Python pruning

Open-source Python projects categorized as pruning

Top 17 Python pruning Projects

  • deepsparse

    Sparsity-aware deep learning inference runtime for CPUs

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

    Interesting company. Yannic Kilcher interviewed Nir Shavit last year and they went into some depth: DeepSparse is on GitHub:

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  • Torch-Pruning

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

  • neural-compressor

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

  • sparseml

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

  • aimet

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

  • model-optimization

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

  • mmrazor

    OpenMMLab Model Compression Toolbox and Benchmark.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • nncf

    Neural Network Compression Framework for enhanced OpenVINO™ inference

    Project mention: FLaNK Stack Weekly 06 Nov 2023 | | 2023-11-06
  • Sparsebit

    A model compression and acceleration toolbox based on pytorch.

  • sparsify

    ML model optimization product to accelerate inference.

  • only_train_once

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

  • wyng-backup

    Fast backups for logical volumes & disk images

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

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

  • UPop

    [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 | | 2023-07-31
  • delve

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

  • OWL

    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 | | 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

  • thesis

    Master's thesis, Uni Passau (by harshildarji)

  • Pi-SqueezeDet

    Pruning SqueezeDet for inference on Raspberry PI

  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

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NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020).

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  • A note from our sponsor - Scout Monitoring | 21 Jul 2024
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What are some of the best open-source pruning projects in Python? This list will help you:

Project Stars
1 deepsparse 2,945
2 Torch-Pruning 2,451
3 neural-compressor 2,082
4 sparseml 2,015
5 aimet 2,023
6 model-optimization 1,477
7 mmrazor 1,416
8 nncf 859
9 Sparsebit 324
10 sparsify 317
11 only_train_once 277
12 wyng-backup 242
13 UPop 91
14 delve 78
15 OWL 40
16 thesis 15
17 Pi-SqueezeDet 2

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