aimet
model-optimization
aimet | model-optimization | |
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2 | 1 | |
1,911 | 1,470 | |
2.5% | 0.8% | |
9.6 | 6.8 | |
1 day ago | 8 days ago | |
Python | Python | |
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
model-optimization
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Need Help With Pruning Model Weights in Tensorflow 2
I have been following the example shown here, and so far I've had mixed results and wanted to ask for some help because the resources I've found online have not been able to answer some of my questions (perhaps because some of these are obvious and I am just being dumb).
What are some alternatives?
tkDNN - Deep neural network library and toolkit to do high performace inference on NVIDIA jetson platforms
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
ludwig - Low-code framework for building custom LLMs, neural networks, and other AI models
qkeras - QKeras: a quantization deep learning library for Tensorflow Keras
open-lpr - Open Source and Free License Plate Recognition Software
sparseml - Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
3d-model-convert-to-gltf - Convert 3d model (STL/IGES/STEP/OBJ/FBX) to gltf and compression
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
larq - An Open-Source Library for Training Binarized Neural Networks
distiller - Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller
only_train_once - OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM