commit | 6165b509c3b3fcfc0b0690d52d1afe6e13d3fc17 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Sat Jan 16 21:47:31 2021 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Jan 16 21:47:31 2021 +0100 |
tree | 6fd8657ac91bfce7dd851a7e0a30ea2e0927ddb3 | |
parent | e8cc1de36777a91a23d02ba5998c2083bbb224b0 [diff] |
[SYSTEMDS-2797] Cleanup new statsNA built-in function * Vectorized all loops of statsNA * Fix statsNA verbose printing of gaps vector * Fix statsNA test formatting and warnings, * Fix statsNA documentation (formatting, conciseness) DIA project WS2020/21, part 2 Co-authored-by: haubitzer <rene.haubitzer@gmail.com> Co-authored-by: Ismael Ibrahim <ismael.ibrahim@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