tag | 7d40ad43e3d9fc9c00599ece57324df0ee702dee | |
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tagger | Arnab Phani <arnabp20@apache.org> | Wed Nov 17 15:58:04 2021 +0100 |
object | 73fb4fe47e65d251ee004d816cf52b9824b32cce |
[maven-release-plugin] copy for tag 2.2.1-rc2
commit | 73fb4fe47e65d251ee004d816cf52b9824b32cce | [log] [tgz] |
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author | Arnab Phani <arnabp20@apache.org> | Wed Nov 17 15:57:32 2021 +0100 |
committer | Arnab Phani <arnabp20@apache.org> | Wed Nov 17 15:57:32 2021 +0100 |
tree | df0650eba4b546a279638eb7de21a5b791409f13 | |
parent | 2711d60f5d706275ee60f9bb26b3c72af924252b [diff] |
[maven-release-plugin] prepare release 2.2.1-rc2
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