commit | 83e5eefd4f30095f05d8dfc549a2db1aae66f766 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Sat Jan 09 21:38:22 2021 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Jan 09 21:42:43 2021 +0100 |
tree | f790f64bc507cc072be7fd24a438d3d5e087470c | |
parent | a65acda690bc6afc77a324b0dc323994f540cb7a [diff] |
[SYSTEMDS-2789] Cleanup disguised missing value detection * Fix thread-safeness for parfor environments (no static instance vars) * Fix hashmap/iterator handling (removed HashedMap, unnecessary casts) * Fix formatting related test DIA project WS2020/21, part 2 Co-authored-by: David Kerschbaumer <david.kerschbaumer@student.tugraz.at> Co-authored-by: Patrick Lovric <patrick.lovric@student.tugraz.at> Co-authored-by: Valentin Edelsbrunner <v.edelsbrunner@student.tugraz.at>
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