commit | cb9c890d207feb7877d0768395c6ea473c7ab0bd | [log] [tgz] |
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author | arnabp <arnab.phani@tugraz.at> | Thu Feb 04 20:35:50 2021 +0100 |
committer | arnabp <arnab.phani@tugraz.at> | Thu Feb 04 20:45:23 2021 +0100 |
tree | 1ad91538ae39632f34f51e05aed7931b3a9bf30f | |
parent | e8b65fe6e97e8d52dc1b0150592090b281dca507 [diff] |
[SYSTEMDS-2799] Update UDF reuse, add Fed pipeline reuse test This patch handles reusing those UDFs that return metadata with federated SUCCESS response (e.g. Rdiag, DiagMatrix). Furthermore, this adds a test to reuse a full federated pipeline having tasks such as preprocessing and hyperparameter tuning (LM).
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
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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