Python Automl-for-compression Resources

☕ CoFiPruning: Structured Pruning Learns Compact and Accurate Models This repository contains the code and pruned models for our ACL'22 paper Structur

Kompressor Branch CI Coverage main (active) main development A neural compression framework built on top of JAX. Install setup.py assumes a compatible

AutoLGBM LightGBM + Optuna: no brainer auto train lightgbm directly from CSV files auto tune lightgbm using optuna auto serve best lightgbm model usin

CATE: Computation-aware Neural Architecture Encoding with Transformers Code for paper: CATE: Computation-aware Neural Architecture Encoding with Trans

Awesome AutoDL A curated list of automated deep learning related resources. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awe

PyTorch Implementation of the SuRP algorithm by the authors of the AISTATS 2022 paper "An Information-Theoretic Justification for Model Pruning".

Code for paper "Exploring extreme parameter compression for pre-trained language models ICLR2022"

FewBit FewBit — a library for memory efficient training of large neural networks. Its efficiency originates from storage optimizations applied to back

The Compression Puzzle One lovely Friday we were faced with this nice yet intriguing programming puzzle. One shall write a program that compresses str

High-fidelity 3D Model Compression based on Key Spheres This repository contains the implementation of the paper: Yuanzhan Li, Yuqi Liu, Yujie Lu, Siy

A library for benchmarking, developing and deploying deep learning anomaly detection algorithms Key Features • Getting Started • Docs • License Introd

package tests docs license stats support This repository contains FEDOT - an open-source framework for automated modeling and machine learning (AutoML

A research on image classification and auto insurance claim prediction, a systematic experiments on modeling techniques and approaches

Tabular-AutoML AutoML Package for tabular datasets Tabular dataset tuning is now hassle free! Run one liner command and get best tuning and processed

Content-Aware GAN Compression [ArXiv] Paper accepted to CVPR2021. @inproceedings{liu2021content, title = {Content-Aware GAN Compression}, auth

AutoTS AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale. There are dozens of forecasting mod

"One of the holy grails of machine learning is to automate more and more of the feature engineering process." ― Pedro Domingos, A Few Useful Things to

AutoML Service Deploy automated machine learning (AutoML) as a service using Flask, for both pipeline training and pipeline serving. The framework imp

Advisor Introduction Advisor is the hyper parameters tuning system for black box optimization. It is the open-source implementation of Google Vizier w

CV Backbones including GhostNet, TinyNet, TNT (Transformer in Transformer) developed by Huawei Noah's Ark Lab. GhostNet Code TinyNet Code TNT Code Pyr

GAN Compression project | paper | videos | slides [NEW!] GAN Compression is accepted by T-PAMI! We released our T-PAMI version in the arXiv v4! [NEW!]

Image Compression GUI APP Image Compression GUI APP Python: PyQt5 Use : f5 or debug or simply run it on your ids(vscode , pycham, anaconda etc.) socia

AutoML for Text, Image, and Tabular Data AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in yo

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

TensorFlow 101: Introduction to Deep Learning I have worked all my life in Machine Learning, and I've never seen one algorithm knock over its benchmar

Scale complex AI/ML pipelines anywhere CodeFlare is a framework to simplify the integration, scaling and acceleration of complex multi-step analytics

title permalink borgmatic index.html It's your data. Keep it that way. borgmatic is simple, configuration-driven backup software for servers and works

Towards End-to-End Image Compression and Analysis with Transformers Source code of our AAAI 2022 paper "Towards End-to-End Image Compression and Analy

AutoSF The code for our paper "AutoSF: Searching Scoring Functions for Knowledge Graph Embedding" and this paper has been accepted by ICDE2020. News:

Documentation: https://mmrazor.readthedocs.io/ English | 简体中文 Introduction MMRazor is a model compression toolkit for model slimming and AutoML, which

More screencasts: installation, advanced usage What is BorgBackup? BorgBackup (short: Borg) is a deduplicating backup program. Optionally, it supports

BERT-of-Theseus Code for paper "BERT-of-Theseus: Compressing BERT by Progressive Module Replacing". BERT-of-Theseus is a new compressed BERT by progre

Luftmensch 1.4.7 (Español) | 1.4.3 (English) Version 1.4.7 (Español) released in October 2021. Version 1.4.3 (English) released in September 2021. 🏮

HyperGBM Doc | 中文 We Are Hiring！ Dear folks,we are offering challenging opportunities located in Beijing for both professionals and students who are k

Build Low Code Automated Tensorflow explainable models in just 3 lines of code.

