commit | 94e557ee3e087c307367465302a6fc498b08e3b6 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Sat Jan 16 22:21:00 2021 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Jan 16 22:21:00 2021 +0100 |
tree | a5bf1aa8e6aec859106b9cd729f5321fbcc367ac | |
parent | 6165b509c3b3fcfc0b0690d52d1afe6e13d3fc17 [diff] |
[MINOR] Fix mdedup built-in function test (marked as thread-unsafe)
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