| commit | 996f61281c45a428986a89bc14c982ad41af0382 | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Tue May 12 22:35:13 2020 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Tue May 12 22:35:13 2020 +0200 |
| tree | d6cb7fb1808744c701fb55f425092daa1d16ab09 | |
| parent | 96a719bc384f0c60dc1994be49d72d91d2031dea [diff] |
[SYSTEMDS] Fix and cleanup steplm feature selection built-in This patch makes several improvements to the existing steplm built-in function (correctness and performance): 1) So far, the lm parameters were not correctly passed through to the actual lm call, which for example rendered tol, reg, and icpt parameters ineffective (except for icpt=1 which was the only one tested). 2) Cleanup of unnecessarily operations and control flow 3) Converted the main two for loops of greedy model building to parfor loops (which required a slightly different analysis of the best model). On a scenario of a dense 10K x 1K input matrix (with convergence after 20 iterations -> ~21000 lm training calls), this patch improved performance from 103.9s to 14.4s due to much better utilization (with fewer barriers) of the available 24 virtual cores.
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.
