commit | 178bdd7ae292323b7a8d57d5ce2612437958800e | [log] [tgz] |
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author | Shafaq Siddiqi <shafaq.siddiqi@tugraz.at> | Fri Jan 15 21:03:08 2021 +0100 |
committer | Shafaq Siddiqi <shafaq.siddiqi@tugraz.at> | Fri Jan 15 23:12:12 2021 +0100 |
tree | ffd65d241961000cdfbba38f39d2ecbb63cd6aa0 | |
parent | 8e5c5f3dc52e5c99bdea3bce39eece71ce91a6c8 [diff] |
[MINOR] Fixing failed test by reverting the changes in BinaryOp file.
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