tag | 9dfc71fab049ebb14b229441efeb78766fdcfa23 | |
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tagger | Janardhan Pulivarthi <janardhan@apache.org> | Wed Jun 15 16:31:29 2022 +0000 |
object | ab5959991e33cec2a1f76ed3356a6e8b2f7a08a3 |
[maven-release-plugin] copy for tag 3.0.0-rc2
commit | ab5959991e33cec2a1f76ed3356a6e8b2f7a08a3 | [log] [tgz] |
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author | Janardhan Pulivarthi <janardhan@apache.org> | Wed Jun 15 16:31:09 2022 +0000 |
committer | Janardhan Pulivarthi <janardhan@apache.org> | Wed Jun 15 16:31:09 2022 +0000 |
tree | c48fbea8724f1a8d3c2688ac6ab186a8bdc41fac | |
parent | a5eb99d1801275b526c7cd2740c642922b464b01 [diff] |
[maven-release-plugin] prepare release 3.0.0-rc2
Overview: SystemDS is an open source ML 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