torchani223-mamba141-ase322-jupyter

TorchANI + ASE + JupyterLab

TorchANI is a software package based on PyTorch and offers a family of (second-generation) ANI neural network potentials. Supported potentials include ANI-1, ANI-1x, ANI-1ccx and ANI-2x.

Source Specifications

• Developers: Roitberg_Group

• Source: aiqm/torchani

• Documentation: https://aiqm.github.io/torchani/index.html

MolSSI Container Hub Specifications

• Repository: https://hub.docker.com/r/molssi/torchani223-mamba141-ase322-jupyter

• Tags: https://hub.docker.com/r/molssi/torchani223-mamba141-ase322-jupyter/tags

• Source: molssi/molssi-hub

• Recipe: molssi/molssi-hub

• Image pull command:

  docker pull molssi/torchani223-mamba141-ase322-jupyter:latest
  

• Container run command:

  docker run -it --name torchani-jupyter -v $(pwd):/home -p 8888:8888 molssi/torchani223-mamba141-ase322-jupyter:latest
  

Note

By default, the -v $(pwd):/home option mounts the current working directory to /home in your running container. Doing so, the contents of your current working directory become available in your running container. If you do not wish this to happen, you may simply remove or change this option. By default, Jupyter server communicates to your computed through port 8888. Make sure to include -p 8888:8888 in the docker run command. For NVIDIA GPU support with nvidia containers, add the --runtime nvidia --gpus all flags to the previous container run command and then run nvidia-smi to make sure all available GPUs on the docker host are visible inside the docker container.

Image Specifications

• OS/Arch: debian:bullseye-slim (linux/amd64)

• Users (UID): root (0)

• Groups (GID): root (0)

• Environment variables:

  • CONDA_PREFIX: /opt/conda

  • PATH: ${CONDA_PREFIX}/bin:$PATH

• Volumes: None

• Network:

  • ip: *

  • port: 8888

• Extras:

  • Added directories: None

  • Important packages installed: ase 3.22.1, jupyterlab, mamba 1.4.1, matplotlib, numpy, scipy, pytorch-cuda 11.7, torchaudio, torchvision