commit | 608073796965354fcdbbd545194bc5b96c1e53d6 | [log] [tgz] |
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author | arnabp <arnab.phani@tugraz.at> | Thu Nov 12 22:23:09 2020 +0100 |
committer | arnabp <arnab.phani@tugraz.at> | Sat Nov 21 02:47:27 2020 +0100 |
tree | 1d2ccf9b1291f2b40a4f793c901f620f05cdeb6a | |
parent | f9e60f2cbd9bbced6f5cccf9d4ad960f9b18ea70 [diff] |
[SYSTEMDS-2739] Adjust computeTime for CostNSize with ref counts This patch improves the CostNsize lineage cache eviction policy by adjusting the compute time of an cache entry with reference counts (#hits, #misses). This patch also introduces a non-recursive equal method for LineageItem.
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