Renate
Model updater
A tool for automatically updating machine learning models with new data without starting from scratch
Library for automatic retraining and continual learning
282 stars
12 watching
6 forks
Language: Python
last commit: about 1 year ago awscontinual-learninghyperparameter-tuninghyperparameters-optimizationmachine-learningmachine-learning-algorithmsneuralneural-networkpytorchpytorch-lightningsagemaker
Related projects:
| Repository | Description | Stars |
|---|---|---|
| | Automated notification system for machine learning model training | 343 |
| | Automatically patches vulnerable EC2 instances after receiving an Inspector assessment notification | 58 |
| | Automates model building and deployment process by optimizing hyperparameters and compressing models for edge computing. | 200 |
| | Replication of Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks in PyTorch for reinforcement learning tasks | 830 |
| | An ORM plugin that allows simple patching of models and skips updating if no values have changed. | 0 |
| | Provides an optimized approach to secure machine learning model updates in distributed settings | 80 |
| | A framework to automate the update and maintenance of biomedical text mining tools | 41 |
| | A lightweight wrapper around PyTorch to prevent CUDA out-of-memory errors and optimize model execution | 1,823 |
| | An open-source framework for adapting representation models to various tasks and industries | 1,743 |
| | An implementation of DeepMind's Relational Recurrent Neural Networks (Santoro et al. 2018) in PyTorch for word language modeling | 245 |
| | An exploratory tool for analyzing and understanding machine learning models | 14 |
| | An R package and workshop materials for explaining machine learning models using explainable AI techniques | 52 |
| | A collection of Ansible modules and plugins for automating AWS instance management | 0 |
| | An algorithm for training self-generalizing gradient boosting machines with automatic hyperparameter optimization and improved performance on various machine learning tasks | 321 |
| | Provides guidance on fine-tuning pre-trained models for image classification tasks using PyTorch. | 279 |