commit | a65acda690bc6afc77a324b0dc323994f540cb7a | [log] [tgz] |
---|---|---|
author | ywcb00 <ywcb00@ywcb.org> | Mon Dec 28 13:50:07 2020 +0100 |
committer | baunsgaard <baunsgaard@tugraz.at> | Sat Jan 09 15:10:53 2021 +0100 |
tree | da956f021ca62d19b00877f57412d5f942f0492b | |
parent | 3a9baf48427c8ad6f51a233feeff03a407175f64 [diff] |
[SYSTEMDS-2747] Federated Quarternary Operations WSLoss and WSigmoid Quaternary operations, part 2 This commit adds support for two federated quarternary operations, along with federated junit tests. - Weighted Sigmoid function - Weighted Sigmoid loss function Closes #1143
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