| commit | 205c67aaf1778cb62f91de415ab677ad8be7f2dd | [log] [tgz] |
|---|---|---|
| author | arnabp <arnab.phani@tugraz.at> | Tue Jun 02 18:20:03 2020 +0200 |
| committer | arnabp <arnab.phani@tugraz.at> | Mon Jun 08 16:05:46 2020 +0200 |
| tree | b7571d2bd9ffa45f4d6fe2985f93708131e57755 | |
| parent | efd89db58b10166e7045ad9125228ee15c1e9b89 [diff] |
[MINOR] Extend reusable instructions and bug fixes This patch - extends reusable opcodes, which primarily improves multilevel (statementblock) cache hits, - removes most of the System.nanoTime calls from cache logic, - replaces operand names with placeholders in datagen lineage items, (Note: this fix is temporarily commented due to a bug in parfor-lineage) - fixes a bug in lineage item creation for multilevel caching, - update grid search lineage test with a loss function.
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.
