https://www.pugetsystems.com/labs/hpc/How-To-Setup-NVIDIA-Docker-and-NGC-Registry-on-your-Workstation---Part-4-Accessing-the-NGC-Registry-1115/
https://www.nvidia.com/en-us/gpu-cloud
https://ngc.nvidia.com/setup/installers/cli
# signup/login
https://ngc.nvidia.com/signin
https://ngc.nvidia.com/setup/api-key
# Generate API Key
# Generate your own API key in order to use the NGC service through the Docker client or through NGC CLI.
https://ngc.nvidia.com/setup
https://ngc.nvidia.com/setup/installers/cli
AMD64 Linux Install
The NGC CLI binary for Linux is supported on Ubuntu 16.04 and later distributions.
Click
Download CLI to download the zip file that contains the binary, then
transfer the zip file to a directory where you have permissions and then
unzip and execute the binary. You can also download, unzip, and install
from the command line by moving to a directory where you have execute
permissions and then running the following command:
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
Check the binary's md5 hash to ensure the file wasn't corrupted during download:
Add your current directory to path:
echo "export PATH=\"\$PATH:$(pwd)\"" >> ~/.bash_profile && source ~/.bash_profile
You must configure NGC CLI for your use so that you can run the commands.
Enter the following command, including your API key when prompted:
Linux Uninstall:
Remove the binary file:
rm `which ngc`
"... Or, create a file (or alias) to make that a single command."
"To make that process a little easier you can put the following into a file, (maybe call it ngc-login)
docker login -u '$oauthtoken' --password-stdin nvcr.io <<< ' your API key here between the quotes '
Then to change the permisions to make it only accessible by you,
chmod 700 ngc-login"
~/Docker/ngc_login.sh
WARNING! Your password will be stored unencrypted in /home/.../.docker/config.json.
Configure a credential helper to remove this warning. See
https://docs.docker.com/engine/reference/commandline/login/#credentials-store
Login Succeeded
$ docker run --runtime=nvidia --rm nvcr.io/nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04 nvidia-smi
Unable to find image 'nvcr.io/nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04' locally
9.0-cudnn7-devel-ubuntu16.04: Pulling from nvidia/cuda
4007a89234b4: Pull complete
c1de0f9cdfc1: Pull complete
c8ee6ca703b8: Pull complete
b39e2761d3d4: Pull complete
6fd4d5fe5247: Pull complete
a2aa5ab1b28d: Pull complete
d115189e3588: Pull complete
c208532c0f67: Pull complete
822dc7c71a4b: Pull complete
05d21eb076f4: Pull complete
b62c22f14976: Pull complete
Digest: sha256:eb0f7d58b15893c7d3d676d23e9a4abaaaa6c09019f24bce29a448a9b8a261d8
Status: Downloaded newer image for nvcr.io/nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
Tue May 4 03:20:53 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.73.01 Driver Version: 460.73.01 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 GeForce GTX 106... Off | 00000000:01:00.0 Off | N/A |
| 0% 36C P8 7W / 200W | 6MiB / 6078MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
# Setup the Docker credential store
https://docs.docker.com/engine/reference/commandline/login/#credentials-store
https://github.com/docker/docker-credential-helpers/releases
# NGC commands
https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html
=====================================================
The NGC registry is the main component of NGC. NGC stands for "NVIDIA
GPU Cloud". That consists of an NVIDIA maintained Docker registry and
an AWS machine instance (AMI) on Amazon AWS. That AMI has a setup
similar to what I have gone through in first 2 posts of this series
(User-namespaces are not configured).
It's the NGC Docker registry that contains all of the great
container images and you can use those docker images on your own
workstation. You don't need to access AWS and you don't need to have an
AWS account. However, you do need to sign up for NGC in order to get a
registry key to access those docker images. It's a private registry so
you need an account with NVIDIA NGC and the registry key to login to it.
There are 4 section in the registry,
- Deep Learning
- HPC Apps
- HPC Visualization
- And a few "partner" docker images
Here are the GPU optimized application container images is in the
repository as of this writing, (using labels as listed in the registry
repository)
- caffe
- caffe2
- cntk
- cuda
- digits
- mxnet
- pytorch
- tensorflow
- tensorrt
- theano
- torch
- gamess
- gromacs
- lammps
- namd
- relion
- paraview-holodeck
- paraview-index
- paraview-optix
- chainer
- h2oai-driverless
- mapd
- paddlepaddle
