commit | b75cf91b9a1077fc04468417ce87d33181295c62 | [log] [tgz] |
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author | arnabp <arnab.phani@tugraz.at> | Wed Dec 23 18:38:13 2020 +0100 |
committer | arnabp <arnab.phani@tugraz.at> | Wed Dec 23 22:15:27 2020 +0100 |
tree | d0bbcf15eab2d9cb7dc7072d55cf249fc02fd8fc | |
parent | 4e7ad989107d98633b5dfdd8612a99b1b16053bd [diff] |
[SYSTEMDS-2769] Fix lineage cache eviction test This patch replaces the current cache eviction test script with a better and robust (hopefully) one. This script simulates a mini-batch scenario with batch-wise preprocessing, which can be reused per epoch.
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