| commit | 08944a7305cbc4f4d9cbbd4565efa8bcc93b82e3 | [log] [tgz] |
|---|---|---|
| author | Shafaq Siddiqi <shafaq.siddiqi@tugraz.at> | Wed Jan 12 16:36:58 2022 +0100 |
| committer | Shafaq Siddiqi <shafaq.siddiqi@tugraz.at> | Wed Jan 12 16:36:58 2022 +0100 |
| tree | dc60e832aed6ce5985443ef938590d1457a4d3b2 | |
| parent | 2bb2b462f517a1205baee15f887c6e26a8e45c62 [diff] |
[MINOR] Minor changes in logical enumeration
- This commit introduce the default parameter values for the cleaning primitives
- The logical enumeration now only use the default parameter values to evaluate the
primitives and iteratively add the categories to logical pipelines until converge.
- The output is a single best logical pipeline
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
