ikbelkirasan/install.md

## install.md

      
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              install.md
            
          
    Install Tensorflow with GPU support

To install tensorflow, you can either install it with pip (or pip3) or with Docker.
Install using pip
# Install using pip (for python3)
pip3 install --user tensorflow-gpu
Install using Docker
# Tensorflow image with GPU support (python3)
docker pull tensorflow/tensorflow:latest-gpu-py3
Install CUDA 10 on Ubuntu 18.04
# Add NVIDIA package repositories
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1804_10.0.130-1_amd64.deb
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
sudo apt-get update
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
sudo apt install ./nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
sudo apt-get update

# Install NVIDIA driver
sudo apt-get install --no-install-recommends nvidia-driver-410
# Reboot. Check that GPUs are visible using the command: nvidia-smi

# Install development and runtime libraries (~4GB)
sudo apt-get install --no-install-recommends \
    cuda-10-0 \
    libcudnn7=7.4.1.5-1+cuda10.0  \
    libcudnn7-dev=7.4.1.5-1+cuda10.0


# Note: Make sure that you're running nvidia-driver-410 in 'Additional Drivers on Ubuntu'

# Install TensorRT. Requires that libcudnn7 is installed above.
sudo apt-get update && \
        sudo apt-get install nvinfer-runtime-trt-repo-ubuntu1804-5.0.2-ga-cuda10.0 \
        && sudo apt-get update \
        && sudo apt-get install -y --no-install-recommends libnvinfer-dev=5.0.2-1+cuda10.0
Install nvidia-docker
# If you have nvidia-docker 1.0 installed: we need to remove it and all existing GPU containers
docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f
sudo apt-get purge -y nvidia-docker

# Add the package repositories
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
  sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update

# Install nvidia-docker2 and reload the Docker daemon configuration
sudo apt-get install -y nvidia-docker2
sudo pkill -SIGHUP dockerd

# Test nvidia-smi with the latest official CUDA image
docker run --runtime=nvidia --rm nvidia/cuda:9.0-base nvidia-smi
Test your code
# Runs `your_script.py` from the current working directory in a Docker container

docker run \
		-it \
		--rm \
		-u 1000:1000 \
		-v $(PWD):/app \
		tensorflow/tensorflow:latest-gpu-py3 \
	python3 /app/your_script.py