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본 포스팅은 아래 링크를 실행하기 위한 준비과정이다.
참고로 아래 링크는 NVIDIA DeepStream을 이용하여 RetinaNet 모델을 학습시키는 과정에 관한 것이다.
developer.nvidia.com/blog/real-time-redaction-app-nvidia-deepstream-part-1-training/
1. nvidia-docker install
1.1 Setting up Docker
$ curl https://get.docker.com | sh \
&& sudo systemctl start docker \
&& sudo systemctl enable docker
1.2 Setting up NVIDIA Container Toollkit
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
&& 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
$ sudo apt-get install -y nvidia-docker2
$ sudo systemctl restart docker
1.3 Check
$ sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
Wed Jan 27 06:16:53 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.33.01 Driver Version: 440.33.01 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 2080 On | 00000000:01:00.0 On | N/A |
| 23% 64C P0 57W / 215W | 1518MiB / 7981MiB | 3% Default |
+-------------------------------+----------------------+----------------------+
| 1 GeForce RTX 2080 On | 00000000:02:00.0 Off | N/A |
| 22% 64C P2 59W / 215W | 125MiB / 7982MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
2. ngc install (AMD64 Linux Install)
2.1 install
$ wget -O ngccli_cat_linux.zip https://ngc.nvidia.com/downloads/ngccli_cat_linux.zip && unzip -o ngccli_cat_linux.zip && chmod u+x ngc
$ md5sum -c ngc.md5
$ echo "export PATH=\"\$PATH:$(pwd)\"" >> ~/.bash_profile && source ~/.bash_profile
$ ngc config set
2.2 ngc login
회원가입 후 로그인
2.3 Generate API Key
아래와 같은 경로에서 Generate API Key를 누르면 키가 생성된다.
좌측 상단 계정 클릭 > Setup > Generate API Key > Get API Key > Generate API Key(좌측 상단 초록 버튼)
2.4 login
$ ngc config set
$ docker login nvcr.io
Username: $oauthtoken
Password:
참고자료 1 : docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker
참고자료 2 : ngc.nvidia.com/setup/installers/cli
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