This script automates the setup of an Amazon EC2 Graviton ARM-based instances for deep learning tasks. It takes care of installing essential utilities, setting up latest Nvidia drivers and CUDA 12.2 toolkit and cuDNN library, and build PyTorch from source. The step-by-step guided can be checked here. https://jumpshare.com/blog/deep-learning-on-a…

ec2_graviton_dl_bootstrap.sh

#!/bin/bash
set -e # Exit on any error

# Check if required arguments are provided
if [ -z "$REGION" ] || [ -z "$SECURITY_GROUPS" ] || [ -z "$KEY_PAIR" ] || [ -z "$SUBNET" ]; then
    echo "Error: You must provide REGION, SECURITY_GROUPS, KEY_PAIR, and SUBNET as environment variables."
    echo "Example:"
    echo "  export REGION=us-east-1"
    echo "  export SECURITY_GROUPS=sg-12345678,sg-87654321"
    echo "  export KEY_PAIR=my-key-pair"
    echo "  export SUBNET=subnet-12345678"
    exit 1
fi

USER_DATA=$(cat <<'EOF'
#!/bin/bash
set -e # Exit on any error

CUDA_HOME=/usr/local/cuda
HOME_DIR=/home/ec2-user
SRC_DIR=$HOME_DIR/sources
USER_EXEC="sudo -u ec2-user"
CPUS=$(nproc)
LOG_FILE="$HOME_DIR/install.log"

if [[ "$1" != "--stdout" ]]; then
exec >>"$LOG_FILE" 2>&1
fi

# Create source directory
mkdir -p $SRC_DIR
pushd $SRC_DIR

# Install system utilities and updates
install_utils() {
    echo "Installing utilities..."
    dnf -y update
    dnf -y groupinstall "Development Tools"
    dnf install -y openssl-devel cmake3 rust cargo amazon-efs-utils htop iotop yasm nasm jq python3-pip python-devel cronie cronie-anacron
    echo "Success : Updates and developer tools installed."
    echo "PATH=$CUDA_HOME/bin:\$PATH" | sudo tee -a $HOME_DIR/.bashrc
    echo "LD_LIBRARY_PATH=$CUDA_HOME/lib64:\$LD_LIBRARY_PATH" | sudo tee -a $HOME_DIR/.bashrc

    echo "/usr/local/lib" | sudo tee /etc/ld.so.conf.d/usr-local-lib.conf
    echo "/usr/local/lib64" | sudo tee -a /etc/ld.so.conf.d/usr-local-lib.conf    
}

# Setup GPU, CUDA and CUDNN
setup_gpu() {
    echo "Setting up GPU..."
    wget https://us.download.nvidia.com/tesla/535.104.05/NVIDIA-Linux-aarch64-535.104.05.run
    sh NVIDIA-Linux-aarch64-535.104.05.run --disable-nouveau --silent

    wget https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda_12.2.2_535.104.05_linux_sbsa.run
    sh cuda_12.2.2_535.104.05_linux_sbsa.run --silent --override --toolkit --samples --toolkitpath=/usr/local/cuda-12.2 --samplespath=$CUDA_HOME --no-opengl-libs

    wget https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-sbsa/cudnn-linux-sbsa-8.9.4.25_cuda12-archive.tar.xz
    tar -xf cudnn-linux-sbsa-8.9.4.25_cuda12-archive.tar.xz
    cp -P cudnn-linux-sbsa-8.9.4.25_cuda12-archive/include/* $CUDA_HOME/include/
    cp -P cudnn-linux-sbsa-8.9.4.25_cuda12-archive/lib/* $CUDA_HOME/lib64/
    chmod a+r $CUDA_HOME/lib64/*
    ldconfig
    rm -fr cu* NVIDIA*
}

# Install PyTorch from source
install_pytorch() {
    echo "Installing PyTorch..."
    wget https://github.com/ccache/ccache/releases/download/v4.8.3/ccache-4.8.3.tar.xz
    tar -xf ccache-4.8.3.tar.xz
    pushd ccache-4.8.3
    cmake .
    make -j $CPUS
    popd
    dnf install -y numpy
    pip3 install typing-extensions
    git clone --recursive https://github.com/pytorch/pytorch.git
    pushd pytorch
    python3 setup.py install
    popd
    ldconfig
    $USER_EXEC pip3 install sympy filelock fsspec networkx
}

mount -o remount,size=15G /tmp/

# Execute Functions
install_utils
setup_gpu
source $HOME_DIR/.bashrc
install_pytorch

# Cleanup
popd
rm -fr $SRC_DIR

# Test the installation
$USER_EXEC python3 -c "import torch; print('Congratulations, you are all set to go.' if torch.cuda.is_available() else 'Something went wrong. Please check if you missed any steps.')"

