commit | 191aa16a7eb7980251355da32a01f35ff3537061 | [log] [tgz] |
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author | ms31 <70102697+manushree635@users.noreply.github.com> | Sat Oct 24 20:04:59 2020 +0530 |
committer | baunsgaard <baunsgaard@tugraz.at> | Sun Oct 25 11:24:51 2020 +0100 |
tree | 51e84f14b50feb877535999bb00b940c4af6089f | |
parent | a047fe5ad223f889de46373d851573fcf9123fba [diff] |
[MINOR] Update install.md Closes #1086
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