How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
Distributed Deep learning with Keras & Spark
Ignite is a high-level training framework for PyTorch neural networks that serves as a training engine and deep learning lifecycle manager. It provides a structured system for organizing and automating training and evaluation loops, managing data iterators and triggering event handlers at specific milestones during the model training process. The project distinguishes itself through a comprehensive suite of tools for distributed training and model evaluation. It includes utilities for synchronizing gradients and coordinating collective communication across multiple GPUs or nodes, as well as a
Hyperparameter Experiments with TensorFlow and Keras
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
The main features of maxpumperla/hyperas are: Deep Learning, Deep Learning Ecosystems, Deep Learning Frameworks, Hyperparameter Tuning, Machine Learning Frameworks, Developer Tools.
Open-source alternatives to maxpumperla/hyperas include: pytorch/ignite — Ignite is a high-level training framework for PyTorch neural networks that serves as a training engine and deep… catalyst-team/catalyst — Accelerated deep learning R&D. maxpumperla/elephas — Distributed Deep learning with Keras & Spark. deepmind/sonnet — Sonnet is a modular machine learning framework and TensorFlow library used for building, training, and managing deep… bsautermeister/tensorlight — TensorLight - A high-level framework for TensorFlow. autonomio/talos — Hyperparameter Experiments with TensorFlow and Keras.
