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Using the Amazon EC2 g4dn.xlarge instance which costs $0.526/hour, all installations will finish in 30 minutes.
Install packages.
sudo apt-get update
sudo apt-get install build-essential tar curl zip unzip autopoint libtool bison libx11-dev libxft-dev libxext-dev libxrandr-dev libxi-dev libxcursor-dev libxdamage-dev libxinerama-dev libxtst-dev cmake libgflags-dev libopencv-dev python3-dev
Install CUDA and cuDNN.
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt-get update
sudo apt install cuda-drivers
nvidia-smi
sudo apt install cuda-toolkit-12-8
vi ~/.bashrc
export PATH="/usr/local/cuda/bin${PATH:+:${PATH}}"
export LD_LIBRARY_PATH="/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}"
source ~/.bashrc
nvcc --version
apt-cache search libcudnn
sudo apt install libcudnn9-cuda-12
sudo apt install libcudnn9-dev-cuda-12
Install PyTorch.
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
source ~/.bashrc
conda create --name my_env python=3.10
conda activate my_env
conda install pytorch torchvision torchaudio pytorch-cuda -c pytorch -c nvidia
import torch
torch.cuda.is_available()
pip install opencv-python onnx optimum[onnxruntime-gpu] onnxsim matplotlib numba
Download LibTorch.
https://pytorch.org/get-started/locally/
wget https://download.pytorch.org/libtorch/cu128/libtorch-cxx11-abi-shared-with-deps-2.7.0%2Bcu128.zip
unzip libtorch-cxx11-abi-shared-with-deps-2.7.0+cu128.zip
Download ONNX Runtime.
https://github.com/microsoft/onnxruntime/releases/
wget https://github.com/microsoft/onnxruntime/releases/download/v1.20.0/onnxruntime-linux-x64-gpu-1.20.0.tgz
tar -xzvpf onnxruntime-linux-x64-gpu-1.20.0.tgz