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.
-
Updated
Jul 17, 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.
Like PyTorch for ML infra. Iterable, debuggable, multi-cloud, 100% reproducible across research and production.
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.
Open source project for data preparation of LLM application builders
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).
an open-sourced highly automated machine learning Python framework for data-driven geochemistry discovery
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Comyx is an optimized and modular Python library for simulating wireless communication systems
Additional stoppers for ray tune
Testing ray tune with slurm batch submission and optuna and wandb
CamTools: Camera Tools for Computer Vision
A basic Ray Tracer that exploits numpy arrays and functions to work reasonably fast.
Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.
Python tools for the Acoustic Toolbox
Add a description, image, and links to the ray topic page so that developers can more easily learn about it.
To associate your repository with the ray topic, visit your repo's landing page and select "manage topics."