NuPIC Studio is an all­-in-­one tool that allows users create a HTM neural network from scratch

Overview

NuPIC Studio Logo NuPIC Studio *nix Build Status

NuPIC Studio is an all­-in-­one tool that allows users create a HTM neural network from scratch, train it, collect statistics, and share it among the members of the community. It is not just a visualization tool but an HTM builder, debugger and laboratory for experiments. It is ideal for newbies with little intimacy with NuPIC code as well as experts that wish a better productivity. Among its features and advantages:

  • Users can open, save, or change their "HTM projects" or of other developers. A typical project contains data to be trained, neural network configuration, statistics, etc, which can be shared to be analysed or integrated with other projects.
  • The HTM engine is the own original NuPIC libray (Python distribution). This means no port, no bindings, no re-implementation, etc. So any changes in the original nupic source can be immediatedly viewed. This helps users that wish test improvements like new encoders or even hierarchy, attention, and motor integration.

Screenshot

For more information, see numenta.org or the NuPIC Studio wiki.

Installation

Currently supported platforms:

  • Windows
  • Linux (32/64bit)
  • Mac OSX

Dependencies:

  • Python (2.7 or later)
  • PIP
  • NuPIC
  • NumPy
  • PyQt5

User instructions

If you want only use it, simply do this:

pip install nupic_studio

Note: Dear *nix users, if you get a "permission denied" error when using pip, you may add the --user flag to install to a location in your home directory, which should resolve any permissions issues. Doing this, you may need to add this location to your PATH and PYTHONPATH. Alternatively, you can run pip with 'sudo'.

Once it is installed, you can execute the app using:

nupic_studio

and then click on Open Project button to open any example to getting started with NuPIC.

Developer instructions

If you want develop, debug, or simply test NuPIC Studio, clone it and follow the instructions:

Using command line

This assumes the NUPIC_STUDIO environment variable is set to the directory where the NuPIC Studio source code exists.

cd $NUPIC_STUDIO
python setup.py build
python setup.py develop

Using an IDE

The following instructions will work in the most Python IDEs:

  • Open your IDE.
  • Open a project specifying the $NUPIC_STUDIO repository folder as location.
  • Click with mouse right button on setup.py file listed on project files and select Run command on pop-up menu. This will call the build process. Check output panel to see the result.
  • If the build was successful, just click on program.py and voilà!

If you don't have a favourite Python IDE, this article can help you to choose one: http://pedrokroger.net/choosing-best-python-ide/

Owner
HTM Community
Home for community-led HTM repositories.
HTM Community
Data Preparation, Processing, and Visualization for MoVi Data

MoVi-Toolbox Data Preparation, Processing, and Visualization for MoVi Data, https://www.biomotionlab.ca/movi/ MoVi is a large multipurpose dataset of

Saeed Ghorbani 51 Nov 27, 2022
Boosted CVaR Classification (NeurIPS 2021)

Boosted CVaR Classification Runtian Zhai, Chen Dan, Arun Sai Suggala, Zico Kolter, Pradeep Ravikumar NeurIPS 2021 Table of Contents Quick Start Train

Runtian Zhai 4 Feb 15, 2022
People Interaction Graph

Gihan Jayatilaka*, Jameel Hassan*, Suren Sritharan*, Janith Senananayaka, Harshana Weligampola, et. al., 2021. Holistic Interpretation of Public Scenes Using Computer Vision and Temporal Graphs to Id

University of Peradeniya : COVID Research Group 1 Aug 24, 2022
Official implementation of Deep Convolutional Dictionary Learning for Image Denoising.

DCDicL for Image Denoising Hongyi Zheng*, Hongwei Yong*, Lei Zhang, "Deep Convolutional Dictionary Learning for Image Denoising," in CVPR 2021. (* Equ

Z80 91 Dec 21, 2022
YOLOX-Paddle - A reproduction of YOLOX by PaddlePaddle

YOLOX-Paddle A reproduction of YOLOX by PaddlePaddle 数据集准备 下载COCO数据集,准备为如下路径 /ho

QuanHao Guo 6 Dec 18, 2022
Membership Inference Attack against Graph Neural Networks

MIA GNN Project Starter If you meet the version mismatch error for Lasagne library, please use following command to upgrade Lasagne library. pip insta

6 Nov 09, 2022
P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks

P-tuning v2 P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks An optimized prompt tuning strategy achievi

THUDM 540 Dec 30, 2022
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search

B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search This is the offical implementation of the

SNU ADSL 0 Feb 07, 2022
Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

CoGAIL Table of Content Overview Installation Dataset Training Evaluation Trained Checkpoints Acknowledgement Citations License Overview This reposito

Jeremy Wang 29 Dec 24, 2022
Episodic-memory - Ego4D Episodic Memory Benchmark

Ego4D Episodic Memory Benchmark EGO4D is the world's largest egocentric (first p

3 Feb 18, 2022
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations This is the repository for the paper Consumer Fairness in Recomm

7 Nov 30, 2022
Myia prototyping

Myia Myia is a new differentiable programming language. It aims to support large scale high performance computations (e.g. linear algebra) and their g

Mila 456 Nov 07, 2022
Neural Style and MSG-Net

PyTorch-Style-Transfer This repo provides PyTorch Implementation of MSG-Net (ours) and Neural Style (Gatys et al. CVPR 2016), which has been included

Hang Zhang 904 Dec 21, 2022
GULAG: GUessing LAnGuages with neural networks

GULAG: GUessing LAnGuages with neural networks Classify languages in text via neural networks. Привет! My name is Egor. Was für ein herrliches Frühl

Egor Spirin 12 Sep 02, 2022
Implementation of: "Exploring Randomly Wired Neural Networks for Image Recognition"

RandWireNN Unofficial PyTorch Implementation of: Exploring Randomly Wired Neural Networks for Image Recognition. Results Validation result on Imagenet

Seung-won Park 684 Nov 02, 2022
Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning.

Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning. Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive

<a href=[email protected](SZ)"> 7 Dec 16, 2021
A Jinja extension (compatible with Flask and other frameworks) to compile and/or compress your assets.

A Jinja extension (compatible with Flask and other frameworks) to compile and/or compress your assets.

Jayson Reis 94 Nov 21, 2022
Torch code for our CVPR 2018 paper "Residual Dense Network for Image Super-Resolution" (Spotlight)

Residual Dense Network for Image Super-Resolution This repository is for RDN introduced in the following paper Yulun Zhang, Yapeng Tian, Yu Kong, Bine

Yulun Zhang 494 Dec 30, 2022
This repo contains research materials released by members of the Google Brain team in Tokyo.

Brain Tokyo Workshop 🧠 🗼 This repo contains research materials released by members of the Google Brain team in Tokyo. Past Projects Weight Agnostic

Google 1.2k Jan 02, 2023
Free Book about Deep-Learning approaches for Chess (like AlphaZero, Leela Chess Zero and Stockfish NNUE)

Free Book about Deep-Learning approaches for Chess (like AlphaZero, Leela Chess Zero and Stockfish NNUE)

Dominik Klein 189 Dec 21, 2022