commit | 2cd9efd7600bf2e8f6c4d78414d8171a8e08c0bb | [log] [tgz] |
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author | baunsgaard <baunsgaard@tugraz.at> | Sat Sep 26 17:21:38 2020 +0200 |
committer | baunsgaard <baunsgaard@tugraz.at> | Wed Sep 30 17:01:11 2020 +0200 |
tree | 78444746903ec5d0f88eab811d3da7e29d65569a | |
parent | 0d1d011f7cf9dcf9fb2013dba523081508cb99fc [diff] |
[DOCS] Add Algorithms reference from previous docs closes #1068
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 renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source