commit | 0ad0c991904cb6d84d62ce2c2a2d6077b4b3973d | [log] [tgz] |
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author | Julia Le <julia.le@student.tugraz.at> | Sat Dec 26 10:10:28 2020 +0100 |
committer | baunsgaard <baunsgaard@tugraz.at> | Fri Feb 26 12:38:41 2021 +0100 |
tree | 875b6e011ee58e8b4b139a545ea0b4ef5ba89e34 | |
parent | 67ae9551f7bd62846e0b084c743018d4a0713ba2 [diff] |
[SYSTEMDS-2871] Python API Autogenerator DIA project WS2020/21 This commit adds a python generator that generates the builtin functions for the PythonAPI based on the function definitions inside scripts/builtin. Closes #1152 Co-authored-by: Anton Postl <anton.postl@student.tugraz.at>
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
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