commit | 9641173dd54ba9d43e4869483006bfc4fc66897c | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Sat Nov 28 23:47:53 2020 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Nov 28 23:47:53 2020 +0100 |
tree | c897addf37c87ed2985c5314859cf9e44fb9e184 | |
parent | 02bd79742b7d932bbd351fcf78fbfbde9446afa7 [diff] |
[SYSTEMDS-2550] Fix in-memory reblock for federated matrices/frames This patch fixes the spark reblock instructions (always compiled in hybrid mode), which incorrectly consolidate federated matrices/frames into the driver. We now simply extended the implementation to respect existing federated data objects.
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.
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Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source