| commit | f3f4fca3727a56224cd85bd177012db4863bdcf7 | [log] [tgz] |
|---|---|---|
| author | baunsgaard <baunsgaard@tu-berlin.de> | Wed Jul 19 22:58:18 2023 +0200 |
| committer | baunsgaard <baunsgaard@tu-berlin.de> | Wed Jul 19 22:58:23 2023 +0200 |
| tree | 61d854693e0cff122492f2249d7effff3ad805ff | |
| parent | fb828dda1ce76679ed031258344759da1b18d68f [diff] |
[MINOR] Batch mode mvn automation This commit change the most obvious places of maven in github actions to run in batch mode. This removes the printing of the download process from the github logs. That sometimes would increase the build log from 300 lines to 300k lines in just the build test.
Overview: SystemDS is an open source ML 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
