tag | 8d88fe3098cc0e56b24fb01aec0304de76aff5cc | |
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tagger | Arnab Phani <arnabp20@apache.org> | Wed Nov 10 14:50:01 2021 +0100 |
object | 3a1681555f79b03c318eadee147be6923a4164eb |
[maven-release-plugin] copy for tag 2.2.1-rc1
commit | 3a1681555f79b03c318eadee147be6923a4164eb | [log] [tgz] |
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author | Arnab Phani <arnabp20@apache.org> | Wed Nov 10 14:49:26 2021 +0100 |
committer | Arnab Phani <arnabp20@apache.org> | Wed Nov 10 14:49:26 2021 +0100 |
tree | a81448a982a7fc9d01e00311ffac4b4abd209bc7 | |
parent | aa42054e6862a1c5b99571d2678ca8beb837709a [diff] |
[maven-release-plugin] prepare release 2.2.1-rc1
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