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Alpha Centauri - Multi-GPU Sub-Cluster

The sub-cluster "Alpha Centauri" had been installed for AI-related computations (ScaDS.AI). It has 34 nodes, each with:

  • 8 x NVIDIA A100-SXM4 (40 GB RAM)
  • 2 x AMD EPYC CPU 7352 (24 cores) @ 2.3 GHz with multi-threading enabled
  • 1 TB RAM 3.5 TB /tmp local NVMe device
  • Hostnames: taurusi[8001-8034]
  • Slurm partition alpha for batch jobs and alpha-interactive for interactive jobs


The NVIDIA A100 GPUs may only be used with CUDA 11 or later. Earlier versions do not recognize the new hardware properly. Make sure the software you are using is built with CUDA11.



The easiest way is using the module system. The software for the partition alpha is available in modenv/hiera module environment.

To check the available modules for modenv/hiera, use the command

marie@alpha$ module spider <module_name>

For example, to check whether PyTorch is available in version 1.7.1:

marie@alpha$ module spider PyTorch/1.7.1

  PyTorch: PyTorch/1.7.1
      Tensors and Dynamic neural networks in Python with strong GPU acceleration. PyTorch is a deep learning framework that puts Python

    You will need to load all module(s) on any one of the lines below before the "PyTorch/1.7.1" module is available to load.

      modenv/hiera  GCC/10.2.0  CUDA/11.1.1  OpenMPI/4.0.5


The output of module spider <module_name> provides hints which dependencies should be loaded beforehand:

marie@alpha$ module load modenv/hiera GCC/10.2.0 CUDA/11.1.1 OpenMPI/4.0.5
Module GCC/10.2.0, CUDA/11.1.1, OpenMPI/4.0.5 and 15 dependencies loaded.
marie@alpha$ module avail PyTorch
-------------------------------------- /sw/modules/hiera/all/MPI/GCC-CUDA/10.2.0-11.1.1/OpenMPI/4.0.5 ---------------------------------------
   PyTorch/1.7.1 (L)    PyTorch/1.9.0 (D)
marie@alpha$ module load PyTorch/1.7.1
Module PyTorch/1.7.1 and 39 dependencies loaded.
marie@alpha$ python -c "import torch; print(torch.__version__); print(torch.cuda.is_available())"

Python Virtual Environments

Virtual environments allow users to install additional python packages and create an isolated runtime environment. We recommend using virtualenv for this purpose.

marie@login$ srun --partition=alpha-interactive --nodes=1 --cpus-per-task=1 --gres=gpu:1 --time=01:00:00 --pty bash
marie@alpha$ mkdir python-environments                               # please use workspaces
marie@alpha$ module load modenv/hiera GCC/10.2.0 CUDA/11.1.1 OpenMPI/4.0.5 PyTorch
Module GCC/10.2.0, CUDA/11.1.1, OpenMPI/4.0.5, PyTorch/1.9.0 and 54 dependencies loaded.
marie@alpha$ which python
marie@alpha$ pip list
marie@alpha$ virtualenv --system-site-packages python-environments/my-torch-env
created virtual environment in 42960ms
  creator CPython3Posix(dest=~/python-environments/my-torch-env, clear=False, global=True)
  seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=~/.local/share/virtualenv)
    added seed packages: pip==21.1.3, setuptools==57.2.0, wheel==0.36.2
  activators BashActivator,CShellActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator
marie@alpha$ source python-environments/my-torch-env/bin/activate
(my-torch-env) marie@alpha$ pip install torchvision
Installing collected packages: torchvision
Successfully installed torchvision-0.10.0
(my-torch-env) marie@alpha$ python -c "import torchvision; print(torchvision.__version__)"
(my-torch-env) marie@alpha$ deactivate


JupyterHub can be used to run Jupyter notebooks on Alpha Centauri sub-cluster. As a starting configuration, a "GPU (NVIDIA Ampere A100)" preset can be used in the advanced form. In order to use latest software, it is recommended to choose fosscuda-2020b as a standard environment. Already installed modules from modenv/hiera can be preloaded in "Preload modules (modules load):" field.


Singularity containers enable users to have full control of their software environment. For more information, see the Singularity container details.

Nvidia NGC containers can be used as an effective solution for machine learning related tasks. (Downloading containers requires registration). Nvidia-prepared containers with software solutions for specific scientific problems can simplify the deployment of deep learning workloads on HPC. NGC containers have shown consistent performance compared to directly run code.