对于TF2.4+， tensorflow网站上列出的“官方”方法来检查TF是否使用GPU

>>> import tensorflow as tf
>>> print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
Num GPUs Available:  2

这是我用来列出tf可用设备的行。会话直接从bash:

python -c "import os; os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'; import tensorflow as tf; sess = tf.Session(); [print(x) for x in sess.list_devices()]; print(tf.__version__);"

它将打印可用的设备和tensorflow版本，例如:

_DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 268435456, 10588614393916958794)
_DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_GPU:0, XLA_GPU, 17179869184, 12320120782636586575)
_DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 13378821206986992411)
_DeviceAttributes(/job:localhost/replica:0/task:0/device:GPU:0, GPU, 32039954023, 12481654498215526877)
1.14.0

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

你可以通过运行下面的代码来检查你是否正在使用GPU:

import tensorflow as tf
tf.test.gpu_device_name()

如果输出是“，这意味着你只使用CPU; 如果输出类似于/device:GPU:0，这意味着GPU工作。

并使用下面的代码来检查您使用的GPU:

from tensorflow.python.client import device_lib 
device_lib.list_local_devices()

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

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