commit | ced2ae8212124b49afb1dff330e2592131e324d0 | [log] [tgz] |
---|---|---|
author | Matthias Boehm <mboehm7@gmail.com> | Sat Jan 30 17:48:29 2021 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Jan 30 17:48:29 2021 +0100 |
tree | 974fed4dc8433be53acf5e107d5c532ebdf877ae | |
parent | 4b46b1f11c235bf998acfb3c42e1b255d853ea13 [diff] |
[MINOR] Fix robustness multiLogReg and multiLogRegPredict * Handling of missing values in the feature matrix X (NaN in double matrices) by replacing NaN with 0 to avoid NaN gradients and intermediates, which ultimately, compute NaN models. * Cleanup multiLogReg formatting and constants * Consistent handling of Y modifications (Y<=0) in both multiLogReg and multiLogRegPredict
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