Author: Deep Learner

Redis

Python cluster client for the official redis cluster

This client provides a client for redis cluster that was added in redis 3.0. This project is a port of redis-rb-cluster by antirez, with a lot of added functionality. The original source can be found at https://github.com/antirez/redis-rb-cluster The branch master will always contain the latest unstable/development code that has been merged from Pull Requests. Use the latest commit from master branch on your own risk, there is no guarantees of compatibility or stability of non tagged commits on the master […]

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Python

Speed up your Conda installs with Mamba

Conda installs can be very very very slow. Every time you run conda install: It has to collect the package metadata. It has to solve the environment. … maybe you can take a coffee break here, or go work on a jigsaw puzzle to relax … It has to download packages. Eventually, finally, it will install the packages it downloaded. By the time this is all done you’ve probably forgotten what it was you were trying to do in the […]

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PyTorch

Lite Inference Toolkit (LIT) for PyTorch

PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices. With the rapid growth of deep learning research, models are becoming increasingly complex in terms of parameters and complexity, making it difficult to run the models on currently available end devices. For example, GPT-J with 6B parameters only needs 24 GB of RAM in full-precision mode to be ready for execution, which may be impossible in most systems; even […]

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Natural Language Processing

Word Of The Day based on Natural Language Toolkit

• This Project is based on NLTK(Natural Language Toolkit) • It generates a RANDOM WORD from predefined list of words • From that random word it read out the word, it’s meaning with parts of speech,it’s antonyms,its synonyms • Using Windows Task Scheduler we make this project to run when user logged on • For IELTS enthusiats, it’s a wonderful script to improve their fluency in english GitHub View Github    

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Parser

Web-server with the parser, connection to DBMS, and machine learning

Web-server with parser, connection to DBMS and machine learning. Team: Aisha Bazylzhanova(SE-2004), Arysbay Dastan(SE-2004) Installation To install, you need to download webserver.py, database.py and templates from the repository and save them in the same folder. Also you need to install Firefox browser and install geckodriver. Usage In database.py file you need to provide your data app.config[‘SQLALCHEMY_DATABASE_URI’] = ‘postgresql://YourUsername:[email protected]/NameOfYourDatabase’ In webServer.py file you need to provide your data

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Scripts

SomaFM Plugin for Kodi

This description is a bit outdated. You can simply install this addon by browsing the official repositories from within Kodi. Installation Installation currently requires you to know where your addons folder is located. Please refer tothe Kodi Wiki article on userdata to find it. To find theaddons folder, simply replace every instance of userdata in the article with addons. Git Change into your addons folder Clone the repository into a new folder plugin.audio.somafm Done On Linux and possibly Mac OSX […]

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Attention

Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation

Introduction This is the official implementation of the paper “Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation“. Installation This repo is built using mmdetection.To install the dependencies, first clone the repository locally: git clone https://github.com/anirudh-chakravarthy/objprop.git Then, install PyTorch 1.1.0, torchvision 0.3.0, mmcv 0.2.12: conda install pytorch==1.1.0 torchvision==0.3.0 -c pytorch pip install mmcv==0.2.12 Then, install the CocoAPI for YouTube-VIS conda install cython scipy pip install git+https://github.com/youtubevos/cocoapi.git#”egg=pycocotools&subdirectory=PythonAPI”    

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Simulation

Create large-scale ML-driven multiscale simulation ensembles to study the interactions

MuMMI RAS v0.1 Released: Nov 16, 2021 MuMMI RAS is the application component of the MuMMI frameworkdeveloped to create large-scale ML-driven multiscale simulation ensembles tostudy the interactions of RAS proteins and RAS-RAF protein complexes with lipidplasma membranes. MuMMI framework was developed as part of the Pilot2 project of theJoint Design of Advanced Computing Solutions for Cancerfunded jointly by the Department of Energy (DOE) and theNational Cancer Institute (NCI). The Pilot 2 project focuses on developing multiscale simulation models forunderstanding the […]

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