| commit | 5d00ea1732e66e5bbcc32dbd065b6fe2b8de0cdf | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Sat Jul 25 20:27:03 2020 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Sat Jul 25 20:27:03 2020 +0200 |
| tree | 1c5b82bd2e3205a1da6d7f0d4d891bc2f1b733d4 | |
| parent | 8d61ae6f46f0a8ce21f9ad7c3a617023f6983778 [diff] |
[SYSTEMDS-2589,2510] Removed dependency commons.collections This patch removes the rarely used (and thus unnecessary) dependency to commons.collections to simplify packaging/installation in various environments. The needed functionality has been re-implemented from scratch, which fixes undetected bugs (ignore order on list compare), and improved performance (containsAny, constant-time lookups) due to specialization. Furthermore, this patch also replaces all occurrences of 'blacklist' with more inclusive language.
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.
