commit | 76a79629db740572a0f7af116fce5afddc9f4f28 | [log] [tgz] |
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author | sebwrede <swrede@know-center.at> | Wed Oct 14 13:10:25 2020 +0200 |
committer | arnabp <arnab.phani@tugraz.at> | Wed Oct 14 13:10:25 2020 +0200 |
tree | a05df4e6c3abfa64e53b6379b7f3a5e910c3c602 | |
parent | 8be71ec01a80fa98b6e0e088a4476f65aabe7707 [diff] |
Move Privacy Post-Processing To Be Before Lineage Cache
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