only_train_once
Torch-Pruning
| only_train_once | Torch-Pruning | |
|---|---|---|
| 1 | 2 | |
| 265 | 2,356 | |
| - | - | |
| 8.9 | 9.4 | |
| 14 days ago | 2 days ago | |
| Python | Python | |
| MIT License | MIT License |
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only_train_once
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OTOv2 Automatic One-Shot General DNN Training and Compression Framework.
Github: https://github.com/tianyic/only_train_once
Torch-Pruning
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[P] Help: I want to compress EfficientnetV2 using pruning.
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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[D] What is your go-to implementation for structured pruning?
I'm currently using this repo and I find it very intuitive: https://github.com/VainF/Torch-Pruning. Not sure if it fits your needs, but check it out :)
What are some alternatives?
archai - Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
SadTalker - [CVPR 2023] SadTalker：Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation
delve - PyTorch model training and layer saturation monitor
efficient-gnns - Code and resources on scalable and efficient Graph Neural Networks
model-optimization - A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
ds2 - Easiest way to use AI models without coding (Web UI & API support)
UPop - [ICML 2023] UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers.