installation.md

Installation and Running Your First Prediction

You will need a machine running Linux; AlphaFold 3 does not support other operating systems. Full installation requires up to 1 TB of disk space to keep genetic databases (SSD storage is recommended) and an NVIDIA GPU with Compute Capability 8.0 or greater (GPUs with more memory can predict larger protein structures). We have verified that inputs with up to 5,120 tokens can fit on a single NVIDIA A100 80 GB, or a single NVIDIA H100 80 GB. We have verified numerical accuracy on both NVIDIA A100 and H100 GPUs.

Especially for long targets, the genetic search stage can consume a lot of RAM – we recommend running with at least 64 GB of RAM.

We provide installation instructions for a machine with an NVIDIA A100 80 GB GPU and a clean Ubuntu 22.04 LTS installation, and expect that these instructions should aid others with different setups.

The instructions provided below describe how to:

1. Provision a machine on GCP.
2. Install Docker.
3. Install NVIDIA drivers for an A100.
4. Obtain genetic databases.
5. Obtain model parameters.
6. Build the AlphaFold 3 Docker container or Singularity image.

Provisioning a Machine

Clean Ubuntu images are available on Google Cloud, AWS, Azure, and other major platforms.

We first provisioned a new machine in Google Cloud Platform using the following command. We were using a Google Cloud project that was already set up.

• We recommend using --machine-type a2-ultragpu-1g but feel free to use --machine-type a2-highgpu-1g for smaller predictions.
• If desired, replace --zone us-central1-a with a zone that has quota for the machine you have selected. See gpu-regions-zones.

gcloud compute instances create alphafold3 \
    --machine-type a2-ultragpu-1g \
    --zone us-central1-a \
    --image-family ubuntu-2204-lts \
    --image-project ubuntu-os-cloud \
    --maintenance-policy TERMINATE \
    --boot-disk-size 1000 \
    --boot-disk-type pd-balanced

This provisions a bare Ubuntu 22.04 LTS image on an A2 Ultra machine with 12 CPUs, 170 GB RAM, 1 TB disk and NVIDIA A100 80 GB GPU attached. We verified the following installation steps from this point.

Installing Docker

These instructions are for rootless Docker.

Installing Docker on Host

Note these instructions only apply to Ubuntu 22.04 LTS images, see above.

Add Docker's official GPG key. Official Docker instructions are here. The commands we ran are:

sudo apt-get update
sudo apt-get install ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc

Add the repository to apt sources:

echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \
  $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update
sudo apt-get install -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
sudo docker run hello-world

Enabling Rootless Docker

Official Docker instructions are here. The commands we ran are:

sudo apt-get install -y uidmap systemd-container

sudo machinectl shell $(whoami)@ /bin/bash -c 'dockerd-rootless-setuptool.sh install && sudo loginctl enable-linger $(whoami) && DOCKER_HOST=unix:///run/user/1001/docker.sock docker context use rootless'

Installing GPU Support

Installing NVIDIA Drivers

Official Ubuntu instructions are here. The commands we ran are:

sudo apt-get -y install alsa-utils ubuntu-drivers-common
sudo ubuntu-drivers install

sudo nvidia-smi --gpu-reset

nvidia-smi  # Check that the drivers are installed.

Accept "Pending kernel upgrade" dialog if it appears.

You will need to reboot the instance with sudo reboot now to reset the GPU if you see the following warning:

NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver.
Make sure that the latest NVIDIA driver is installed and running.

Proceed only if nvidia-smi has a sensible output.

Installing NVIDIA Support for Docker

Official NVIDIA instructions are here. The commands we ran are:

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
nvidia-ctk runtime configure --runtime=docker --config=$HOME/.config/docker/daemon.json
systemctl --user restart docker
sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place

Check that your container can see the GPU:

docker run --rm --gpus all nvidia/cuda:12.6.0-base-ubuntu22.04 nvidia-smi

The output should look similar to this:

Mon Nov  11 12:00:00 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.120                Driver Version: 550.120        CUDA Version: 12.6     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA A100-SXM4-80GB          Off |   00000000:00:05.0 Off |                    0 |
| N/A   34C    P0             51W /  400W |       1MiB /  81920MiB |      0%      Default |
|                                         |                        |             Disabled |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|  No running processes found                                                             |
+-----------------------------------------------------------------------------------------+

Obtaining AlphaFold 3 Source Code

You will need to have git installed to download the AlphaFold 3 repository: