A Python Package Simulating For NVIDIA GPU Acceleration

In this post, I am going to describe one of the GitHub Repository, A Python Package Simulating For NVIDIA GPU Acceleration.

A python package simulating the quasi-2D pseudospin-1/2 Gross-Pitaevskii equation with NVIDIA GPU acceleration.


spinor-gpe is high-level, object-oriented Python package for numerically solving the quasi-2D, psuedospinor (two component) Gross-Piteavskii equation (GPE), for both ground state solutions and real-time dynamics. This project grew out of a desire to make high-performance simulations of the GPE more accessible to the entering researcher.

While this package is primarily built on NumPy, the main computational heavy-lifting is performed using PyTorch, a deep neural network library commonly used in machine learning applications. PyTorch has a NumPy-like interface, but a backend that can run either on a conventional processor or a CUDA-enabled NVIDIA(R) graphics card. Accessing a CUDA device will provide a significant hardware acceleration of the simulations.

This package has been tested on Windows 10.


Primary packages:

  1. PyTorch >= 1.8.0
  2. cudatoolkit >= 11.1
  3. NumPy

Other packages:

  1. matplotlib (visualizing results)
  2. tqdm (progress messages)
  3. scikit-image (matrix signal processing)
  4. ffmpeg = 4.3.1 (animation generation)

Installing Dependencies

The dependencies for spinor-gpe can be installed directly into the new conda virtual environment spinor using the environment.yml file included with the package:

conda env create --file environment.yml

Note, this installation may take a while.

The dependencies can also be installed manually using conda into a virtual environment:

conda activate <new_virt_env_name>
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c conda-forge
conda install numpy matplotlib tqdm scikit-image ffmpeg spyder


For more information on installing PyTorch, see its installation instructions page.

To verify that Pytorch was installed correctly, you should be able to import it:

>>>import torch
>>>x = torch.rand(5, 3)
tensor([[0.2757, 0.3957, 0.9074],
        [0.6304, 0.1279, 0.7565],
        [0.0946, 0.7667, 0.2934],
        [0.9395, 0.4782, 0.9530],
        [0.2400, 0.0020, 0.9569]])

Also, if you have an NVIDIA GPU, you can test that it is available for GPU computing:




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Lingaraj Senapati
Hey There! I am Lingaraj Senapati, the Co-founder of lingarajtechhub.com My skills are Freelance, Web Developer & Designer, Corporate Trainer, Digital Marketer & Youtuber.
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