commit | b37026bac137f780bc9f3fb34887c7dac35a2a5c | [log] [tgz] |
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author | arnabp <arnab.phani@tugraz.at> | Wed Feb 10 20:15:04 2021 +0100 |
committer | arnabp <arnab.phani@tugraz.at> | Wed Feb 10 20:15:04 2021 +0100 |
tree | 80e2077489b8197bc360de5f22550b2d2567adbb | |
parent | 2e341dd346aa4efd9b8e4fbf44cfa12f63719815 [diff] |
[SYSTEMDS-2739] Fix Cost&Size eviction policy This patch fixes a bug in the logic of adjusting scores by cache reference count. In addition to that, this patch makes the estimation of saved and missed compute time more robust and accurate.
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