commit | 756e4471527281439fc62610cdb30f6c8cfe9301 | [log] [tgz] |
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author | Shafaq Siddiqi <shafaq.siddiqi@tugraz.at> | Mon Nov 16 21:21:59 2020 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Mon Nov 16 21:22:00 2020 +0100 |
tree | 8d7a9a849063ad3eae1c6860e95db1d67d845f43 | |
parent | b29eb148c75cdc87b3613a9b603c7d1750cc2b7c [diff] |
[SYSTEMDS-2625]: Cleanup GMM built-in function, new logSumExp This cleanup includes the functions reordering, branching cleanups and elimination of lists, now matrices are used for storing covariance. The private function logSumExp is replaced with a new builtin logSumExp(). Closes #1023.
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