EOF
)

AMI_ID="ami-0b9ce70cf1bc24fc3"

aws ec2 run-instances \
    --image-id $AMI_ID \
    --instance-type g5g.4xlarge \
    --key-name $KEY_PAIR \
    --subnet-id $SUBNET \
    --security-group-ids $SECURITY_GROUPS \
    --region $REGION \
    --block-device-mappings '[{"DeviceName":"/dev/xvda","Ebs":{"VolumeSize":100,"VolumeType":"gp3"}}]' \
    --tag-specifications 'ResourceType=instance,Tags=[{Key=Name,Value=AMI-Builder}]' \
    --user-data "$USER_DATA"

linear.sh

/bin/bash
whoami
# ec2-user
pwd
# /home/ec2-user


sudo su

CUDA_HOME=/usr/local/cuda
HOME_DIR=/home/ec2-user
SRC_DIR=$HOME_DIR/sources
USER_EXEC="sudo -u ec2-user"
CPUS=$(nproc)
LOG_FILE="$HOME_DIR/install.log"

mkdir -p $SRC_DIR
pushd $SRC_DIR

dnf -y update
dnf -y groupinstall "Development Tools"
dnf install -y openssl-devel cmake3 rust cargo amazon-efs-utils htop iotop yasm nasm jq python3-pip python-devel cronie cronie-anacron
echo "Success : Updates and developer tools installed."
echo "PATH=$CUDA_HOME/bin:\$PATH" | sudo tee -a $HOME_DIR/.bashrc
echo "LD_LIBRARY_PATH=$CUDA_HOME/lib64:\$LD_LIBRARY_PATH" | sudo tee -a $HOME_DIR/.bashrc

echo "/usr/local/lib" | sudo tee /etc/ld.so.conf.d/usr-local-lib.conf
echo "/usr/local/lib64" | sudo tee -a /etc/ld.so.conf.d/usr-local-lib.conf

cat .bashrc
# .bashrc

# Source global definitions
if [ -f /etc/bashrc ]; then
	. /etc/bashrc
fi

# User specific environment
if ! [[ "$PATH" =~ "$HOME/.local/bin:$HOME/bin:" ]]
then
    PATH="$HOME/.local/bin:$HOME/bin:$PATH"
fi
export PATH

# Uncomment the following line if you don't like systemctl's auto-paging feature:
# export SYSTEMD_PAGER=

# User specific aliases and functions
if [ -d ~/.bashrc.d ]; then
	for rc in ~/.bashrc.d/*; do
		if [ -f "$rc" ]; then
			. "$rc"
		fi
	done
fi

unset rc
PATH=/usr/local/cuda/bin:$PATH
LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

echo "Setting up GPU..."
wget https://us.download.nvidia.com/tesla/535.104.05/NVIDIA-Linux-aarch64-535.104.05.run
sh NVIDIA-Linux-aarch64-535.104.05.run --disable-nouveau --silent

wget https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda_12.2.2_535.104.05_linux_sbsa.run
sh cuda_12.2.2_535.104.05_linux_sbsa.run --silent --override --toolkit --samples --toolkitpath=/usr/local/cuda-12.2 --samplespath=$CUDA_HOME --no-opengl-libs

wget https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-sbsa/cudnn-linux-sbsa-8.9.4.25_cuda12-archive.tar.xz
tar -xf cudnn-linux-sbsa-8.9.4.25_cuda12-archive.tar.xz
cp -P cudnn-linux-sbsa-8.9.4.25_cuda12-archive/include/* $CUDA_HOME/include/
cp -P cudnn-linux-sbsa-8.9.4.25_cuda12-archive/lib/* $CUDA_HOME/lib64/
chmod a+r $CUDA_HOME/lib64/*
ldconfig

echo "Installing PyTorch..."
wget https://github.com/ccache/ccache/releases/download/v4.8.3/ccache-4.8.3.tar.xz
tar -xf ccache-4.8.3.tar.xz
pushd ccache-4.8.3
cmake .
make -j $CPUS
popd
dnf install -y numpy
pip3 install typing-extensions
git clone --recursive https://github.com/pytorch/pytorch.git
pushd pytorch
python3 setup.py install
popd
ldconfig
sudo -u ec2-user pip3 install sympy filelock fsspec networkx

export LD_LIBRARY_PATH=/usr/local/cuda-12.6/lib64:/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export PATH=/usr/local/cuda-12.6/bin:/usr/local/cuda/bin:$PATH
export USE_PRIORITIZED_TEXT_FOR_LD=1
sudo dnf install kernel-modules-extra
wget https://us.download.nvidia.com/tesla/560.35.03/NVIDIA-Linux-aarch64-560.35.03.run
sh NVIDIA-Linux-aarch64-560.35.03.run --disable-nouveau --silent

wget https://developer.download.nvidia.com/compute/cuda/12.6.0/local_installers/cuda_12.6.0_560.28.03_linux_sbsa.run
sudo sh cuda_12.6.0_560.28.03_linux_sbsa.run --silent --override --toolkit --samples --toolkitpath=/usr/local/cuda-12.6 --samplespath=$CUDA_HOME --no-opengl-libs

wget https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-sbsa/cudnn-linux-sbsa-9.3.0.75_cuda12-archive.tar.xz
tar -xf cudnn-linux-sbsa-9.3.0.75_cuda12-archive.tar.xz
cp -P cudnn-linux-sbsa-9.3.0.75_cuda12-archive/include/* $CUDA_HOME/include/
cp -P cudnn-linux-sbsa-9.3.0.75_cuda12-archive/lib/* $CUDA_HOME/lib64/
chmod a+r $CUDA_HOME/lib64/*

LD_LIBRARY_PATH includes /usr/local/cuda-12.6/lib64, or, add /usr/local/cuda-12.6/lib64 to /etc/ld.so.conf and run ldconfig as root

ldconfig

pip3 install opencv-python-headless