commit | dfb36d102ff76a55d130b33fad7791dd2108db9c | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Sat Jun 27 00:26:01 2020 +0200 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Jun 27 00:38:19 2020 +0200 |
tree | 8df13fa275a3ce3916a593a26c913eb93d8f3093 | |
parent | 73a25a651659137716c02e8b633bb54c95dcb97c [diff] |
[SYSTEMDS-415] Initial mlcontext lineage support (tracing, reuse) This patch adds basic lineage support to the MLContext API. Since in-memory objects are directly bound to the symbol table, lineage tracing views these objects as literals and incorrectly reused intermediates even if different in-memory objects where used in subsequent mlcontext invocations.
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
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