Ray Insights | Sumble

What is Ray?

Ray is a unified framework for scaling AI and Python applications. It provides simple, universal primitives for building distributed applications. Ray is commonly used for scaling machine learning workloads, reinforcement learning, model serving, and general-purpose distributed computing. It simplifies the process of parallelizing tasks and managing distributed resources.

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What other technologies are related to Ray?

Ray Competitor Technologies

Dask

Dask is a parallel computing framework that competes with Ray for distributed task execution, especially in Python.

mentioned alongside Ray in 14% (808) of relevant job posts

Spark

Spark is a distributed computing framework that competes with Ray for data processing and ML tasks.

mentioned alongside Ray in 1% (1.9k) of relevant job posts

Flyte

Flyte is a workflow orchestration platform that can be used for machine learning pipelines, similar to Ray's capabilities.

mentioned alongside Ray in 12% (73) of relevant job posts

Flink

Flink is a stream processing framework that competes with Ray for real-time data processing and ML tasks.

mentioned alongside Ray in 1% (377) of relevant job posts

Metaflow

Metaflow is a framework for building and managing data science workflows, which overlaps with some of Ray's capabilities in workflow orchestration and distributed computing.

mentioned alongside Ray in 6% (61) of relevant job posts

Ray Complementary Technologies

Horovod

Horovod is a distributed training framework, often used in conjunction with distributed computing frameworks like Ray to accelerate model training.

mentioned alongside Ray in 33% (153) of relevant job posts

DeepSpeed

DeepSpeed is a deep learning optimization library that can be integrated with distributed computing frameworks like Ray to improve training efficiency and scale.

mentioned alongside Ray in 13% (229) of relevant job posts

MLFlow

MLflow is a platform for managing the ML lifecycle, and Ray can be used as a backend to run MLflow experiments and deployments in a distributed manner.

mentioned alongside Ray in 3% (752) of relevant job posts