tag | a55ee06d38e2620433ecd39e9343c1d8e0cfb9d4 | |
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tagger | Arnab Phani <arnabp20@apache.org> | Thu Jun 17 12:02:27 2021 +0200 |
object | 2df424a68c35104a02c9feac75e271dde8de31e2 |
[maven-release-plugin] copy for tag 2.1.0-rc2
commit | 2df424a68c35104a02c9feac75e271dde8de31e2 | [log] [tgz] |
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author | Arnab Phani <arnabp20@apache.org> | Thu Jun 17 12:01:44 2021 +0200 |
committer | Arnab Phani <arnabp20@apache.org> | Thu Jun 17 12:01:44 2021 +0200 |
tree | 45d434f20c6a8d4cd889229fe0a7f3eb44707ea4 | |
parent | 7dcf6fe424e80901852785a4a0bf7065c15973bb [diff] |
[maven-release-plugin] prepare release 2.1.0-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