Installing Nvidia, Cuda, CuDNN, TensorFlow and Keras

lspci -nnk | grep -i nvidia4b:00.0 VGA compatible controller [0300]: NVIDIA Corporation Device [10de:1b80] (rev a1)
4b:00.1 Audio device [0403]: NVIDIA Corporation Device [10de:10f0] (rev a1)
sudo apt-get update
sudo apt-get install libglu1-mesa libxi-dev libxmu-dev -y
sudo apt-get — yes install build-essential
sudo apt-get install python-pip python-dev -y
sudo apt-get install python-numpy python-scipy -y
wget chmod +x
./ --silent
wget chmod +x
./ --driver --silent
./ --toolkit --silent
./ --samples --silent
echo ‘export LD_LIBRARY_PATH=”$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"’ >> ~/.bashrcecho 'export CUDA_HOME=/usr/local/cuda' >> ~/.bashrc

The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN is part of the NVIDIA Deep Learning SDK.

UPDATE: I’ve noticed the latest version of TensorFlow 0.10 only works with CudNN v5.1, possibly because this version of TF is still in development.

sudo scp cudnn-7.0-linux-x64-v4.0-prod.tgz root@
tar -xzvf cudnn-7.0-linux-x64-v4.0-prod.tgz
cp cuda/lib64/* /usr/local/cuda/lib64/
cp cuda/include/cudnn.h /usr/local/cuda/include/
pip install — upgrade
python -m tensorflow.models.image.mnist.convolutional
sudo apt-get install python-numpy python-scipy -y
sudo apt-get install python-yaml -y
sudo apt-get install libhdf5-serial-dev -y
sudo pip install keras==1.0.8



Curious about Deep Learning, NLP, AI. Hopeful traveler, wannabe chef.

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