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
Aug 1, 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.
Open source project for data preparation of LLM application builders
A next-generation dynamic and high-performance language for AI and IOT with natural born distributed computing ability.
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Like PyTorch for ML infra. Iterable, debuggable, multi-cloud, 100% reproducible across research and production.
Octree/Quadtree/N-dimensional linear tree
A toolkit to run Ray applications on Kubernetes
Malicious URLs identified by scanning various public URL sources using the Google Safe Browsing API (over 6 billion URLs scanned daily)
pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).
DoEKS is a tool to build, deploy and scale Data & ML Platforms on Amazon EKS
an open-sourced highly automated machine learning Python framework for data-driven geochemistry discovery
Learn how to design, develop, deploy and iterate on production-grade ML applications.
TorchX is a universal job launcher for PyTorch applications. TorchX is designed to have fast iteration time for training/research and support for E2E production ML pipelines when you're ready.
An open source library for connecting AnyLogic models with Reinforcement Learning frameworks through OpenAI Gymnasium
A scalable RAG-based Wikipedia Chat Assistant that leverages the Llama-2-7b-chat LLM, inferenced using KServe
Serving Inside Pytorch With Multi-threads
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