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Awesome GitHub RepositoriesTraining Configuration Management
Systems for defining and managing the parameters, hyperparameters, and iteration schedules of a training run, distinct from execution engines by focusing on the setup phase.
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Awesome Training Configuration Management GitHub Repositories
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- keras-team/keras
Keras is a high-level deep learning framework designed for constructing and training neural networks through the composition of modular, functional layers. It serves as a comprehensive modeling toolkit that provides standardized procedures for defining, evaluating, and deploying complex architectures. By utilizing a di
Pythondata-sciencedeep-learningjax - ultralytics/yolov5
YOLOv5 is a comprehensive computer vision framework designed for end-to-end deep learning, specializing in real-time object detection, image classification, and instance segmentation. It provides a unified toolkit that manages the entire lifecycle of a model, from initial dataset configuration and hyperparameter tuning
Pythoncoremldeep-learningios - deepfakes/faceswap
Faceswap is a comprehensive framework for automated media manipulation and neural face synthesis. It provides a modular pipeline that manages the entire lifecycle of facial feature extraction, deep learning model training, and image conversion. By coordinating complex computer vision workflows, the system enables users
Pythondeep-face-swapdeep-learningdeep-neural-networks - unslothai/unsloth
Unsloth is a high-performance training and inference platform designed to optimize the lifecycle of large language and multimodal models. It provides a comprehensive engine for fine-tuning, executing, and managing models locally, with a focus on reducing memory consumption and increasing compute speed on consumer-grade
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Explore sub-tags
- Training Configurations1 sub-tagSettings and parameters used to manage and optimize the training process of machine learning models.
- Training Epoch ConfigurationsMethods for determining the optimal number of training passes through a dataset based on performance monitoring.
- Training Hyperparameter ConfigurationsTools for adjusting specific model training parameters like batch size and learning rate to optimize processing performance.