| commit | 8da7d9ba7015e7d740f3a7765fdaa197d80bc2ab | [log] [tgz] |
|---|---|---|
| author | Sebastian <baunsgaard@tugraz.at> | Mon Mar 23 22:00:18 2020 +0100 |
| committer | Sebastian <baunsgaard@tugraz.at> | Mon Mar 23 22:00:18 2020 +0100 |
| tree | 8fe931904b0c953c5560ea3ff47a75883145b48f | |
| parent | a93ad0c4b45e14e6357e2df723bdcaeea3c961fc [diff] |
[MINOR] Fix workflow testing Removed output action for the custom action. Now the entrypoint script for testing workflows looks at the last 100 lines of the test output, and determine if the tests was passed or not. If the tests are passed only print 100 lines to the logging from the workflow, if the tests fail, print all lines to the logging file.
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.
Documentation: SystemDS Documentation
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 -DskipTests clean package.
