[개발환경] RTX 4090 Ubuntu 22.04 딥러닝 학습 서버 환경 구축 https://aeong-dev.tistory.com/1 sudo apt-get update sudo apt install -y ubuntu-drivers-common lspci | grep -i nvidia uname -m && cat /etc/*release sudo apt install build-essential uname -r sudo apt-get install linux-headers-$(uname -r) sudo lshw -numeric -C display sudo ubuntu-drivers devices sudo apt install nvidia-driver-570 sudo reboot nvidia-smi wget https://developer.download.nvidia.com/compute/cuda/12.9.1/local_installers/cuda_12.9.1_575.57.08_linux.run sudo sh cuda_12.9.1_575.57.08_linux.run wget https://developer.download.nvidia.com/compute/cuda/12.8.1/local_installers/cuda_12.8.1_570.124.06_linux.run sudo sh cuda_12.8.1_570.124.06_linux.run =========== = Summary = =========== Driver: Not Selected Toolkit: Installed in /usr/local/cuda-12.9/ Please make sure that - PATH includes /usr/local/cuda-12.9/bin - LD_LIBRARY_PATH includes /usr/local/cuda-12.9/lib64, or, add /usr/local/cuda-12.9/lib64 to /etc/ld.so.conf and run ldconfig as root To uninstall the CUDA Toolkit, run cuda-uninstaller in /usr/local/cuda-12.9/bin ***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 575.00 is required for CUDA 12.9 functionality to work. To install the driver using this installer, run the following command, replacing with the name of this run file: sudo .run --silent --driver Logfile is /var/log/cuda-installer.log sudo nano ~/.bashrc export PATH="/usr/local/cuda/bin:$PATH" export LD_LIBRARY_PATH="/usr/local/cuda/lib64:$LD_LIBRARY_PATH" cuDNN 설치 tar -xvf cudnn-linux-x86_64-8.9.7.29_cuda12-archive.tar.xz cd cudnn-linux-x86_64-8.9.7.29_cuda12-archive/ sudo mv ./include/cudnn*.h /usr/local/cuda/include sudo mv ./lib/libcudnn* /usr/local/cuda/lib64 sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2 cd .. rm -rf cudnn-linux-x86_64-8.9.7.29_cuda12-archive/ [Ubuntu] GPU 그래픽카드 실시간 사용량 확인 https://zorang2.tistory.com/106 watch -d -n 0.5 nvidia-smi Window | 윈도우 원격 데스크톱으로 ubuntu 접속하기, mstsc로 ubuntu 접속 https://iambeginnerdeveloper.tistory.com/304 systemctl status xrdp 도커설치 sudo apt-get update -y sudo apt-get upgrade -y sudo apt install apt-transport-https ca-certificates curl software-properties-common -y curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null sudo apt update sudo apt install docker-ce docker-ce-cli containerd.io sudo systemctl start docker sudo systemctl enable docker sudo systemctl status docker sudo cat /etc/group sudo usermod -aG docker "nominc" docker --version 도커 컴포즈 (Docker-Compose) 설치 //--------------------------- // DOCKER 사용법 /****************************************************************** docker 관련명령어 [참조] https://subicura.com/2017/01/19/docker-guide-for-beginners-2.html ******************************************************************/ docker search 설치할내용 docker pull 설치할내용 docker start $(docker ps -a -q) docker stop $(docker ps -a -q) docker restart $(docker ps -a -q) :: 전체삭제 주의 모든 도커 컨테이너 삭제(remove all docker containers) docker rm $(docker ps -a -q) 모든 도커 이미지 삭제(remove all docker images) docker rmi $(docker images -q) :: 전체삭제 주의 docker ps -a docker images docker 컨테이너 접속 docker exec -it 컨테이너번호 /bin/bash 도커 재시작 docker stop c24a329837c7 docker stop 71254fc1f81f docker start c24a329837c7 docker start 71254fc1f81f docker ps -a /opt/manager/myfirewall.sh iptables -L -v docker restart c24a329837c7 docker restart 71254fc1f81f docker ps -a iptables -L -v *** docker update 이후, Error response from daemon: oci runtime error 가 날 경우 // 2022/03/14 10:33 김태관 service docker stop cd /run/runc/ 하위 내용 모두 삭제 또는 이동 후 재시작 service docker start docker start .... Installing the NVIDIA Container Toolkit # https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html # Ubuntu/Debian curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \ && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \ sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \ sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list sudo apt-get update export NVIDIA_CONTAINER_TOOLKIT_VERSION=1.17.8-1 sudo apt-get install -y \ nvidia-container-toolkit=${NVIDIA_CONTAINER_TOOLKIT_VERSION} \ nvidia-container-toolkit-base=${NVIDIA_CONTAINER_TOOLKIT_VERSION} \ libnvidia-container-tools=${NVIDIA_CONTAINER_TOOLKIT_VERSION} \ libnvidia-container1=${NVIDIA_CONTAINER_TOOLKIT_VERSION} sudo systemctl restart docker 개인용 로컬 AI 서버 만들기 (feat. Docker Compose) # https://velog.io/@io_/%EA%B0%9C%EC%9D%B8-%EB%A1%9C%EC%BB%AC-AI-%ED%99%88%EC%84%9C%EB%B2%84-%EA%B5%AC%EC%B6%95%ED%95%98%EA%B8%B0-feat.-Docker-Compose git clone https://github.com/valiantlynx/ollama-docker.git cd ./ollama-docker sudo docker compose -f ./docker-compose-ollama-gpu.yaml up -d sudo docker compose -f ./docker-compose-ollama-gpu.yaml down 언어모델 설치는 ollama 안에서 생성한다. docker exec -it ollama /bin/bash ollama run gemma3:12b