diff --git a/README.md b/README.md index c76ba5c812f4438b6bacb4f58581f224ea4690f0..75e444077a4f64a958b062bc0bb7a3d45b5e324d 100644 --- a/README.md +++ b/README.md @@ -15,38 +15,38 @@ sudo apt install -y ./*.deb Plese note that enroot has been installed on Berzelius. You can skip this installation step if you plan to use it on Berzeliu. ## Set up Nvidia credentials +This step is necessary for importing container images from Nvidia NGC. -Complete step [4.1](https://docs.nvidia.com/ngc/ngc-overview/index.html#account-signup) and [4.3](https://docs.nvidia.com/ngc/ngc-overview/index.html#generating-api-key). Save the API key. +- Complete step [4.1](https://docs.nvidia.com/ngc/ngc-overview/index.html#account-signup) and [4.3](https://docs.nvidia.com/ngc/ngc-overview/index.html#generating-api-key). Save the API key. -Add the API key to the config file at ```~/.config/enroot/.credentials ``` +- Add the API key to the config file at ```~/.config/enroot/.credentials ``` ``` machine nvcr.io login $oauthtoken password your_api_key machine authn.nvidia.com login $oauthtoken password your_api_key ``` -Set the config path by adding the line to ```~/.bashrc``` +- Set the config path by adding the line to ```~/.bashrc``` ``` export ENROOT_CONFIG_PATH=/home/xuagu37/.config/enroot ``` -To make the path valid +- To make the path valid ``` source ~/.bashrc ``` -This step is necessary for importing container images from Nvidia NGC. ## Import container images You can import a container image either from Nvidia NGC or Pytorch/Tensorflow official Docker Hub repositories. -From Nvidia NGC +- From Nvidia NGC ``` enroot import 'docker://nvcr.io#nvidia/pytorch:22.09-py3' enroot import 'docker://nvcr.io#nvidia/tensorflow:22.11-tf2-py3' ``` For other versions, please see the release notes for [Pytorch](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html) and [Tensorflow](https://docs.nvidia.com/deeplearning/frameworks/tensorflow-release-notes/index.html). -From Pytorch/Tensorflow official Docker Hub repositories +- From Pytorch/Tensorflow official Docker Hub repositories ``` enroot import 'docker://pytorch/pytorch:1.12.1-cuda11.3-cudnn8-devel' enroot import 'docker://tensorflow/tensorflow:2.11.0-gpu' @@ -62,19 +62,19 @@ enroot create --name nvidia_pytorch_22.09 nvidia+pytorch+22.09-py3.sqsh ## Start a container -As the root user +- As the root user ``` enroot start --root --rw --mount /proj/nsc_testing/xuan:/proj/nsc_testing/xuan nvidia_pytorch_22.09 ``` -As a non-root user +- As a non-root user ``` enroot start --rw --mount /proj/nsc_testing/xuan:/proj/nsc_testing/xuan nvidia_pytorch_22.09 ``` The flag ```--mount``` mounts your local directory to your container. -You can also start a container and run your command at the same time. +- You can also start a container and run your command at the same time. ``` enroot start --rw --mount /proj/nsc_testing/xuan:/proj/nsc_testing/xuan nvidia_pytorch_22.09 sh -c 'python path_to_your_script.py' ```