commit | d439f0e321fd84c37ed4f26bea68552ee907c6b1 | [log] [tgz] |
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author | baunsgaard <baunsgaard@tugraz.at> | Sat Dec 12 14:29:36 2020 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Dec 12 14:43:17 2020 +0100 |
tree | f88bc7a7d26ce91f127446ff969e136ace000f68 | |
parent | 67e150d3d68de5dd63f4255112ce2161fcd7873f [diff] |
[SYSTEMDS-2756] Improved scale built-in function Closes #1123.
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