commit | c36a8369e5f394a362ac69eb90e3ab62b50c8db9 | [log] [tgz] |
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author | ReneEnjilian <enjilianrene@gmail.com> | Sun Mar 17 18:37:21 2024 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sun Mar 17 19:14:23 2024 +0100 |
tree | 5c0eff25594d57a23c18cd27dc6fbc94a1f29888 | |
parent | 1f2a1be1affcf36cc4dcd3476187186a9fe9256e [diff] |
[SYSTEMDS-3666] New simplification rewrite not-over-comparisons Closes #1988.
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