| commit | bd0b319df52215b359c04590ed4091ad136ea4f9 | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Sat May 16 17:00:59 2020 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Sat May 16 17:03:11 2020 +0200 |
| tree | ebdeac2a759d3297b1eb7420c65eaffa47a1d133 | |
| parent | 9bc52328f5535492d50cca811a67bd81829220ce [diff] |
[SYSTEMDS-74] Cleanup lineage tracing (unnecessary variable names) This patch removes unnecessary attributes from lineage items in order to reduce the size (and GC overhead) for long lineage traces. So far, each lineage item kept the variable name to which is was bound. As lineage information should be independent of such properties, this information was already ignored for lineage hashing and comparisons. In few, places however, we use it to rewire placeholders, which is now cleaned up.
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.
