Couler is a system designed for unified machine learning workflow optimization in the cloud. Couler endeavors to provide a unified interface for constructing and optimizing workflows across various workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow. Couler enhances workflow efficiency through features like Autonomous Workflow Construction, Automatic Artifact Caching Mechanisms, Big Workflow Auto Parallelism Optimization, and Automatic Hyperparameters Tuning.
Features
- Couler is included in CNCF Cloud Native Landscape and LF AI Landscape
- Many workflow engines exist nowadays, e.g. Argo Workflows, Tekton Pipelines, and Apache Airflow
- Couler provides a unified programming interface for workflow definition, ensuring independence from the workflow engine and compatibility with various workflow engines such as Argo Workflows, Airflow, and Tekton
- Couler integrates LLMs in unified programming code generation. By leveraging LLMs, Couler facilitates the generation of unified programming code using NL descriptions
- Couler introduces the Intermediate Representative (IR) to depict the workflow Directed Acyclic Graph (DAG), optimizing extensive workflow computations by dividing a large workflow into smaller ones for auto-parallelism optimization
- Couler also implements dynamic caching of artifacts, which are the outputs of jobs in the workflow, to minimize redundant computations and ensure fault tolerance
- Documentation available
Project Samples
Categories
Machine LearningLicense
Apache License V2.0Follow Couler
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