linuxserver/foldingathome¶
Folding@home 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.
Supported Architectures¶
We utilise the docker manifest for multi-platform awareness. More information is available from docker here and our announcement here.
Simply pulling lscr.io/linuxserver/foldingathome:latest should retrieve the correct image for your arch, but you can also pull specific arch images via tags.
The architectures supported by this image are:
| Architecture | Available | Tag |
|---|---|---|
| x86-64 | ✅ | amd64-<version tag> |
| arm64 | ✅ | arm64v8-<version tag> |
| armhf | ❌ |
Application Setup¶
This image sets up the Folding@home client. The interface is available at https://app.foldingathome.org.
Before setting up this container, please register for an account on https://app.foldingathome.org and retrieve the account token shown in the account settings. That value should be populated in the ACCOUNT_TOKEN env var.
Once the container is created with the token and the machine name, the instance should be listed in the web app and can be controlled there.
Afterwards, the ACCOUNT_TOKEN and the MACHINE_NAME vars can be removed as the instance will already be associated with the online account and the info stored in the config folder.
Migration from version 7.6¶
Version 8 of fah-client has been rewritten and has some breaking changes that we can't automatically mitigate in this container.
Unlike v7, v8 no longer bundles a local webgui. The web app is loaded from an online source and can only auto-detect instances that are running on the same machine (bare metal) as the browser. This is not possible in a docker container. Therefore, upgrading to v8 requires registering for an online account, retrieving the account token and setting it in the new env var ACCOUNT_TOKEN, along with a friendly name in MACHINE_NAME.
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://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
We automatically add the necessary environment variable that will utilise all the features available on a GPU on the host. Once nvidia container toolkit 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.
Usage¶
To help you get started creating a container from this image you can either use docker-compose or the docker cli.
docker-compose (recommended, click here for more info)¶
---
+ foldingathome - LinuxServer.io linuxserver/foldingathome¶
Folding@home 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.
Supported Architectures¶
We utilise the docker manifest for multi-platform awareness. More information is available from docker here and our announcement here.
Simply pulling lscr.io/linuxserver/foldingathome:latest should retrieve the correct image for your arch, but you can also pull specific arch images via tags.
The architectures supported by this image are:
Architecture Available Tag x86-64 ✅ amd64-<version tag> arm64 ✅ arm64v8-<version tag> armhf ❌
Application Setup¶
This image sets up the Folding@home client. The interface is available at https://app.foldingathome.org.
Before setting up this container, please register for an account on https://app.foldingathome.org and retrieve the account token shown in the account settings. That value should be populated in the ACCOUNT_TOKEN env var.
Once the container is created with the token and the machine name, the instance should be listed in the web app and can be controlled there.
Afterwards, the ACCOUNT_TOKEN and the MACHINE_NAME vars can be removed as the instance will already be associated with the online account and the info stored in the config folder.
Migration from version 7.6¶
Version 8 of fah-client has been rewritten and has some breaking changes that we can't automatically mitigate in this container.
Unlike v7, v8 no longer bundles a local webgui. The web app is loaded from an online source and can only auto-detect instances that are running on the same machine (bare metal) as the browser. This is not possible in a docker container. Therefore, upgrading to v8 requires registering for an online account, retrieving the account token and setting it in the new env var ACCOUNT_TOKEN, along with a friendly name in MACHINE_NAME.
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://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html We automatically add the necessary environment variable that will utilise all the features available on a GPU on the host. Once nvidia container toolkit 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.
Usage¶
To help you get started creating a container from this image you can either use docker-compose or the docker cli.
docker-compose (recommended, click here for more info)¶
---
services:
foldingathome:
image: lscr.io/linuxserver/foldingathome:latest
@@ -50,4 +50,4 @@
--pull \
-t lscr.io/linuxserver/foldingathome:latest .
The ARM variants can be built on x86_64 hardware using multiarch/qemu-user-static
Once registered you can define the dockerfile to use with -f Dockerfile.aarch64.
Versions¶
- 25.06.24: - Breaking Changes - Please see the Application Setup section for more details. Restructure image for F@H v8.
- 15.06.24: - Rebase to Ubuntu Noble, add optional cli args.
- 14.12.22: - Rebase to Ubuntu Jammy, migrate to s6v3.
- 15.01.22: - Rebase to Ubuntu Focal. Add arm64v8 builds (cpu only). Increase verbosity about gpu driver permission settings.
- 09.01.21: - Add nvidia.icd.
- 14.04.20: - Add Folding@home donation links.
- 20.03.20: - Initial release.
\ No newline at end of file
+Once registered you can define the dockerfile to use with -f Dockerfile.aarch64.
Versions¶
- 25.06.24: - Breaking Changes - Please see the Application Setup section for more details. Restructure image for F@H v8.
- 15.06.24: - Rebase to Ubuntu Noble, add optional cli args.
- 14.12.22: - Rebase to Ubuntu Jammy, migrate to s6v3.
- 15.01.22: - Rebase to Ubuntu Focal. Add arm64v8 builds (cpu only). Increase verbosity about gpu driver permission settings.
- 09.01.21: - Add nvidia.icd.
- 14.04.20: - Add Folding@home donation links.
- 20.03.20: - Initial release.