Top 20 pruning Open-Source Projects

• deepsparse

  21 2,873 9.5 Python

  Sparsity-aware deep learning inference runtime for CPUs

  

Project mention: Fast Llama 2 on CPUs with Sparse Fine-Tuning and DeepSparse | news.ycombinator.com | 2023-11-23

Interesting company. Yannic Kilcher interviewed Nir Shavit last year and they went into some depth: https://www.youtube.com/watch?v=0PAiQ1jTN5k DeepSparse is on GitHub: https://github.com/neuralmagic/deepsparse

• Torch-Pruning

  2 2,307 9.4 Python

  [CVPR 2023] Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs

Project mention: [P] Help: I want to compress EfficientnetV2 using pruning. | /r/MachineLearning | 2023-06-28

I also tried structured pruning from https://github.com/VainF/Torch-Pruning, as they report EfficientNetV2 to be "prunable", but got much worse results. However, the advantage of this approach is that it keeps the model dense, and you can get a real speed-up with common GPUs, while unstructured pruning sparsifies the model and you need hardware that can exploit such sparsity.

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• sparseml

  12 1,976 9.6 Python

  Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

  

• neural-compressor

  3 1,964 9.8 Python

  SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime

  

• aimet

  2 1,908 9.6 Python

  AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

  

• model-optimization

  1 1,470 6.8 Python

  A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

  

• mmrazor

  4 1,365 2.8 Python

  OpenMMLab Model Compression Toolbox and Benchmark.

  

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• autorestic

  12 1,099 7.3 Go

  Config driven, easy backup cli for restic.

Project mention: Duplicity | news.ycombinator.com | 2024-01-24

I really like restic, and am personally happy to use it via the command line. It's very fast and efficient! However, I do wish there was better tooling / wrappers around it. For example, Pika Backup is a popular UI for Borg of which no equivalent exists for Restic. I'd love to be able to set something simple up on my partner's Macbook.

For my own purposes, I've been using a script I found on Github[0] for a while, but it only really supports Backblaze B2 AFAIK.[1]

I've been meaning to try autorestic[2] and resticprofile[3] as they are potentially more flexible than the script I'm currently using, and prestic[4] looks intriguing for my partner's use, but seems to have very few users. And the fact that there are so many competing tools makes it difficult to land on one.

[0] https://github.com/erikw/restic-automatic-backup-scheduler

[1] https://github.com/erikw/restic-automatic-backup-scheduler/i...

[2] https://github.com/cupcakearmy/autorestic

[3] https://github.com/creativeprojects/resticprofile

[4] https://github.com/ducalex/prestic

• nncf

  2 819 9.7 Python

  Neural Network Compression Framework for enhanced OpenVINO™ inference

  

Project mention: FLaNK Stack Weekly 06 Nov 2023 | dev.to | 2023-11-06

• optimum-intel

  1 324 9.6 Jupyter Notebook

  🤗 Optimum Intel: Accelerate inference with Intel optimization tools

  

• Sparsebit

  1 320 5.9 Python

  A model compression and acceleration toolbox based on pytorch.

  

• sparsify

  1 315 7.7 Python

  ML model optimization product to accelerate inference.

  

• only_train_once

  1 261 8.9 Python

  OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM

• wyng-backup

  5 237 8.1 Python

  Fast Time Machine-like backups for logical volumes & disk images

Project mention: My SSD suddenly died. I only lost 10 minutes of data, thanks to ZFS | news.ycombinator.com | 2023-08-22

For people who don't want to use ZFS but are okay with LVM: wyng-backup (formerly sparsebak)

https://github.com/tasket/wyng-backup

• UPop

  1 83 8.4 Python

  [ICML 2023] UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers.

Project mention: Show HN: Compress vision-language and unimodal AI models by structured pruning | news.ycombinator.com | 2023-07-31

• delve

  1 77 4.0 Python

  PyTorch model training and layer saturation monitor (by delve-team)

  

• Generalizing-Lottery-Tickets

  1 50 0.0 Jupyter Notebook

  This repository contains code to replicate the experiments given in NeurIPS 2019 paper "One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers"

• OWL

  1 39 7.3 Python

  Official Pytorch Implementation of "Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity" (by luuyin)

Project mention: Outlier Weighed Layerwise Sparsity: A Missing Secret Sauce for Pruning LLMs | news.ycombinator.com | 2023-10-10

Paper abstract: Large Language Models (LLMs), renowned for their remarkable performance across diverse domains, present a challenge due to their colossal model size when it comes to practical deployment. In response to this challenge, efforts have been directed toward the application of traditional network pruning techniques to LLMs, uncovering a massive number of parameters can be pruned in one-shot without hurting performance. Building upon insights gained from pre-LLM models, particularly BERT-level language models, prevailing LLM pruning strategies have consistently adhered to the practice of uniformly pruning all layers at equivalent sparsity levels, resulting in robust performance. However, this observation stands in contrast to the prevailing trends observed in the field of vision models, where non-uniform layerwise sparsity typically yields substantially improved results. To elucidate the underlying reasons for this disparity, we conduct a comprehensive analysis of the distribution of token features within LLMs. In doing so, we discover a strong correlation with the emergence of outliers, defined as features exhibiting significantly greater magnitudes compared to their counterparts in feature dimen- sions. Inspired by this finding, we introduce a novel LLM pruning methodology that incorporates a tailored set of non-uniform layerwise sparsity ratios specif- ically designed for LLM pruning, termed as Outlier Weighed Layerwise sparsity (OWL). The sparsity ratio of OWL is directly proportional to the outlier ratio observed within each layer, facilitating a more effective alignment between layer- wise weight sparsity and outlier ratios. Our empirical evaluation, conducted across the LLaMA-V1 family and OPT, spanning various benchmarks, demonstrates the distinct advantages offered by OWL over previous methods. For instance, our approach exhibits a remarkable performance gain, surpassing the state-of-the-art Wanda and SparseGPT by 61.22 and 6.80 perplexity at a high sparsity level of 70%, respectively. Codes are available at https://github.com/luuyin/OWL.

• thesis

  3 15 0.0 Python

  Master's thesis, Uni Passau (by harshildarji)

• Pi-SqueezeDet

  1 2 0.0 Python

  Pruning SqueezeDet for inference on Raspberry PI

• SaaSHub

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pruning related posts

• [P] Help: I want to compress EfficientnetV2 using pruning.

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• Intel Textual Inversion Training on Hugging Face

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• [R] New sparsity research (oBERT) enabled 175X increase in CPU performance for MLPerf submission

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• [R] BERT-Large: Prune Once for DistilBERT Inference Performance

  2 projects | /r/MachineLearning | 16 Jul 2022

• [R] How well do sparse ImageNet models transfer? Prune once and deploy anywhere for inference performance speedups! (arxiv link in comments)

  2 projects | /r/MachineLearning | 26 Jun 2022
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