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()

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

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

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

不，我不认为“开放CUDA库”足以说明问题，因为图的不同节点可能在不同的设备上。

当使用tensorflow2时:

print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

对于tensorflow1，要找出使用了哪个设备，您可以像这样启用日志设备放置:

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

检查控制台中是否有这种类型的输出。

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))