| commit | 47924e6aced3dac0768756c7dfec932d696b6a3f | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Sat Apr 11 22:51:50 2020 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Sat Apr 11 22:51:50 2020 +0200 |
| tree | fb89c26c4d721409fe4837eb05137a36d5b66595 | |
| parent | 8adb8c0ec3ed9a9e867a5695a5390dc86a48413f [diff] |
[SYSTEMML-2538] Fix csv/text output rename in forced singlenode This patch fixes an issue where an input csv/text file is directly fed into a persistent write, which eventually just renames the input file because it already exist on HDFS in the right format. We now explicitly guard against persistently read inputs, which only can occur w/ forced singelnode execution mode because other (in spark and hybrid) there is a reblock (potentially in memory) that creates a new metadata object. Furthermore, this also includes a minor internal refactoring for consistently obtaining input/output infos for external format strings, as well as a slight modification of the MatrixMatrixCellwiseTest to run over smaller inputs (because R is taken quite a while for them).
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.
Documentation: 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 -DskipTests clean package.
