commit | 60ea6b378c6358d9370178730f2ec0648853d4df | [log] [tgz] |
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author | Sebastian Baunsgaard <baunsgaard@apache.org> | Thu May 09 23:47:15 2024 +0200 |
committer | GitHub <noreply@github.com> | Thu May 09 23:47:15 2024 +0200 |
tree | 4c5525023951c23210341a3b6969277bc6adae4d | |
parent | b1cb505dd7c9cf2f2703b143f3567eaaa8509ae7 [diff] |
[MINOR ]Update CITATION There was an error in the citation file, with an extra space in the reference name.
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.
Resource | Links |
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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