commit | 516822f957710fe1be742031d05dbc5f488a473b | [log] [tgz] |
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author | e-strauss <92718421+e-strauss@users.noreply.github.com> | Wed Nov 08 15:33:41 2023 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Mar 16 17:28:58 2024 +0100 |
tree | 946fcba55d7046c08ed151a8398a5b3806ab2c7e | |
parent | 69ae5719883ae47ab2b6da704ca255737533655d [diff] |
[SYSTEMDS-3648] Extended ops for dedup-blocks, LSTM CP instruction Closes #1994.
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