aimet
distiller
aimet | distiller | |
---|---|---|
2 | 1 | |
2,197 | 4,096 | |
1.6% | - | |
9.8 | 10.0 | |
3 days ago | about 2 years ago | |
Python | Jupyter Notebook | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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aimet
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I was looking for some great quantization open-source libraries that could actually be applied in production (both edge or cloud CPU/GPU). Do you know if I am missing any good libraries?
Qualcomm AIMET | Advanced quantization and compression techniques for trained neural network models
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Model/Tool to use on Jetson for efficient Quantization/Pruning
Qualcomm AIMET may help you
distiller
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Avoid vver fitting in iterative pruning [D]
Code for https://arxiv.org/abs/1510.00149 found: https://github.com/NervanaSystems/distiller
What are some alternatives?
tkDNN - Deep neural network library and toolkit to do high performace inference on NVIDIA jetson platforms
Lottery-Ticket-Hypothesis-for-DNNs - This repo aims to provide an easy-to-use interface for searching the lottery ticket of a DNN structure.
model-optimization - A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
Generalizing-Lottery-Tickets - 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"
qkeras - QKeras: a quantization deep learning library for Tensorflow Keras
deconstructing-lottery-tickets
ludwig - Low-code framework for building custom LLMs, neural networks, and other AI models
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
open-lpr - Open Source and Free License Plate Recognition Software
dl-colab-notebooks - Try out deep learning models online on Google Colab
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
optimum-intel - 🤗 Optimum Intel: Accelerate inference with Intel optimization tools