| commit | b84a2d16e1796a6d68baf9e16b7ab87982c8e34d | [log] [tgz] |
|---|---|---|
| author | arnabp <arnab.phani@tugraz.at> | Wed Aug 19 20:45:36 2020 +0200 |
| committer | arnabp <arnab.phani@tugraz.at> | Sat Aug 29 18:35:16 2020 +0200 |
| tree | 0792fda3b7e270a51958a9551ee5c3be20ebfdbe | |
| parent | f60b790e3571f401be2626f222aeec4a5a9fe53d [diff] |
[SYSTEMDS-2650] Re-computation from lineage with dedup
This patch adds the below changes.
- We now compile all the dedup patches into functions,
- The main program places a function call for each dedupOp
and calls the corresponding function,
- Move the recomputation related code to a new class,
- Add a new test class to match the recomputed results
with the original outputs.
Current code doesn't support multiple loops. Future commits
will add optimizations to construct multi-return functions
(instead of one per output variable), and compile sequence
of equivalent function calls into 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
Issue Tracker Jira Dashboard
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.
