commit | c02f3bb38dcdda8a43ec805255f07967c9ac4809 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Sat Jun 27 19:58:56 2020 +0200 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Jun 27 19:58:56 2020 +0200 |
tree | a698bc5651ba9120783432e28bb283537664fde3 | |
parent | dfb36d102ff76a55d130b33fad7791dd2108db9c [diff] |
[SYSTEMDS-416] Lineage deduplication (while, nested if, robustness) This patch makes a major refactoring of the lineage deduplication framework, including removed indirections and support for while loops and nested if program blocks. We now drop support for nested loops but this is fine as they are anyway split into many items and the biggest benefit comes from the last-level loop. In contrast, nested if blocks are critical in practice and this required a more generic collection of the lineage patches for all distinct paths (which we still do in a single pass over the loop body program). Additionally, we now support while loops with an integration very similar to for loops.
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
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