commit | 3a4decdb2a7969cf2d68707675dc96868f197de3 | [log] [tgz] |
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author | Mark Dokter <mark@dokter.cc> | Mon Nov 02 18:09:57 2020 +0100 |
committer | Mark Dokter <mark@dokter.cc> | Mon Nov 02 18:09:57 2020 +0100 |
tree | 9d41f327e292ccd2d42dd6fbbf57a7efc04af1e7 | |
parent | 34116722d908dc5dec172104ab3a6efdfb71a8bc [diff] |
[SYSTEMDS-152] Refactor readDML*FromHDFS and readR*FromFS methods in AutomatedTestBase Resolving JIRA issue https://issues.apache.org/jira/browse/SYSTEMDS-152
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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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