| commit | f29ae426be1722fba9468609976709068e6e5d7d | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Fri May 22 22:38:54 2020 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Fri May 22 22:42:03 2020 +0200 |
| tree | d0fcfa8009c6e080ede77ac77173ce51d0b71d93 | |
| parent | 0dc1d16694a9fd7b56583b53566eab724d74a4db [diff] |
[SYSTEMDS-339] Fix robustness lineage tracing/parsing, part II This patch fixes many additional issues in lineage tracing and parsing in order to support the round-trip for steplm and kmeans. 1) Lineage tracing with default arguments of function call parameters (so far missing arguments where traces as literal variable name) 2) Lineage Tracing: rshape with parameters, ctable w/ dimensions, rand/seq w/ variable rows/cols, from/to/incr inputs 3) Lineage Parsing: rshape, rdiag, nrow, ncol, all casts ops, ifelse with scalar/matrix inputs (so far block size wrong), ctable /w dimensions, gappend spark ops 4) New lineage parfor algorithm tests: steplm, kmeans
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.
