tag | 3e18a1b5ad9ad6da4237b9c473c36df234965009 | |
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tagger | Janardhan Pulivarthi <janardhan@apache.org> | Fri Jun 10 16:43:35 2022 +0000 |
object | ad1c5fad5bca20e2487d095fc912f97fe1326851 |
[maven-release-plugin] copy for tag 2.2.2-rc1
commit | ad1c5fad5bca20e2487d095fc912f97fe1326851 | [log] [tgz] |
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author | Janardhan Pulivarthi <janardhan@apache.org> | Fri Jun 10 16:43:02 2022 +0000 |
committer | Janardhan Pulivarthi <janardhan@apache.org> | Fri Jun 10 16:43:02 2022 +0000 |
tree | c1dada8d0a8f871ef4273d1369e98700a7331093 | |
parent | d1d25121687bf0a9d0d1d2437943b9552e36cddd [diff] |
[maven-release-plugin] prepare release 2.2.2-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