Getting tensorflow2.0 (1): GPU version environment to build Win10 + Cuda10 + cudnn7.6.3

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This link: https://blog.csdn.net/id_iot/article/details/102753243

Foreword

After the first half of this year TensorFlow2.0 release, currently growing gradually learning materials, is a good time to start.

The upgrade can be said that unprecedented efforts, the next period of time will tensorflow2.0 write a theme: tensorflow2.0 started.

The first chapter begins operating environment to build tensorflow

First, the necessary configuration

Anaconda3-2019.07-Windows-x86_64

Download: https: //www.anaconda.com/distribution
cuda_10.0.130_411.31_win10 (be careful not to under-date 10.1 does not support tf2.0)

Download: https: //developer.nvidia.com/cuda-10.0-download-archive target_os = Windows & target_arch = x86_64 & target_version = 10 & target_type = exelocal?
cudnn-10.0-windows10-x64-v7.6.3.30

Download: https: //developer.nvidia.com/rdp/cudnn-download
TensorFlow2.0

Computer basic situation:

Upon inquiry, the card can use the GPU version TensorFlow2.0, inquiries to:

https://developer.nvidia.com/cuda-gpus

Second, the installation configuration

1.anaconda installation

All the way to fool installation:

Add the anaconda installation path to the environment variable;

Add pycharm in anaconda for the project interpreter:

This will build a good environment to run a python.

2.cuda installation

Double-click fool install:

View the environment variables, CUDA and libnvvp of bin path is added to it:

3.cudnn installation

Decompression cudnn, obtain the following documents:

The three folders to copy, then paste it into C: \ Program Files \ NVIDIA GPU Computing Toolkit \ CUDA \ v10.0, depending on their own path to change.

And after

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\lib\x64

Added to the environment variable path.

4.tensorflow2.0 installation

By mounting the mirror faster speed

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorflow-gpu==2.0.0a0

Third, the test code

After installation can be tested using the following code:

import tensorflow as tf
import timeit

with tf.device('/cpu:0'):
   cpu_a = tf.random.normal([10000, 1000])
   cpu_b = tf.random.normal([1000, 2000])
   print(cpu_a.device, cpu_b.device)

with tf.device('/gpu:0'):
   gpu_a = tf.random.normal([10000, 1000])
   gpu_b = tf.random.normal([1000, 2000])
   print(gpu_a.device, gpu_b.device)

def cpu_run():
   with tf.device('/cpu:0'):
      c = tf.matmul(cpu_a, cpu_b)
   return c

def gpu_run():
   with tf.device('/gpu:0'):
      c = tf.matmul(gpu_a, gpu_b)
   return c

# warm up cpu_time = timeit.timeit(cpu_run, number=10)
gpu_time = timeit.timeit(gpu_run, number=10)
print('warmup:', cpu_time, gpu_time)

cpu_time = timeit.timeit(cpu_run, number=10)
gpu_time = timeit.timeit(gpu_run, number=10)
print('run time:', cpu_time, gpu_time)

If the test results are as follows:

Congratulations on your success! ! !

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