Series
Tutorial
version
version 1:
- windows 10 64 bit + GTX 1060(8G) + cuda driver
- windows 10 64 bit + GTX 1080(12G) + cuda driver
- CUDA 8.0 + cudnn 6.0.1(win10) + tensorflow-gpu 1.4.0
- python 3.5.3
version 2:
- windows 10 64 bit + GeForce Titan Xp(12G) + cuda driver for Titan xp
- CUDA 9.0 + cudnn 7.1.4(win10) + tensorflow-gpu 1.8.0 ( 1.8.0, 1.9.0 for cuda 9.0)
version 3:
- windows 10 64 bit + Quadro P4000(8G) + cuda driver for Quadro P4000(实测用Titan Xp的driver也可以)
- CUDA 9.0 + cudnn 7.1.4(win10) + tensorflow-gpu 1.8.0 ( 1.8.0, 1.9.0 for cuda 9.0)
errors
error retrieving driver version: Unimplemented: kernel reported driver version not implemented on Windows
Tips: for tensorflow-gpu==1.4.0
on linux, support python 2.7,3.3,3.4,3.5,3.6.
on windows, only support python 3.5,3.6.
Tips: for tensorflow-gpu==1.8.0
on linux, support python 2.7,3.3,3.4,3.5,3.6.
on windows, only support python 3.5,3.6.
from Tensorflow1.6
use CUDA9.0+cuDNN7
.
cuda & cudnn
see Part 1: Install and Configure Caffe on windows 10
system env
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin
python
install python 3.5.3
,add python and pip path to system env.
copy python.exe
to python3.exe
,
copy pip.exe
to pip3.exe
system env
C:\Users\zunli\AppData\Local\Programs\Python\Python35\
C:\Users\zunli\AppData\Local\Programs\Python\Python35\Scripts
test
python3
Python 3.5.3 (v3.5.3:1880cb95a742, Jan 16 2017, 16:02:32) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> ^Z
pip3
pip3 -V
pip 9.0.1 from c:\users\zunli\appdata\local\programs\python\python35\lib\site-packages (python 3.5)
tensorflow
pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple Pillow scipy sklearn scikit-image matplotlib
1.4.0
pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorflow-gpu==1.4.0 keras=2.1.0
1.8.0
pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorflow-gpu==1.8.0 keras=2.2.0
test tensorflow
import tensorflow as tf
import numpy as np
hello=tf.constant('hhh')
sess=tf.Session()
print (sess.run(hello))
test cuda and gpu
import tensorflow as tf
a = tf.test.is_built_with_cuda() # 判断CUDA是否可以用
b = tf.test.is_gpu_available(
cuda_only=False,
min_cuda_compute_capability=None
) # 判断GPU是否可以用
print(a)
print(b)
test gpu
import tensorflow as tf
with tf.device('/cpu:0'):
a = tf.constant([1.0, 2.0, 3.0], shape=[3], name='a')
b = tf.constant([1.0, 2.0, 3.0], shape=[3], name='b')
with tf.device('/gpu:0'):
c = a + b
# 注意:allow_soft_placement=True表明:计算设备可自行选择,如果没有这个参数,会报错。
# 因为不是所有的操作都可以被放在GPU上,如果强行将无法放在GPU上的操作指定到GPU上,将会报错。
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True))
# sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
sess.run(tf.global_variables_initializer())
print(sess.run(c))
pycharm
run code with pycharm
jupyter notebook
pip install ipykernel
python -m ipykernel install --user --name=tensorflow
Installed kernelspec tensorflow in C:\Users\zunli\AppData\Roaming\jupyter\kernels\tensorflow
error fix
errors:
No matching distribution found for tensorflow
solution: use python 3.5
instead of python 2.7
Reference
History
- 20180829: created.