Linux¶

On Linux, run FastSurfer in one of our container images: they contain the full pipeline (segmentation and surface reconstruction) and all software it needs. The surface reconstruction also needs a FreeSurfer license, which is free but not included: get it from FreeSurfer before your first full run. We provide images for NVIDIA GPUs (CUDA), for AMD GPUs (ROCm, experimental) and for the CPU only, on Docker Hub.

A GPU with enough memory makes the segmentation much faster, see the system requirements. Without a GPU, use the CPU image.

The images are tagged deepmi/fastsurfer:<device>-v<version>, where <device> is cu<cuda_version> for NVIDIA GPUs (see below), rocm<rocm_version> for AMD GPUs (experimental), or cpu without GPU support (smaller and thus faster to download), and <version> is the FastSurfer version. latest points to the newest NVIDIA image, with the default CUDA version of that release, and cpu-latest to the newest CPU image. Docker Hub lists all tags. For reproducible results, use a versioned tag.

NVIDIA GPUs¶

We build the images for two CUDA versions, because newer CUDA versions drop old GPUs and older ones lack the newest:

Image	CUDA	GPUs	NVIDIA driver
`cu132-v<version>` (default)	13.2	Turing (RTX 20, T4) to Blackwell (RTX 50, B200)	580 or newer
`cu126-v<version>`	12.6	Maxwell (GTX 900) to Hopper (H100), including Pascal (GTX 10, P100) and Volta (V100), but not Blackwell	525 or newer

Use the default image unless your GPU is older than Turing or your driver is older than 580; then use the 12.6 image. nvidia-smi shows the name of your GPU and the driver version.

If FastSurfer cannot use your GPU, because the image has no support for it, the driver is too old, or the container was started without access to the GPU, it says so, names the image or flag to use instead, and runs on the CPU, which takes much longer.

Apptainer or Singularity¶

Apptainer is the open-source continuation of Singularity, and both run the same images. Apptainer also installs the singularity command, so the commands below work with either; with Apptainer, you can also write apptainer instead of singularity.

With Apptainer (or Singularity) installed, build an image from our Docker image, for example in $HOME/my_singularity_images:

Note

The commands below use the tagged FastSurfer image :cu132-v2.6.0. CUDA 13.2 is the default CUDA version bundled in both :latest and that image. Images of 2.6.0 for other CUDA versions, ROCm and CPU are available on Docker Hub.

mkdir -p $HOME/my_singularity_images
singularity build \
    $HOME/my_singularity_images/fastsurfer-cu132-v2.6.0.sif \
    docker://deepmi/fastsurfer:cu132-v2.6.0

Running FastSurfer in a container shows how to run FastSurfer with it and explains the flags.

Docker¶

With Docker installed, download our image:

Note

docker pull deepmi/fastsurfer:cu132-v2.6.0

Running FastSurfer in a container shows how to run FastSurfer with it and explains the flags.

For NVIDIA GPUs, Docker needs the NVIDIA Container Toolkit. In Docker’s rootless mode, also follow its configuration for the rootless mode, otherwise Docker stops with docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].

CPU only¶

Without a GPU, use the CPU image, which is smaller. It runs the same pipeline, only the segmentation takes longer. For Apptainer (or Singularity):

mkdir -p $HOME/my_singularity_images
singularity build \
    $HOME/my_singularity_images/fastsurfer-cpu-v2.6.0.sif \
    docker://deepmi/fastsurfer:cpu-v2.6.0

or, for Docker:

docker pull deepmi/fastsurfer:cpu-v2.6.0

Run it as described in Running FastSurfer in a container, without --nv (Apptainer or Singularity) or --gpus all (Docker).

AMD GPUs (experimental)¶

We have successfully run the segmentation on an AMD GPU (Radeon Pro W6600) with ROCm. This needs a supported (or semi-supported) GPU and the right kernel version. Install the AMD kernel modules on the host as described in the ROCm installation instructions, and add your user to the groups they name.

Then download our image for ROCm 7.2:

docker pull deepmi/fastsurfer:rocm7.2-v2.6.0

AMD needs a few more flags in the docker run command, see Running FastSurfer in a container for <docker_flags> and <fastsurfer_flags>. To build your own ROCm image, see Building FastSurfer Docker images.

Usage:

docker run --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
        --device=/dev/kfd --device=/dev/dri --group-add video \
        --ipc=host --shm-size 8G \
        <docker_flags> deepmi/fastsurfer:rocm7.2-v2.6.0 \
                <fastsurfer_flags>

This image is experimental and uses different Python packages, so its results can differ from our validation results. Check them visually.

Installing from source¶

To install FastSurfer without containers, see installing from source.