| commit | cde6142d8c579ab6afd7aa129ef3b3e79395660e | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Sat Jun 27 22:08:33 2020 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Sat Jun 27 22:08:33 2020 +0200 |
| tree | f2b69ad73607fb8dc5e5857fd470281a8ca7d2dc | |
| parent | c02f3bb38dcdda8a43ec805255f07967c9ac4809 [diff] |
[SYSTEMDS-211] Fix incorrect size propagation list-matrix rbind/cbind This patch fixes size propagation issues during parsing and recompilation for rbind/cbind operations over lists into a single matrix. Together with other rewrites, the incorrect size propagation led to invalid runtime plans. However, the additional tests with CV-lm still require an assertion to allow function inlining as a precondition for the fold-rewrite to eliminate redundancy. Solving this remaining issue requires a principled size propagation approach for matrix objects in lists.
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
Status and Build: SystemDS is still in pre-alpha status. The original code base was forked from Apache SystemML 1.2 in September 2018. We will continue to support linear algebra programs over matrices, while replacing the underlying data model and compiler, as well as substantially extending the supported functionalities. Until the first release, you can build your own snapshot via Apache Maven: mvn clean package -P distribution.
