Python 如何确保 tensorflow 正在使用 GPU
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How to ensure tensorflow is using the GPU
提问by Mihir Deshpande
I installed CUDA v9.2 and corresponding cuDNN manually to install tensorflow gpu But I realized that tensorflow 1.8.0 requires CUDA 9.0 so I ran
我手动安装了 CUDA v9.2 和相应的 cuDNN 来安装 tensorflow gpu 但是我意识到 tensorflow 1.8.0 需要 CUDA 9.0 所以我跑了
pip install tensorflow-gpu
from the anaconda prompt (base environment) where it automatically installed CUDA 9.0 and corresponding cuDNN. I started Spyder from the same command prompt. So here is my code in Python 3.6 where I'm using keras and tensorflow to train using 8000 odd images -
从 anaconda 提示（基本环境）开始，它自动安装了 CUDA 9.0 和相应的 cuDNN。我从同一个命令提示符启动了 Spyder。所以这是我在 Python 3.6 中的代码，其中我使用 keras 和 tensorflow 来训练使用 8000 张奇数图像 -
# Convolutional Neural Networks
# Part 1 - Building the CNN
# Not important
# Part 2- Fitting the CNN to the images -
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
training_set = train_datagen.flow_from_directory(
'dataset/training_set',
target_size=(64, 64),
batch_size=32,
class_mode='binary')
test_set = test_datagen.flow_from_directory(
'dataset/test_set',
target_size=(64, 64),
batch_size=32,
class_mode='binary')
with tf.device("/gpu:0"): # Notice THIS
classifier.fit_generator(
training_set,
steps_per_epoch=8000,
epochs=25,
validation_data=test_set,
validation_steps=2000)
Notice that right before fitting the dataset at the end, I put it inside
请注意，在最后拟合数据集之前，我将其放入
with tf.device("/gpu:0"):
I think this should ensure that it uses the GPU for training? I'm not sure because changing " gpu:0 " to " cpu:0 " gives the exact same time (18-20 minutes per epoch) for training. How do I ensure that tensorflow in Spyder uses my GPU ?
我认为这应该确保它使用 GPU 进行训练？我不确定，因为将“ gpu:0 ”更改为“ cpu:0 ”可以提供完全相同的训练时间（每个时期 18-20 分钟）。如何确保 Spyder 中的 tensorflow 使用我的 GPU？
I have a NVIDIA GTX 970 so its CUDA compatible. Also I'm using python 3.6 , is that a problem ? Should I create a seperate Python 3.5 environment and install tensorflow-gpu in that similarly and try ?
我有一个 NVIDIA GTX 970，所以它兼容 CUDA。另外我使用的是 python 3.6，这是一个问题吗？我应该创建一个单独的 Python 3.5 环境并在其中安装 tensorflow-gpu 并尝试吗？
回答by dimension
Creates a graph.
创建图表。
with tf.device('/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)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
r = sess.run(c)
print(r)
import numpy as np
assert np.all(r == np.array([[22., 28.], [49., 64.]]))
or go tensorflow website (https://www.tensorflow.org/programmers_guide/using_gpu)
或者去 tensorflow 网站（https://www.tensorflow.org/programmers_guide/using_gpu）
import tensorflow as tf
if tf.test.gpu_device_name():
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
else:
print("Please install GPU version of TF")
or this :
或这个 ：
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
