Ray.io

Ray.io is an open-source unified compute framework designed to scale AI and Python workloads. According to the vendor, it provides a distributed execution framework for tasks such as deep learning, hyperparameter tuning, model serving, reinforcement learning, and general Python applications. This versatile product is suitable for companies of all sizes, from startups to large enterprises, and caters to various professions and industries, including machine learning engineers, data scientists, AI researchers, software engineers, and gaming companies.

Key Features

Distributed deep learning: According to the vendor, Ray.io accelerates PyTorch and TensorFlow workloads using a resource-efficient and flexible distributed execution framework. It offers native multi-GPU support and an intuitive API for gradient descent. Ray.io is also framework-agnostic and compatible with popular deep learning frameworks.

Fast and easy hyperparameter tuning: The vendor claims that Ray Tune speeds up hyperparameter search workloads. It supports state-of-the-art optimization algorithms like PBT, HyperBAND, and ASHA. Ray Tune is library agnostic and compatible with popular machine learning frameworks. Additionally, it offers a built-in distributed mode with multi-GPU and multi-node support.

Fast and simple model serving: Ray Serve, as per the vendor, enables the deployment of machine learning models at scale. It is a Python-first and framework-agnostic model serving framework designed for low latency and high throughput. Ray Serve supports multi-model composition and offers native GPU support. According to the vendor, it seamlessly integrates with FastAPI for easy integration.

Scalable reinforcement learning: The vendor states that RLlib, a framework-agnostic RL library, allows for the scaling of reinforcement learning (RL). It ships with 25+ cutting-edge RL algorithms, including A3C, DQN, and PPO. RLlib supports multi-agent mode and external simulators. It can also integrate with other Ray libraries for hyperparameter search and model serving.

General Python applications: Ray Core, as claimed by the vendor, provides simple and composable primitives for building scalable and distributed systems in Python. It allows for the flexible composition of distributed applications with tasks, actors, and objects. Ray Core enables running the same Ray code on any cloud or on-premises. According to the vendor, it offers dynamic scaling with automatic scaling up or down based on compute load.

Scale data processing: According to the vendor, Ray Datasets allows for the scaling of data loading, writing, conversions, and transformations in Python. It supports basic data operations like map, filter, repartition, and shuffle on petabyte-scale data. Ray Datasets is compatible with various file formats, data sources, and distributed frameworks. It seamlessly integrates with other Ray libraries for data processing in machine learning pipelines.