Spatio-Temporal Entropy Model A Pytorch Reproduction of Spatio-Temporal Entropy Model (STEM) for end-to-end leaned video compression. More details can

Sklearn-genetic-opt scikit-learn models hyperparameters tuning and feature selection, using evolutionary algorithms. This is meant to be an alternativ

Snakemake workflow: name A Snakemake workflow for description Usage The usage of this workflow is described in the Snakemake Workflow Catalog. If

Pretrained Language Model This repository provides the latest pretrained language models and its related optimization techniques developed by Huawei N

Introduction Official Pytorch implementation for Deep Contextual Video Compression, NeurIPS 2021 Prerequisites Python 3.8 and conda, get Conda CUDA 11

Network Compression via Central Filter Environments The code has been tested in the following environments: Python 3.8 PyTorch 1.8.1 cuda 10.2 torchsu

CompressAI CompressAI (compress-ay) is a PyTorch library and evaluation platform for end-to-end compression research. CompressAI currently provides: c

A library for researching neural networks compression and acceleration methods.

ImageCompressionSimulation An Image compression simulator that uses Source Extractor and Monte Carlo methods to examine the post compressive effects o

geneticml This package contains a simple and lightweight genetic algorithm for optimization of any machine learning model. Installation Use pip to ins

geneticml This package contains a simple and lightweight genetic algorithm for optimization of any machine learning model. Installation Use pip to ins

Automated Learning Rate Scheduler for Large-Batch Training The official repository for Automated Learning Rate Scheduler for Large-Batch Training (8th

MEAL-V2 This is the official pytorch implementation of our paper: "MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tric

AutoLRS This is the PyTorch code implementation for the paper AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly published

ASSL This repository is for a new network pruning method (Aligned Structured Sparsity Learning, ASSL) for efficient single image super-resolution (SR)

Milano (This is a research project, not an official NVIDIA product.) Documentation https://nvidia.github.io/Milano Milano (Machine learning autotuner

beyond-preserved-accuracy Repo for EMNLP 2021 paper "Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression" How to implemen

Structured Super Lottery Tickets in BERT This repo contains our codes for the paper "Super Tickets in Pre-Trained Language Models: From Model Compress

Pretrained Language Model This repository provides the latest pretrained language models and its related optimization techniques developed by Huawei N

Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators This is our Pytorch implementation for t

Code For AC-GC: Lossy Activation Compression with Guaranteed Convergence This code is intended to be used as a supplemental material for submission to

An Open Source Project from the Data to AI Lab, at MIT MLPrimitives Pipelines and primitives for machine learning and data science. Documentation: htt

This is a collection of our NAS and Vision Transformer work.

AutoML - Neural Architecture Search This is a collection of our AutoML-NAS work iRPE (NEW): Rethinking and Improving Relative Position Encoding for Vi

Patient Knowledge Distillation for BERT Model Compression Knowledge distillation for BERT model Installation Run command below to install the environm

SwinIR: Image Restoration Using Swin Transformer This repository is the official PyTorch implementation of SwinIR: Image Restoration Using Shifted Win

AutoML for Image Semantic Segmentation Currently this repo contains the only working open-source implementation of Auto-Deeplab which, by the way out-

Fasterai fasterai is a library created to make neural network smaller and faster. It essentially relies on common compression techniques for networks

Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis. It is distributed under the MIT License.

