openvino
neural-compressor
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| openvino | neural-compressor | |
|---|---|---|
| 17 | 3 | |
| 5,911 | 1,964 | |
| 6.6% | 6.5% | |
| 10.0 | 9.8 | |
| 4 days ago | about 7 hours ago | |
| C++ | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
openvino
- FLaNK Stack 05 Feb 2024
- QUIK is a method for quantizing LLM post-training weights to 4 bit precision
- Intel OpenVINO 2023.1.0 released
- Intel OpenVINO 2023.1.0 released, open-source toolkit for optimizing and deploying AI inference
- OpenVINO 2023.1.0 released
- [N] Intel OpenVINO 2023.1.0 released, open-source toolkit for optimizing and deploying AI inference
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Powering Anomaly Detection for Industry 4.0
Anomalib is an open-source deep learning library developed by Intel that makes it easy to benchmark different anomaly detection algorithms on both public and custom datasets, all by simply modifying a config file. As the largest public collection of anomaly detection algorithms and datasets, it has a strong focus on image-based anomaly detection. It’s a comprehensive, end-to-end solution that includes cutting-edge algorithms, relevant evaluation methods, prediction visualizations, hyperparameter optimization, and inference deployment code with Intel’s OpenVINO Toolkit.
neural-compressor
- Intel Textual Inversion Training on Hugging Face
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An open-source library for optimizing deep learning inference. (1) You select the target optimization, (2) nebullvm searches for the best optimization techniques for your model-hardware configuration, and then (3) serves an optimized model that runs much faster in inference
Open-source projects leveraged by nebullvm include OpenVINO, TensorRT, Intel Neural Compressor, SparseML and DeepSparse, Apache TVM, ONNX Runtime, TFlite and XLA. A huge thank you to the open-source community for developing and maintaining these amazing projects.
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Meet Intel® Neural Compressor: An Open-Source Python Library for Model Compression that Reduces the Model Size and Increases the Speed of Deep Learning Inference for Deployment on CPUs or GPUs
Continue reading | The Github repo for the library can be accessed here.
What are some alternatives?
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
tflite-micro - Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors).
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
mmrazor - OpenMMLab Model Compression Toolbox and Benchmark.
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
nebuly - The user analytics platform for LLMs
stable-diffusion - Go to lstein/stable-diffusion for all the best stuff and a stable release. This repository is my testing ground and it's very likely that I've done something that will break it.
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
Lion - Code for "Lion: Adversarial Distillation of Proprietary Large Language Models (EMNLP 2023)"