commit | 0d6236454bd0757e15218854327574a48583177c | [log] [tgz] |
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author | Sebastian Baunsgaard <baunsgaard@apache.org> | Tue Mar 05 15:40:16 2024 +0100 |
committer | Sebastian Baunsgaard <baunsgaard@apache.org> | Tue Mar 05 15:40:16 2024 +0100 |
tree | f5ad765c829bd6ad716c10b80258b01a30a59961 | |
parent | 42ed9e795135409bb8032de7ce0d885831f3467f [diff] |
[MINOR] Add warnings logging for hadoop and systemds for releases
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