| commit | 0d1d011f7cf9dcf9fb2013dba523081508cb99fc | [log] [tgz] |
|---|---|---|
| author | arnabp <arnab.phani@tugraz.at> | Wed Sep 30 11:18:31 2020 +0200 |
| committer | arnabp <arnab.phani@tugraz.at> | Wed Sep 30 16:30:57 2020 +0200 |
| tree | d11faef184e58978c960a2d380fae107f892e03c | |
| parent | 77010bf4f77b7ee09595e7562bc72b043e0c1f9b [diff] |
[SYSTEMDS-2667] Update bin.xml, source.xml and README
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
