commit | fe0efcc751a2f235850d55bfb2152e12900a622c | [log] [tgz] |
---|---|---|
author | Janardhan Pulivarthi <j143@protonmail.com> | Mon Feb 12 18:59:22 2024 +0530 |
committer | GitHub <noreply@github.com> | Mon Feb 12 18:59:22 2024 +0530 |
tree | 1dd8117a489dbb76f1150e2449c2f45dfa093e92 | |
parent | 60a2acbf4bcfcb1e41a1c12be05504ffafa02720 [diff] |
[MINOR] copy shaded jar instead of moving (#1996) During the `mvn deploy` operation, maven is searching for shaded jar gpg: can't open 'systemds/target/systemds-3.2.0-shaded.jar' as a workaround, keep the shaded jar too in the folder.
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