| commit | c85e9f4819d6936008a945eabae91e025da1c957 | [log] [tgz] |
|---|---|---|
| author | baunsgaard <baunsgaard@tu-berlin.de> | Thu May 11 19:05:05 2023 +0200 |
| committer | baunsgaard <baunsgaard@tu-berlin.de> | Thu May 11 19:07:51 2023 +0200 |
| tree | 0b1ae37f8ffc4002d022e133ce3a1ebacaf1bd1c | |
| parent | d7838c8d55e38db7d9ead5104ba8696eec64ac9a [diff] |
[MINOR] Update to java 11 in python docs Update the python docs to say java 11.
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
