The following are tested system settings, newer hardware/software could also work but has not been tested.
- CPU (AMD Ryzen 9 7900X 12-Core Processor)
- GPU (NVIDIA GeForce GTX 1080)
- GNU/Linux x86_64 (Ubuntu Server 22.04.2 LTS kernel 5.15.0-75-generic)
- Docker (Docker version 24.0.2, build cb74dfc)
- NVIDIA Driver (Version 530.41.03, your NVIDIA Driver version should at least support CUDA 10.1)
👉 Please refer to official install guide for installing Docker.
Create the docker group:
sudo groupadd docker
Add your user to the docker group:
sudo usermod -aG docker $USER
newgrp docker
👉 You may need to start a new session to update the groups.
docker run hello-world
This command downloads a test image and runs it in a container. When the container runs, it prints a message and exits.
The recommended way to install drivers is to use the package manager for your distribution but other installer mechanisms are also available (e.g., by downloading .run installers from NVIDIA driver Downloads).
👉 installed NVIDIA driver should be compatible with CUDA Toolkit 10.1.
nvidia-smi
you should see similar output as the following:
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 530.41.03 Driver Version: 530.41.03 CUDA Version: 12.1 |
|-----------------------------------------+----------------------+----------------------+
| 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 GeForce GTX 1080 Off| 00000000:01:00.0 Off | N/A |
| 0% 37C P8 8W / 215W| 2MiB / 8192MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
👉 Please also refer to official install guide 1 and install guide 2. And remember to configure the Docker daemon to recognize the NVIDIA Container Runtime and then restart the Docker daemon.
See the first step in Section Setting up NVIDIA Container Toolkit to setup the package repo and the GPG key.
sudo apt-get update
sudo apt-get install nvidia-container-runtime
sudo systemctl restart docker
docker run -it --rm --gpus all ubuntu nvidia-smi
This should result in a console output similar as the one shown below:
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 530.41.03 Driver Version: 530.41.03 CUDA Version: 12.1 |
|-----------------------------------------+----------------------+----------------------+
| 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 GeForce GTX 1080 Off| 00000000:01:00.0 Off | N/A |
| 0% 37C P8 8W / 215W| 2MiB / 8192MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
Now you are ready to run the MgNet in Docker!
git clone https://github.com/Vfold-RNA/MgNet.git ${HOME}/MgNet
cd ${HOME}/MgNet && ./setup
mgnet -h
mgnet -i ${HOME}/MgNet/example/example.pdb -o ${HOME}/MgNet/example/
The ions predicted by 5 trained models will be saved into ${HOME}/MgNet/example/ as xxxx_model_y_prediction.pdb, where xxxx and y represents name of the input pdb and index of the trained model, respectively.
mgnet -l
mgnet -r
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