commit | 0eba4dcdd3d92c91b5192e1e7d2d84cff5326068 | [log] [tgz] |
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author | Shafaq Siddiqi <shafaq.siddiqi@tugraz.at> | Sat Jan 23 23:58:03 2021 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Jan 23 23:58:03 2021 +0100 |
tree | 91158363eb13866632350be465d54784512b7b29 | |
parent | 8fc199532146b50643d30609435102c8dc6d819f [diff] |
[SYSTEMDS-2735] Builtin function gmmPredict for clustering instances Closes #1108.
Overview: SystemDS is a versatile 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.
Quick Start Install, Quick Start and Hello World
Documentation: SystemDS Documentation
Python Documentation Python SystemDS Documentation
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Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source