commit | d2fdeeb5c1f0361ee0c5deacd5989649bf13e5f4 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Thu Aug 20 19:46:20 2020 +0200 |
committer | Matthias Boehm <mboehm7@gmail.com> | Thu Aug 20 20:04:13 2020 +0200 |
tree | 039bbe18a425ad5e5ebfd73156b45bc050244e6f | |
parent | c9a02a2e9fa3ec50a2a8c3611d62a2a334b5bab3 [diff] |
[SYSTEMDS-2624] Cleanup federated workers for repeated execution This patch fixes the cleanup of federated workers to perform a full cleanup of variables and execution context before and after every execution. This change now enables keeping the federated workers as standing executors and launch repeated coordinator jobs without any conflicts of existing variables or unnecessary memory pressure and evictions. Furthermore this also adds related Kmeans tests with multiple runs (and reset ID sequences to provoke conflicts) as well as PCA tests with more than two workers.
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 still in pre-alpha status. The original code base was forked from Apache SystemML 1.2 in September 2018. We will continue to support linear algebra programs over matrices, while replacing the underlying data model and compiler, as well as substantially extending the supported functionalities. Until the first release, you can build your own snapshot via Apache Maven: mvn clean package -P distribution
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