elephas 5.2.1

Horovod is a powerful distributed training framework for Python that allows you to train deep learning models across multiple GPUs and servers quickly and efficiently. It falls under the category of distributed computing libraries. Built on top of TensorFlow, PyTorch, and other popular deep learning frameworks, Horovod simplifies the process of scaling up your model training by handling the complexities of distributed training under the hood.

Haiku API Bindings

ktrain is a wrapper for TensorFlow Keras that makes deep learning and AI more accessible and easier to apply

Model summary in PyTorch similar to model.summary() in Keras

Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Currently we focus on the capabilities needed for inferencing (scoring).

Unity Machine Learning Agents Interface

Unity Machine Learning Agents

Live training loss plot in Jupyter Notebook for Keras, PyTorch and others.

A Hyperparameter Tuning Library for Keras