| commit | 4e1339222c1c69c0d0f46f1d7bdf651da4b3a325 | [log] [tgz] |
|---|---|---|
| author | arnabp <arnab.phani@tugraz.at> | Mon Aug 10 16:37:14 2020 +0200 |
| committer | arnabp <arnab.phani@tugraz.at> | Sun Aug 16 18:36:48 2020 +0200 |
| tree | f56fb7c1fcf0d3fed6c8c7fd99d38ac3440bd05a | |
| parent | 0f54db9c79cd768f76c04007bd9fc5069fed5060 [diff] |
[SYSTEMDS-2581] Serialization, deserialization of dedup DAGs This patch includes: - a new approach to hook the dedup patches to the main trace. Now the dedup opcode also encodes details needed for mapping the item back to the correct dedup patch after deserialization. - Two methods to serialize and deserialize the compressed dedup DAGs. Serialization logic merges all the patches in a single string and maintains headers with all the information. Deserialization logic parses the headers to map the DAGs to the right dedup entries from the main trace.
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.
