.0, compute capability: 6.1)
2018-06-26 14:47:58.069316: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:7) -> (device: 7, name: GeForce GTX 1080 Ti, pci bus id: 0000:89:00.0, compute capability: 6.1)

GPU放置

全局配置

####终端执行程序时设置使用的GPU 如果电脑有多个GPU，tensorflow默认全部使用。如果想只使用部分GPU，可以设置CUDA_VISIBLE_DEVICES。在调用python程序时，可以使用。

CUDA_VISIBLE_DEVICES=1
python my_script.py

####python代码中设置使用的GPU 如果要在python代码中设置使用的GPU（如使用pycharm进行调试时），可以使用下面的代码（见第二个参考网址中Yaroslav Bulatov的回复）：

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "2"

Simple placement普通放置原则（tf.device）

默认放在GPU #0上或者没有GPU时，放在CPU。

• If a node was already placed on a device in a previous run of the graph, it is left
on that device.
• Else, if the user pinned a node to a device (described next), the placer places it on
that device.
• Else, it defaults to GPU #0, or the CPU if there is no GPU.

只要有某个node被eval执行，会将所有node都进行放置

Whenever you run a graph, if TensorFlow needs to evaluate a node that is not placed on a device yet, it uses the simple placer to place it, along with all other nodes that are not placed yet.

import tensorflow as tf
with tf.device("/cpu:0"):
    a = tf.Variable(3.0, name='a1')
    b = tf.constant(4.0, name='b1')

c = a * b
config = tf.ConfigProto()
config.log_device_placement = True
sess = tf.Session(config=config)
a.initializer.run(session=sess) #会显示所有的放置位置，见下方信息
sess.run(c) #此时不会有log信息显示，因为所有的节点已经被放置好了。
Variable: (VariableV2): /job:localhost/replica:0/task:0/device:CPU:0
2018-06-26 18:29:29.119474: I tensorflow/core/common_runtime/placer.cc:874] Variable: (VariableV2)/job:localhost/replica:0/task:0/device:CPU:0
Variable/read: (Identity): /job:localhost/replica:0/task:0/device:CPU:0
2018-06-26 18:29:29.119540: I tensorflow/core/common_runtime/placer.cc:874] Variable/read: (Identity)/job:localhost/replica:0/task:0/device:CPU:0
mul: (Mul): /job:localhost/replica:0/task:0/device:GPU:0
2018-06-26 18:29:29.119597: I tensorflow/core/common_runtime/placer.cc:874] mul: (Mul)/job:localhost/replica:0/task:0/device:GPU:0
Variable/Assign: (Assign): /job:localhost/replica:0/task:0/device:CPU:0
2018-06-26 18:29:29.119658: I tensorflow/core/common_runtime/placer.cc:874] Variable/Assign: (Assign)/job:localhost/replica:0/task:0/device:CPU:0
Const: (Const): /job:localhost/replica:0/task:0/device:CPU:0
2018-06-26 18:29:29.119718: I tensorflow/core/common_runtime/placer.cc:874] Const: (Const)/job:localhost/replica:0/task:0/device:CPU:0
Variable/initial_value: (Const): /job:localhost/replica:0/task:0/device:CPU:0
2018-06-26 18:29:29.119754: I tensorflow/core/common_runtime/placer.cc:874] Variable/initial_value: (Const)/job:localhost/replica:0/task:0/device:CPU:0

Kernel：implementation of op for the device

For a TensorFlow operation to run on a device, it needs to have an implementation for that device; this is called a kernel. Many operations have kernels for both CPUs and GPUs, but not all of them. For example, TensorFlow does not have a GPU kernel for integer variables.

Parallelized execution of a TensorFlow graph

  • 先从source nodes开始（即zero dependency），放到inter_op thread pools
  • 如果op有多线程内核，就会放到intra_op thread pools
  • 然后依赖它们的操作也变成zero dependency，接着进行执行

![](picture/Parallelized execution of a TensorFlow graph.png)

Control Dependencies

a = tf.constant(1.0)
b = a + 2.0
with tf.control_dependencies([a, b]):
  x = tf.constant(3.0)
  y = tf.constant(4.0)
z = x + y

##Kill进程

找到进程号PID，kill的信号signal是9，-s 9

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 390.25                 Driver Version: 390.25                    |
|-------------------------------+----------------------+----------------------+
| 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 108...  Off  | 00000000:04:00.0 Off |                  N/A |
| 29%   34C    P0    56W / 250W |      0MiB / 11178MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   1  GeForce GTX 108...  Off  | 00000000:05:00.0 Off |                  N/A |
| 62%   79C    P2   201W / 250W |  10775MiB / 11178MiB |    100%      Default |
+-------------------------------+----------------------+----------------------+
|   2  GeForce GTX 108...  Off  | 00000000:08:00.0 Off |                  N/A |
| 29%   39C    P0    58W / 250W |      0MiB / 11178MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   3  GeForce GTX 108...  Off  | 00000000:09:00.0 Off |                  N/A |
| 29%   34C    P0    56W / 250W |      0MiB / 11178MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   4  GeForce GTX 108...  Off  | 00000000:84:00.0 Off |                  N/A |
| 29%   35C    P0    57W / 250W |      0MiB / 11178MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   5  GeForce GTX 108...  Off  | 00000000:85:00.0 Off |                  N/A |
| 29%   33C    P0    57W / 250W |      0MiB / 11178MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   6  GeForce GTX 108...  Off  | 00000000:88:00.0 Off |                  N/A |
| 29%   24C    P8     8W / 250W |    253MiB / 11178MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   7  GeForce GTX 108...  Off  | 00000000:89:00.0 Off |                  N/A |
| 29%   32C    P0    57W / 250W |      0MiB / 11178MiB |      2%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    1      9704      C   python                                     10761MiB |
|    6     20187      C   /home/test/anaconda2/bin/python              243MiB |
+-----------------------------------------------------------------------------+

kill -s 9 20187
[1]+  Killed                  /home/test/anaconda2/bin/python new_age_test.py --images "./data/test2" --model_path "./models2" --batch_size 128 --choose_best --cuda