tag | 56af8ceaa4d4018c25158aeef60f6e3fcf069391 | |
---|---|---|
tagger | Arnab Phani <arnabp20@apache.org> | Fri Nov 26 11:59:58 2021 +0100 |
object | ada4706f29b57e085c0e83911b59373f6a7f1eb8 |
[maven-release-plugin] copy for tag 2.2.1-rc3
commit | ada4706f29b57e085c0e83911b59373f6a7f1eb8 | [log] [tgz] |
---|---|---|
author | Arnab Phani <arnabp20@apache.org> | Fri Nov 26 11:59:17 2021 +0100 |
committer | Arnab Phani <arnabp20@apache.org> | Fri Nov 26 11:59:17 2021 +0100 |
tree | b0a0f92d5caf72f0c873854b4fea155933f5ee40 | |
parent | d11b642fcf8e027569ce6b5916df4a0d5ecf1be2 [diff] |
[maven-release-plugin] prepare release 2.2.1-rc3
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