pittisl/ElasticTrainer
Code for paper "ElasticTrainer: Speeding Up On-Device Training with Runtime Elastic Tensor Selection" (MobiSys'23)
This project helps machine learning practitioners speed up the training of neural networks directly on edge devices like Raspberry Pi or Nvidia Jetson TX2. It takes your existing TensorFlow model and dataset, and outputs a trained model that runs significantly faster on-device, potentially doubling the speed, without losing accuracy. It's designed for embedded systems developers or ML engineers deploying models to resource-constrained hardware.
No commits in the last 6 months.
Use this if you need to train image classification models faster on resource-limited embedded devices while maintaining model accuracy.
Not ideal if you are working with PyTorch models or primarily training large language models (LLMs), although a related project, GreenTrainer, addresses LLM fine-tuning.
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Language
Python
License
MIT
Category
Last pushed
Nov 01, 2023
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