commit | 998dd43b1ea32cda7d35f03edf7f4731a2d1fc06 | [log] [tgz] |
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author | Arnab Phani <phaniarnab@gmail.com> | Thu Apr 27 23:22:47 2023 +0200 |
committer | Arnab Phani <phaniarnab@gmail.com> | Fri Apr 28 00:02:15 2023 +0200 |
tree | ebb3c0f5b7a41875255d4b46ed211b7586b247c6 | |
parent | a30b5b02747d98b1f513129d73a65a466a20261d [diff] |
[MINOR] Add coalesce after matrix indexing in Spark Closes #1816
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