[DeepStream] ubuntu에서 nvidia docker + ngc 설치하기

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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/

Building a Real-time Redaction App Using NVIDIA DeepStream, Part 1: Training | NVIDIA Developer Blog

Some of the biggest challenges in deploying an AI-based application are the accuracy of the model and being able to extract insights in real time. There’s a trade-off between accuracy and inference…

developer.nvidia.com

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

ngc.nvidia.com/

NVIDIA NGC

ngc.nvidia.com

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

Installation Guide — NVIDIA Cloud Native Technologies documentation

On RHEL 7, install the nvidia-container-toolkit package (and dependencies) after updating the package listing: Restart the Docker daemon to complete the installation after setting the default runtime: Note Depending on how your RHEL 7 system is configured

docs.nvidia.com

참고자료 2 : ngc.nvidia.com/setup/installers/cli