Ray is an open-source framework designed to scale and distribute Python and AI applications easily. Developed at the UC Berkeley RISELab, it provides a simple, universal API to build applications that can run across a cluster of machines. Key features and aspects of Ray include:
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Parallel and Distributed Computing: Ray simplifies the process of writing and running code that executes in parallel across multiple cores and machines. It automatically handles the distribution of data and scheduling of tasks.
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Scalability: Designed for high-performance computing, Ray can scale from a single machine to large clusters with minimal changes to the code. It's efficient for both fine-grained tasks (like functions) and large-scale applications.
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Flexible: Ray is flexible and can be used for a wide range of applications, from machine learning and AI to general-purpose Python scripting. It supports integration with popular libraries like TensorFlow, PyTorch, and scikit-learn.
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Ecosystem: Ray comes with a rich ecosystem of libraries and tools specifically designed for machine learning and AI tasks. This includes libraries for hyperparameter tuning (Ray Tune), reinforcement learning (RLlib), and distributed training (Ray SGD).
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Fault Tolerance: Ray provides fault tolerance through automatic restarting of failed tasks and nodes, making it robust for long-running and mission-critical applications.
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Easy to Use: Despite its powerful capabilities, Ray is designed to be easy to use, with a Pythonic API that feels natural for Python developers. You can parallelize existing code with minimal changes.
In summary, Ray is a powerful framework for building complex, high-performance, distributed applications and workflows, particularly in the domain of AI and machine learning, but also more broadly for any Python-based parallel computing tasks.