The Ray Ecosystem
The Ray Ecosystem#
This page lists libraries that have integrations with Ray for distributed execution in alphabetical order. It’s easy to add your own integration to this list. Simply open a pull request with a few lines of text, see the dropdown below for more information.
Adding Your Integration
To add an integration, simply add an entry to the
projects list of our
Gallery YAML on GitHub.
- name: the integration link button text section_title: The section title for this integration description: A quick description of your library and its integration with Ray website: The URL of your website repo: The URL of your project on GitHub image: The URL of a logo of your project
Flambé is a machine learning experimentation framework built to accelerate the entire research life cycle. Flambé’s main objective is to provide a unified interface for prototyping models, running experiments containing complex pipelines, monitoring those experiments in real-time, reporting results, and deploying a final model for inference.
Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. With Ludwig, you can train a deep learning model on Ray in zero lines of code, automatically leveraging Dask on Ray for data preprocessing, Horovod on Ray for distributed training, and Ray Tune for hyperparameter optimization.
RayDP (“Spark on Ray”) enables you to easily use Spark inside a Ray program. You can use Spark to read the input data, process the data using SQL, Spark DataFrame, or Pandas (via Koalas) API, extract and transform features using Spark MLLib, and use RayDP Estimator API for distributed training on the preprocessed dataset.
Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
Sematic is an open-source ML pipelining tool written in Python. It enables users to write end-to-end pipelines that can seamlessly transition between your laptop and the cloud, with rich visualizations, traceability, reproducibility, and usability as first-class citizens. This integration enables dynamic allocation of Ray clusters within Sematic pipelines.