commit | f2cf133a5c935a356d2d0d1bb0b0035e13a840ed | [log] [tgz] |
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author | Janardhan Pulivarthi <j143@protonmail.com> | Mon Oct 25 17:01:21 2021 +0530 |
committer | GitHub <noreply@github.com> | Mon Oct 25 17:01:21 2021 +0530 |
tree | fe5daf9c952d89b2e1f2faf9c7ab6ebdf6a07200 | |
parent | f93cd5d4330ed55fffd33a6c8edf392f9bbf897d [diff] |
[MINOR] Publish to nexus repo during dry run (#1420) * Publish to repository.apache.org during dry run * fix temp repo folder name for release run
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