Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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Updated
Jul 2, 2024 - Python
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
A hyperparameter optimization framework
Like PyTorch for building ML systems. Iterable, debuggable, multi-cloud, 100% reproducible across research and production.
embedded systems control library for development, testing and installation
Distributed tensors and Machine Learning framework with GPU and MPI acceleration in Python
Python client library for Modal
The Coherence Python Client allows Python applications to act as cache clients to an Oracle Coherence cluster using gRPC as the network transport.
Distributed Reinforcement Learning accelerated by Lightning Fabric
A multi-cloud framework for big data analytics and embarrassingly parallel jobs, that provides an universal API for building parallel applications in the cloud ☁️🚀
Hazelcast Python Client
Modin: Scale your Pandas workflows by changing a single line of code
A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
Examples of programs built using Modal
Distributed Networks Institute
Documentation repo of nebula orchestration system
The worker node manager container which manages nebula nodes
The API endpoint that manages nebula orchestrator clusters
The optional reporter container which reads nebula reports from Kafka into the backend DB
A multi-cloud framework for big data analytics and embarrassingly parallel jobs, that provides an universal API for building parallel applications in the cloud ☁️🚀
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