| commit | 04e1e61a3ad2d6dd48e92c04fb44fe3b0eb107ad | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Wed Jun 07 20:59:19 2023 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Wed Jun 07 20:59:47 2023 +0200 |
| tree | 4e5f688591e79a79ffeca21962e5236ba49eea22 | |
| parent | 36507ce4e1aed403742a144b8bfecd1bae5ba749 [diff] |
[MINOR] Added missing builtin functions docs to dml language reference * Builtin functions added: isNA, isNaN, isInf, contains, inv * Alternative name isInf for is.infinity for consistency with isNA and isNaN
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
