Keras Learning Environment Configuration-gpu accelerated version (Ubuntu 16.04 + CUDA8.0 + cuDNN6.0 + tensorflow)

This article is a personal summary of the Keras deep Learning framework configuration, the shortcomings please point out, thank you!

1. First, we need to install the Ubuntu operating system (under Windows) , which uses the Ubuntu16.04 version:

2. After installing the Ubuntu16.04, the system needs to be initialized and updated:

Open Terminal input:

System Upgrade:

→~ sudo apt-get update

→~ sudo apt-get upgrade

To install a base Dependent library:

→~ sudo apt-get install python-dev python-pip python-nose gcc g++ git gfortran libopenblas-dev liblapack-dev libatlas-base -dev

→~ sudo apt-get install setuptools wheel python-numpy python-scipy python-matplotlib

3. Installing the CUDA development environment

Download CUDA8.0:

Open the terminal and switch to the download directory:

→~ sudo dpkg-i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb

→~ sudo apt-get update

→~ sudo apt-get install Cuda

After the installation is complete, configure the Cuda path:

→~ sudo gedit/etc/profile

Enter add at the end of the profile file ( Note: If you are not using version 8.0, you need to modify the version number ):

→~ Export cuda_home=/usr/local/cuda-8.0

→~ Export Path=/usr/local/cuda-8.0/bin${path:+:${path}}

→~ Export Ld_library_path=/usr/local/cuda-8.0/lib64${ld_library_path:+:${ld_library_path}}

After modification:

→~ Source/etc/profile

Verify that the configuration is successful:

→~ nvcc-v

The following message appears to be successful:

4. Installing the CUDNN Acceleration Library

This article uses the CUDA8.0, the corresponding installation cudnn-8.0-linux-x64-v6.0.tgz. Download extracted is a folder named Cuda, which contains bin, include, Lib, three folders copied to the location of the installation Cuda to overwrite the corresponding folder:

→~ cd/home/..../cudnn/

→~ sudo cp cuda/include/cudnn.h/usr/local/cuda/include
→~ sudo cp cuda/lib64/libcudnn*/usr/local/cuda/lib64

→~ sudo chmod a+r/usr/local/cuda/include/cudnn.h/usr/local/cuda/lib64/libcudnn*

5. Installing the Keras frame

→~ sudo pip install scikit-learn scikit-image

→~ sudo pip install TENSORFLOW-GPU # GPU Accelerated version

→~ sudo pip install Keras

Verify that the installation was successful in the terminal:

→~ Import TensorFlow

→~ Import Keras

If you do not error, the configuration is successful!

Keras Learning Environment Configuration-gpu accelerated version (Ubuntu 16.04 + CUDA8.0 + cuDNN6.0 + tensorflow)