Hardware Configutation
S7050GM2NR
CPU: E5-2650 v3 x1
RAM: DDR4 8GB x4
OS: Ubuntu 14.04 LTS Desktop 64bit
GPU: Nvidia Tesla K80 x1 (Driver: v352.93)
Setup Environment
1.
Install Ubuntu 14.04 LTS
Desktop
2.
Boot to text mode
vi
/etc/default/grub
mark
GRUB_CMDLINE_LINUX_DEFAULT=”quiet”
change
GRUB_CMDLINE_LINUX=”text”
umark
GRUB_TERMINAL=console
save
update grub via command:
update-grub
reboot system
Install K80 Driver
./NVIDIA-Linux-x86_64-352.93.run
apt-get update
apt-get install –y
build-essential git libblas-dev libopencv-dev libatlas-base-dev
Download MXnet
1.
git clone –recursive https://github.com/dmlc/mxnet
Install CUDA 7.5 environment
dpkg –I
cuda-repo-ubuntu1404_7.5-18_amd64.deb
You can also type follows command if you
have CUDA 7.5 source file already
dpkg
–I cuda-repo-ubuntu1404-7.5-local_7.5-18_amd64.deb
apt-get update
apt-get install cuda
Set CUDA environment
export
PATH=/usr/local/cuda-7.5/bin:$PATH
export
LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib64:$LD_LIBRARY_PATH
Check CUDA environment
printenv LD_LIBRARY_PATH
nvidia-smi
Set CUDA for MXnet
cd /temp/mxnet
cp /temp/mxnet/make/config.mk /temp/mxnet/.
Modify config.mk
USE_CUDA = 1
USE_CUDA_PATH = /usr/local/cuda
USE_BLAS = atlas
Complie MXNET
make –j4
Install Python
apt-get install python-setuptools python-dev
python-pip
pip install numpy
cd python; python setup.py install
Test MXnet
cd /temp/mxnet/example/image-classificatopm
python train_mnist.py <= use CPU only
python train_mnist.py --gpus “0,1” <=use
GPUs
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