| commit | d1d25121687bf0a9d0d1d2437943b9552e36cddd | [log] [tgz] |
|---|---|---|
| author | Janardhan Pulivarthi <j143@protonmail.com> | Fri Jun 10 20:48:07 2022 +0530 |
| committer | GitHub <noreply@github.com> | Fri Jun 10 20:48:07 2022 +0530 |
| tree | 050cfbee19e7c4d20b5652bf7da2294a5517ff75 | |
| parent | d7d77dfc0b8ec7164bb3d9db30181aedd185fe14 [diff] |
[MINOR] Prepare for next development iteration 2.3.0
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
