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Commit 98b48d5c authored by Xuan Gu's avatar Xuan Gu
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Update README.md

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...@@ -32,74 +32,56 @@ To make the path valid ...@@ -32,74 +32,56 @@ To make the path valid
``` ```
source ~/.bashrc source ~/.bashrc
``` ```
This step is necessary for importing container images from Nvidia NGC.
## Integrate with your tools ## Import container images
- [ ] [Set up project integrations](https://gitlab.liu.se/xuagu37/run-pytorch-and-tensorflow-containers-with-nvidia-enroot/-/settings/integrations) You can import a container image either from Nvidia NGC or Pytorch/Tensorflow official Docker Hub repositories.
## Collaborate with your team From Nvidia NGC
```
- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/) enroot import 'docker://nvcr.io#nvidia/pytorch:22.09-py3'
- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html) enroot import 'docker://nvcr.io#nvidia/tensorflow:22.11-tf2-py3'
- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically) ```
- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/) 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).
- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
## Test and Deploy
Use the built-in continuous integration in GitLab.
- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
***
# Editing this README
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
## Suggestions for a good README
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
## Name
Choose a self-explaining name for your project.
## Description
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
## Badges
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
## Visuals From Pytorch/Tensorflow official Docker Hub repositories
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method. ```
enroot import 'docker://pytorch/pytorch:1.12.1-cuda11.3-cudnn8-devel'
enroot import 'docker://tensorflow/tensorflow:2.11.0-gpu'
```
For other versions, please see the Docker tags for [Pytorch](https://hub.docker.com/r/pytorch/pytorch/tags) and [Tensorflow](https://hub.docker.com/r/tensorflow/tensorflow/tags).
## Installation ## Create a container
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
## Usage I will only take Pytorch from Nvidia NGC for an example.
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README. ```
enroot create --name nvidia_pytorch_22.09 nvidia+pytorch+22.09-py3.sqsh
```
## Support ## Start a container
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
## Roadmap As the root user
If you have ideas for releases in the future, it is a good idea to list them in the README. ```
enroot start --root --rw --mount /proj/nsc_testing/xuan:/proj/nsc_testing/xuan nvidia_pytorch_22.09
```
## Contributing As a non-root user
State if you are open to contributions and what your requirements are for accepting them. ```
enroot start --rw --mount /proj/nsc_testing/xuan:/proj/nsc_testing/xuan nvidia_pytorch_22.09
```
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self. The flag ```--mount``` mounts your local directory to your container.
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser. 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'
```
## Authors and acknowledgment ## Access to GUI
Show your appreciation to those who have contributed to the project.
## License ```
For open source projects, say how it is licensed. enroot start --rw --env DISPLAY --mount /tmp/.X11-unix:/tmp/.X11-unix --mount /proj/nsc_testing/xuan:/proj/nsc_testing/xuan nvidia_pytorch_22.09
```
## Project status Please note that you need to use the flag ```-X``` when connecting to Berzelius.
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
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