An open source ML system for the end-to-end data science lifecycle
Overview: SystemDS is an open source ML system for the end-to-end data science lifecycle from data integration, cleaning,
and feature engineering, over efficient, local and distributed ML model training, to deployment and serving. To this
end, we aim to provide a stack of declarative languages with R-like syntax for (1) the different tasks of the data-science
lifecycle, and (2) users with different expertise. These high-level scripts are compiled into hybrid execution plans of
local, in-memory CPU and GPU operations, as well as distributed operations on Apache Spark. In contrast to existing
systems - that either provide homogeneous tensors or 2D Datasets - and in order to serve the entire data science lifecycle,
the underlying data model are DataTensors, i.e., tensors (multi-dimensional arrays) whose first dimension may have a
heterogeneous and nested schema.
Resource | Links |
---|---|
Quick Start | Install, Quick Start and Hello World |
Documentation: | SystemDS Documentation |
Python Documentation | Python SystemDS Documentation |
Issue Tracker | Jira Dashboard |
Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project.
To build from source visit SystemDS Install from source