commit | e57dfd0e593052bb84ee388ec516bea578b90227 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Sun Jan 31 22:58:16 2021 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sun Jan 31 22:58:16 2021 +0100 |
tree | 78d82a54a7fcbc2cb53165baa911780e06551137 | |
parent | ebcc44db28d59ec076e3a56cee1051361a1d3c24 [diff] |
[SYSTEMDS-2819,2020] Various ctable improvements (rewrites), part II * Fix ctable->rexpand rewrite (failed tests in previous commit) * Allow ctable-reshape -> ctable rewrite besides for CP also for Spark * Fix the ctable estimation of output partitions, to account for both vectors and matrices, not just vectors (otherwise potentially large underestimation of the nnz upper bound) * Post-processing of spark ctable outputs to obtain exact sparsity of ultra-sparse matrices that fit comfortably into the local memory budget.
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