commit | 3a9baf48427c8ad6f51a233feeff03a407175f64 | [log] [tgz] |
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author | David Kerschbaumer <david.kerschbaumer@student.tugraz.at> | Sat Jan 09 00:08:15 2021 +0100 |
committer | Shafaq Siddiqi <shafaq.siddiqi@tugraz.at> | Sat Jan 09 01:30:29 2021 +0100 |
tree | b5d004d4a82db27671559035296ed207c36a8c1b | |
parent | 12cdc89a31d1b33728bf074005aa426f1beac11d [diff] |
[SYSTEMDS-2789] Disguised Missing Values Detection Co-authored-by: Patrick Lovric <patrick.lovric@student.tugraz.at> Co-authored-by: Valentin Edelsbrunner <v.edelsbrunner@student.tugraz.at> DIA project WS2020/21. Closes #1144. Date: Sat Jan 9 00:05:47 2021 +0100
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