commit | 67e150d3d68de5dd63f4255112ce2161fcd7873f | [log] [tgz] |
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author | j143 <j143@protonmail.com> | Tue Dec 08 18:02:26 2020 +0530 |
committer | GitHub <noreply@github.com> | Tue Dec 08 18:02:26 2020 +0530 |
tree | a5ebad52e09eea9b9a7fb05bc4cb0db5676b1629 | |
parent | 8f179036aa2306e0e090a41e7c8224125d3597d2 [diff] |
[MINOR][DOC] Update algolia index name Refer https://github.com/algolia/docsearch-configs/pull/2943
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