在Jupyter或你的IDE中运行这个命令，检查Tensorflow是否使用GPU:

Tensorflow 2.1

一个简单的计算，可以用nvidia-smi验证GPU上的内存使用情况。

import tensorflow as tf 

c1 = []
n = 10

def matpow(M, n):
    if n < 1: #Abstract cases where n < 1
        return M
    else:
        return tf.matmul(M, matpow(M, n-1))

with tf.device('/gpu:0'):
    a = tf.Variable(tf.random.uniform(shape=(10000, 10000)), name="a")
    b = tf.Variable(tf.random.uniform(shape=(10000, 10000)), name="b")
    c1.append(matpow(a, n))
    c1.append(matpow(b, n))

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))

我找到了最简单、最全面的方法。只要设置tf.debug .set_log_device_placement(True)，你就可以看到ops是否真的在GPU上运行，例如在device /job:localhost/replica:0/task:0/device:GPU:0中执行op _EagerConst

更多文档:https://www.tensorflow.org/guide/gpu#logging_device_placement

>>> import tensorflow as tf 
>>> tf.config.list_physical_devices('GPU')

2020-05-10 14:58:16.243814: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-05-10 14:58:16.262675: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-10 14:58:16.263119: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1060 6GB computeCapability: 6.1
coreClock: 1.7715GHz coreCount: 10 deviceMemorySize: 5.93GiB deviceMemoryBandwidth: 178.99GiB/s
2020-05-10 14:58:16.263143: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-05-10 14:58:16.263188: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-05-10 14:58:16.264289: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-05-10 14:58:16.264495: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-05-10 14:58:16.265644: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-05-10 14:58:16.266329: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-05-10 14:58:16.266357: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-05-10 14:58:16.266478: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-10 14:58:16.266823: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-10 14:58:16.267107: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

正如@AmitaiIrron所建议的:

这个部分表示找到了一个gpu

2020-05-10 14:58:16.263119: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:

pciBusID: 0000:01:00.0 name: GeForce GTX 1060 6GB computeCapability: 6.1
coreClock: 1.7715GHz coreCount: 10 deviceMemorySize: 5.93GiB deviceMemoryBandwidth: 178.99GiB/s

这里它被添加为一个可用的物理设备

2020-05-10 14:58:16.267107: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0

[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

与tensorflow 2.0 >=

import tensorflow as tf
sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True))