TL; DR
In order to deploy network to train Deep Learning Network, a GPU Enabled machine is required. Fortunately, AWS provides GPU Accelerated Machine.
https://aws.amazon.com/blogs/aws/new-g2-instance-type-with-4x-more-gpu-power/
Installation scripts:
Install Nvidia Drivers, CUDNn, Python, TensorFlow on Ubuntu 16.04
Provision Machine
- AMI
Ubuntu Server 14.04 LTS (HVM), SSD Volume Type
- Select Instance Type
http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using_cluster_computing.html
About CUDA Cores （2560）
Nvidia GPU Product Matrix
Install TensorFlow with pip
manual
使用python3
- # ubuntu @ dagama in ~ [2:54:27] C:1
- $ cd /usr/local/bin
- # ubuntu @ dagama in /usr/local/bin [2:54:46]
- $ ls -l|grep pip
- -rwxr-xr-x 1 root root 204 Nov 7 11:08 pip
- -rwxr-xr-x 1 root root 204 Nov 7 11:08 pip2
- -rwxr-xr-x 1 root root 204 Nov 7 11:08 pip2.7
- $ sudo mv pip2 ~/bakup1
- $ sudo mv pip2.7 ~/bakup1
- # ubuntu @ dagama in /usr/local/bin [2:57:46]
- $ ls -l|grep pip
- -rwxr-xr-x 1 root root 204 Nov 7 11:08 pip
- ###尝试用pip安装模块,以查看pip是否安装成功###
- $ pip install wheel
- Traceback (most recent call last):
- File "/usr/local/bin/pip", line 7, in <module>
- from pip import main
- ImportError: No module named 'pip
- ###应该是安装python3的pip? 并更新pip###
- $ sudo apt-get install python3-pip
- $sudo pip install --upgrade pip
- $ pip --version
- pip 9.0.1 from /usr/local/lib/python3.4/dist-packages (python 3.4)
- sudo apt-get install python-numpy python-scipy python-matplotlib ipython ipython-notebook python-pandas python-sympy python-nose
- # 直接利用"pip install -U scikit-learn "安装scikit-learn,会提示"UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 52: ordinal not in range(128)"的错误,可以先升级一下setuptools,如下
- sudo pip install --upgrade setuptools
- sudo pip install -U scikit-learn # 安装成功
- # Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
- # Requires CUDA toolkit 7.5 and CuDNN v4. For other versions, see "Install from sources" below.
- $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.9.0-cp34-cp34m-linux_x86_64.whl
- # Python3
- $ sudo pip3 install --upgrade $TF_BINARY_UR
Clone the TensorFlow repository
- $ git clone https://github.com/tensorflow/tensorflow
https://aws.amazon.com/blogs/aws/new-g2-instance-type-with-4x-more-gpu-power/
Install utilities
- sudo apt-get install wget zsh git curl ack-grep -yy
manual
CUDA Driver
manual
- sudo dpkg -i cuda-repo-ubuntu1404_8.0.44-1_amd64.deb
- sudo apt-get update
- sudo apt-get install cuda
edit /etc/profile
- export CUDA_HOME=/usr/local/cuda
- export PATH=$PATH:$CUDA_HOME/bin
- export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_HOME/lib64
Install cuDNN v5.
Uncompress and copy the cuDNN files into the toolkit directory. Assuming the toolkit is installed in /usr/local/cuda, run the following commands (edited to reflect the cuDNN version you downloaded):
- tar xvzf cudnn-8.0-linux-x64-v5.1.tgz
- 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*
- cd /usr/local/cuda/lib64/
- sudo rm -rf libcudnn.so libcudnn.so.5
- sudo ln -s libcudnn.so.5.0.5 libcudnn.so.5
- sudo ln -s libcudnn.so.5 libcudnn.so
manual
For Ubuntu Trusty (14.04 LTS) users, since OpenJDK 8 is not available on Trusty, please install Oracle JDK 8:
- $ sudo add-apt-repository ppa:webupd8team/java
- $ sudo apt-get update
- $ sudo apt-get install oracle-java8-installer
- $ sudo apt-get update && sudo apt-get install bazel
- #Once installed, you can upgrade to newer version of Bazel with:
- $ sudo apt-get upgrade bazel
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