Learn how to design, develop, deploy and iterate on production-grade ML applications.
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Updated
Dec 7, 2023 - Jupyter Notebook
Learn how to design, develop, deploy and iterate on production-grade ML applications.
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.
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
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).
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
A toolkit to run Ray applications on Kubernetes
A comprehensive guide to building RAG-based LLM applications for production.
Real-time PathTracing with global illumination and progressive rendering, all on top of the Three.js WebGL framework. Click here for Live Demo: https://erichlof.github.io/THREE.js-PathTracing-Renderer/Geometry_Showcase.html
A toy physically based GPU path tracer (C++/OpenGL/GLSL)
DoEKS is a tool to build, deploy and scale Data & ML Platforms on Amazon EKS
LuxCore source repository
One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)
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.
NanoRT, single header only modern ray tracing kernel.
Framework for Multi-Agent Deep Reinforcement Learning in Poker
RayLLM - LLMs on Ray
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