| commit | edd3a8d6804da648b925993ea7d7742df30beb10 | [log] [tgz] |
|---|---|---|
| author | Mark Dokter <mark@dokter.cc> | Fri May 08 20:34:53 2020 +0200 |
| committer | Mark Dokter <mark@dokter.cc> | Fri May 08 20:34:53 2020 +0200 |
| tree | 96e84ffc8896e3ce4a9cdebfac5e81c8fc822d8c | |
| parent | 8cbc85a949b3699cde8ed3cf3e3abec6a27fbc60 [diff] |
[SYSTEMDS-38] Runscript rename, improve, fix cygwin * Runscript is now called systemds (formerly systemds.sh) * For the sake of consistency, one may use '-f' now in front of the dml script file * The script now works in cygwin on Windows (make sure the script stays in unix LF format now (git for win usually checks out files in CRLF) * Cleaned up messy handling of absolute and relative paths (using 'realpath' executable from gnu coreutils package now - should be part of any standard bash installation) * Fixed some directory separator issues, shellcheck warnings and native libs path * Replaced all occurances of systemds.sh in the codebase
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.