A repository to compare many popular NAS algorithms seamlessly across three popular benchmarks (NASBench 101, 201, and 301). You can implement your ow

HAWQ: Hessian AWare Quantization HAWQ is an advanced quantization library written for PyTorch. HAWQ enables low-precision and mixed-precision uniform

Chord Recognition Demo application The demo application is written in C# with .NETCore. As of July 9, 2020, the only version available is for windows

Deep Learning with PyTorch made easy 🚀 ! Carefree? carefree-learn aims to provide CAREFREE usages for both users and developers. It also provides a c

DA2Lite (Deep Architecture to Lite) is a toolkit to compress and accelerate deep network models. ⭐ Star us on GitHub — it helps!! Frameworks & Librari

Official Website: autokeras.com AutoKeras: An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras

What is DeepHyper? DeepHyper is a software package that uses learning, optimization, and parallel computing to automate the design and development of

Simple but powerful Automated Machine Learning library for tabular data. It uses efficient in-memory SAP HANA algorithms to automate routine Data Scie

Automated Machine Learning Pipeline for tabular data. Designed for predictive maintenance applications, failure identification, failure prediction, condition monitoring, etc.

ZipFly is a zip archive generator based on zipfile.py. It was created by Buzon.io to generate very large ZIP archives for immediate sending out to clients, or for writing large ZIP archives without memory inflation.

IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization paper Requirements Python = 3.7.10 Pytorch == 1.7

PyTorch reference implementation of "End-to-end optimized image compression with competition of prior distributions" by Benoit Brummer and Christophe

IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization paper Requirements Python = 3.7.10 Pytorch == 1.7

Bert-squeeze is a repository aiming to provide code to reduce the size of Transformer-based models or decrease their latency at inference time.

Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization This repository contains the source code for the paper (link wi

FisherPruning-Pytorch An implementation of Group Fisher Pruning for Practical Network Compression based on pytorch and mmcv Main Functions Pruning f

Spiking neural network (SNN), compared with depth neural network (DNN), has faster processing speed, lower energy consumption and more biological interpretability, which is expected to approach Strong AI.

PyAF (Python Automatic Forecasting) PyAF is an Open Source Python library for Automatic Forecasting built on top of popular data science python module

A hands-off Anomaly Detection Library Table of contents What is Luminaire Quick Start Time Series Outlier Detection Workflow Anomaly Detection for Hig

AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet buil

NNI Doc | 简体中文 NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate Feature Engineering, Neural Architecture

automl-gs Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learn

Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.

AutoML - Neural Architecture Search This is a collection of our AutoML-NAS work iRPE (NEW): Rethinking and Improving Relative Position Encoding for Vi

Benchmarking Multimodal AutoML for Tabular Data with Text Fields Repository for the NeurIPS 2021 Dataset Track Submission "Benchmarking Multimodal Aut

One Proxy Device Is Enough for Hardware-Aware Neural Architecture Search paper | website One Proxy Device Is Enough for Hardware-Aware Neural Architec

TinyNeuralNetwork 简体中文 TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework, which contains features like neura

Back to Basics: Efficient Network Compression via IMP Authors: Max Zimmer, Christoph Spiegel, Sebastian Pokutta This repository contains the code to r

Training Deep Learning Models on The Edge Training on the Edge enables continuous learning from new data for deployed neural networks on memory-constr

Lossy Compression for Lossless Prediction Using: Training: This repostiory contains our implementation of the paper: Lossy Compression for Lossless Pr

Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme (NeurIPS2021) (Link) Overview Prerequisites Linu

MedMNIST Project (Website) | Dataset (Zenodo) | Paper (arXiv) | MedMNIST v1 (ISBI'21) Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bili

Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme (NeurIPS2021) Overview Prerequisites Linux Pytho

YOLOv5-Compression Update News Requirements 环境安装 pip install -r requirements.txt Evaluation metric Visdrone Model mAP mAP@50 Parameters(M) GFLOPs FPS@

GainedVAE A Pytorch Implementation of a continuously rate adjustable learned image compression framework, Gained Variational Autoencoder(GainedVAE). N

Model Search Model search (MS) is a framework that implements AutoML algorithms for model architecture search at scale. It aims to help researchers sp