commit | 23533facd248e0b8b60a5dbbeefc3fc5098acf4b | [log] [tgz] |
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author | baunsgaard <baunsgaard@tugraz.at> | Thu Oct 29 11:38:00 2020 +0100 |
committer | baunsgaard <baunsgaard@tugraz.at> | Thu Oct 29 11:38:00 2020 +0100 |
tree | 4f2848d4745568a6e22cfb38e68d4fd3e668633c | |
parent | dcdb21eed45b3ad6655ebee475009b1888d81345 [diff] |
Merged Tests
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