commit | 6e938bdbc6071bf6919640356428f6c05fd25033 | [log] [tgz] |
---|---|---|
author | Arnab Phani <arnabp20@apache.org> | Mon Oct 25 18:21:18 2021 +0200 |
committer | Arnab Phani <arnabp20@apache.org> | Mon Oct 25 18:21:18 2021 +0200 |
tree | b1eb53de11286faec805f52f96eeb92865e4ff34 | |
parent | f2cf133a5c935a356d2d0d1bb0b0035e13a840ed [diff] |
[maven-release-plugin] prepare release 2.2.0-rc1
Overview: SystemDS is a versatile 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.
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