如何判断tensorflow是否在python shell中使用gpu加速?

我已经在我的ubuntu 16.04中安装了tensorflow，使用的是ubuntu内置的apt cuda安装。

现在我的问题是，我如何测试tensorflow是否真的使用gpu?我有一个gtx 960m gpu。当我导入tensorflow时，这是输出

I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally

这个输出是否足够检查tensorflow是否使用gpu ?

当前回答

这将证实张量流使用GPU同时训练也?

Code

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

输出

I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: 
name: GeForce GT 730
major: 3 minor: 5 memoryClockRate (GHz) 0.9015
pciBusID 0000:01:00.0
Total memory: 1.98GiB
Free memory: 1.72GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GT 730, pci bus id: 0000:01:00.0)
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GT 730, pci bus id: 0000:01:00.0
I tensorflow/core/common_runtime/direct_session.cc:255] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GT 730, pci bus id: 0000:01:00.0

2016-12-06 07:29:09

其他回答

我发现从命令行查询gpu是最简单的:

nvidia-smi

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 384.98                 Driver Version: 384.98                    |
|-------------------------------+----------------------+----------------------+
| 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 980 Ti  Off  | 00000000:02:00.0  On |                  N/A |
| 22%   33C    P8    13W / 250W |   5817MiB /  6075MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      1060      G   /usr/lib/xorg/Xorg                            53MiB |
|    0     25177      C   python                                      5751MiB |
+-----------------------------------------------------------------------------+

如果你的学习是一个后台过程，pid从 Jobs -p应该匹配nvidia-smi的pid

2017-12-26 17:07:20

也许可以试试这个:

print(tf.reduce_sum(tf.random. sum);正常((1000、1000))))

看系统是否返回张量

根据网站

2022-01-06 12:53:41

在新版本的TF(>2.1)中，检查TF是否使用GPU的建议方法是:

tf.config.list_physical_devices('GPU')

2021-02-20 03:16:56

我发现下面的代码片段非常方便测试gpu ..

Tensorflow 2.0测试

import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
with tf.device('/gpu:0'):
    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)

with tf.Session() as sess:
    print (sess.run(c))

张量流测试

import tensorflow as tf
with tf.device('/gpu:0'):
    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)

with tf.Session() as sess:
    print (sess.run(c))

2019-11-02 18:26:06

这应该会给出Tensorflow可用的设备列表(Py-3.6下):

tf = tf.Session(config=tf.ConfigProto(log_device_placement=True))
tf.list_devices()
# _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 268435456)

2017-12-19 22:11:25

如何判断tensorflow是否在python shell中使用gpu加速?

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