| commit | 47c52f185bb193a8d20a6a04e00a16e1881378de | [log] [tgz] |
|---|---|---|
| author | baunsgaard <baunsgaard@tugraz.at> | Mon Oct 19 14:13:10 2020 +0200 |
| committer | baunsgaard <baunsgaard@tugraz.at> | Mon Oct 19 14:13:10 2020 +0200 |
| tree | 307f1272985eb57afa40fce14838efd0e0dccdbb | |
| parent | 3560a1e0a632e3c1f712d68392342648a787003c [diff] |
[MINOR] Remove debug printing in python 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
