| commit | aed82e65dea3d47ba72e7af9cc64ce0747414399 | [log] [tgz] |
|---|---|---|
| author | baunsgaard <baunsgaard@tugraz.at> | Thu Sep 17 12:44:04 2020 +0200 |
| committer | baunsgaard <baunsgaard@tugraz.at> | Thu Sep 17 12:51:35 2020 +0200 |
| tree | a7d8f07d826bce059c4805e95658e299c12f5629 | |
| parent | 3df0d3c88ad309455bc5a2278d6f417e6e580aa6 [diff] |
[MINOR] Allow command-line specification of buffering test output This Commit change the default for our tests back to not buffer output. But it allows an alternative which, based on the argument: -D automatedtestbase.outputbuffering=true that re-enables the output buffering. The benefit of the buffering is that only test that fail, will print stdout and stderr, and thereby improve debug-ability;. This commit change the CI testing in GitHub workflows to per default use this configuration. This Commit also remove download printing for GitHub actions.
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 still in pre-alpha status. The original code base was forked from Apache SystemML 1.2 in September 2018. We will continue to support linear algebra programs over matrices, while replacing the underlying data model and compiler, as well as substantially extending the supported functionalities. Until the first release, you can build your own snapshot via Apache Maven: mvn clean package -P distribution.
