commit | af68796b5bf5093e8bdf55012486d2858de9e2aa | [log] [tgz] |
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author | Frederic Zoepffel <f.zoepffel@gmail.com> | Mon Apr 22 20:32:12 2024 +0200 |
committer | Matthias Boehm <mboehm7@gmail.com> | Mon Apr 22 20:44:46 2024 +0200 |
tree | 268e1383a1abbb9010c51104beff5fa72776b908 | |
parent | e671cc30bc6852090126f9f57458707210398f1c [diff] |
[SYSTEMDS-3639] Added new SliceLine tests, prep for incSliceLine Closes #2021.
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