commit | 5aafde0a029360535ead2926200baaa9bdb1c0cf | [log] [tgz] |
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author | baunsgaard <baunsgaard@tugraz.at> | Fri Oct 30 21:17:42 2020 +0100 |
committer | baunsgaard <baunsgaard@tugraz.at> | Fri Oct 30 22:19:21 2020 +0100 |
tree | 7474a1cac77dff85b7731b74b99462082c2f3023 | |
parent | 0888631043ba57d29e682367712619815ececc9d [diff] |
[SYSTEMDS-2707] Split Federated Tests This commit merge federated tests into blocks of reasonable execution times. That should sum to ~ 30 min. Furthermore this should reduce the number of docker image pulls our tests produce, since there is an 6 hour limit of 200 images.
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