[Folding@home](https://foldingathome.org/) is a distributed computing project for simulating protein dynamics, including the process of protein folding and the movements of proteins implicated in a variety of diseases. It brings together citizen scientists who volunteer to run simulations of protein dynamics on their personal computers. Insights from this data are helping scientists to better understand biology, and providing new opportunities for developing therapeutics.
We utilise the docker manifest for multi-platform awareness. More information is available from docker [here](https://github.com/docker/distribution/blob/master/docs/spec/manifest-v2-2.md#manifest-list) and our announcement [here](https://blog.linuxserver.io/2019/02/21/the-lsio-pipeline-project/).
Simply pulling `ghcr.io/linuxserver/foldingathome` should retrieve the correct image for your arch, but you can also pull specific arch images via tags.
This image sets up the Folding@home client. The interface is available at `http://your-ip:7396`.
The built-in webserver provides very basic control (ie. GPUs are only active when set to `Medium` or higher). For more fine grained control of individual devices, you can use the FAHControl app on a different device and connect remotely via port `36330` (no password).
There are a couple of minor issues with the webgui:
- If you get an "ERR_EMPTY_RESPONSE" error when trying to access via IP, it's most likely due to a clash of cookies/cache. Try opening in an incgnito window.
- If you're getting a constant refresh of the window but no display of info, try a force refresh via `shft-F5` or `ctrl-F5`.
## GPU Hardware Acceleration
### Nvidia
Hardware acceleration users for Nvidia will need to install the container runtime provided by Nvidia on their host, instructions can be found here:
https://github.com/NVIDIA/nvidia-docker
We automatically add the necessary environment variable that will utilise all the features available on a GPU on the host. Once nvidia-docker is installed on your host you will need to re/create the docker container with the nvidia container runtime `--runtime=nvidia` and add an environment variable `-e NVIDIA_VISIBLE_DEVICES=all` (can also be set to a specific gpu's UUID, this can be discovered by running `nvidia-smi --query-gpu=gpu_name,gpu_uuid --format=csv` ). NVIDIA automatically mounts the GPU and drivers from your host into the foldingathome docker container.
Docker images are configured using parameters passed at runtime (such as those above). These parameters are separated by a colon and indicate `<external>:<internal>` respectively. For example, `-p 8080:80` would expose port `80` from inside the container to be accessible from the host's IP on port `8080` outside the container.
For all of our images we provide the ability to override the default umask settings for services started within the containers using the optional `-e UMASK=022` setting.
Keep in mind umask is not chmod it subtracts from permissions based on it's value it does not add. Please read up [here](https://en.wikipedia.org/wiki/Umask) before asking for support.
When using volumes (`-v` flags), permissions issues can arise between the host OS and the container, we avoid this issue by allowing you to specify the user `PUID` and group `PGID`.
[](https://mods.linuxserver.io/?mod=foldingathome "view available mods for this container.") [](https://mods.linuxserver.io/?mod=universal "view available universal mods.")
We publish various [Docker Mods](https://github.com/linuxserver/docker-mods) to enable additional functionality within the containers. The list of Mods available for this image (if any) as well as universal mods that can be applied to any one of our images can be accessed via the dynamic badges above.
