Installs everything you need to run TF 2.0-preview on an Ubuntu 18.04 server (includes the Colab connector) - run as regular user with sudo rights

tf2-colab.sh

#!/bin/bash

cat <<EOF > install_cuda_10_and_nvidia_driver_384.sh
#!/bin/bash
apt-get update && apt-get install -y --no-install-recommends gnupg2 curl ca-certificates && \
    curl -fsSL https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub | apt-key add - && \
    echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 /" > /etc/apt/sources.list.d/cuda.list && \
    echo "deb https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 /" > /etc/apt/sources.list.d/nvidia-ml.list

export CUDA_VERSION=10.0.130

export CUDA_PKG_VERSION="10-0=$CUDA_VERSION-1"

# For libraries in the cuda-compat-* package: https://docs.nvidia.com/cuda/eula/index.html#attachment-a
apt-get update && apt-get install -y --no-install-recommends \
        cuda-cudart-$CUDA_PKG_VERSION \
        cuda-compat-10-0=410.48-1 && \
    ln -s cuda-10.0 /usr/local/cuda

export PATH=/usr/local/cuda/bin:${PATH}

# nvidia-container-runtime
export NVIDIA_VISIBLE_DEVICES=all
export NVIDIA_DRIVER_CAPABILITIES=compute,utility
export NVIDIA_REQUIRE_CUDA="cuda>=10.0 brand=tesla,driver>=384,driver<385"

export CUDNN_VERSION=7.4.1.5

apt-get install -y --no-install-recommends \
            libcudnn7=$CUDNN_VERSION-1+cuda10.0 \
            libcudnn7-dev=$CUDNN_VERSION-1+cuda10.0 && \
    apt-mark hold libcudnn7

apt-get install -y --no-install-recommends \
        build-essential \
        cuda-command-line-tools-10-0 \
        cuda-cublas-10-0 \
        cuda-cufft-10-0 \
        cuda-curand-10-0 \
        cuda-cusolver-10-0 \
        cuda-cusparse-10-0 \
        libcudnn7=$CUDNN_VERSION-1+cuda10.0 \
        libfreetype6-dev \
        libhdf5-serial-dev \
        libpng-dev \
        libzmq3-dev \
        pkg-config \
        software-properties-common \
        unzip

apt-get install -y nvidia-384

apt-get install -y nvinfer-runtime-trt-repo-ubuntu1804-5.0.2-ga-cuda10.0 \
        && apt-get update \
        && apt-get install -y --no-install-recommends libnvinfer5=5.0.2-1+cuda10.0 \
        && apt-get clean \
        && rm -rf /var/lib/apt/lists/*

# For CUDA profiling, TensorFlow requires CUPTI.
export LD_LIBRARY_PATH=/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH

export PYTHON=python3
export PIP=pip3

# See http://bugs.python.org/issue19846
export LANG=C.UTF-8

apt-get update && apt-get install -y \
    python3 \
    python3-pip

pip3 --no-cache-dir install --upgrade \
    pip \
    setuptools

hash -d pip

# Some TF tools expect a "python" binary
ln -s $(which python3) /usr/local/bin/python 

nvidia-smi

EOF

chmod +x install_cuda_10_and_nvidia_driver_384.sh
sudo ./install_cuda_10_and_nvidia_driver_384.sh

grep export install_cuda_10_and_nvidia_driver_384.sh > ~/.bashrc
echo 'export PATH=$HOME/.local/bin:$PATH' >> ~/.bashrc

source ~/.bashrc

# Options:
#   tensorflow
#   tensorflow-gpu
#   tf-nightly
#   tf-nightly-gpu
#   tf-nightly-2.0-preview
#   tf-nightly-gpu-2.0-preview
export TF_PACKAGE=tf-nightly-gpu-2.0-preview

pip3 install --user ${TF_PACKAGE}
pip3 install --user jupyter jupyter_http_over_ws matplotlib numpy scipy Pillow pandas scikit-learn pydot graphviz
jupyter serverextension enable --py jupyter_http_over_ws

cat <<EOF > start-colab-server.sh
#!/bin/bash
jupyter notebook \
  --NotebookApp.allow_origin='https://colab.research.google.com' \
  --port=8888 \
  --NotebookApp.port_retries=0
EOF

chmod +x start-colab-server.sh