| commit | 1f136eb3d0fdc17cc66c5c93006cc3ab6d81fc34 | [log] [tgz] |
|---|---|---|
| author | baunsgaard <baunsgaard@tugraz.at> | Sun Sep 06 12:30:31 2020 +0200 |
| committer | baunsgaard <baunsgaard@tugraz.at> | Mon Sep 21 11:18:44 2020 +0200 |
| tree | 3cc01ae40dd7ff6c1d4b1a80eb1789aaaa437f85 | |
| parent | e2c59779dacc6d28043a5de4a38bc620c450fb9e [diff] |
[MINOR] Python API hardening, and stability Minor changes in Python API startup for ease of startup if SystemDS is installed somewhere else it will use that SystemDS. This practically means that if you have SystemDS home set, it will allow the python to use that SystemDS, while if it is not set, it will default back to the installed jar files from the PIP install. This is a debated topic in #992, where it is argued that it would make it harder for a user if the PIP does not contain the jar files. - Fix dual setup of systemds_context - Added to usertest
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.
