| commit | f0f8f0c19083114be5383d91ebbe80d362f6abbe | [log] [tgz] |
|---|---|---|
| author | baunsgaard <baunsgaard@tu-berlin.de> | Mon Jul 03 19:37:37 2023 +0200 |
| committer | baunsgaard <baunsgaard@tu-berlin.de> | Wed Jul 05 14:22:55 2023 +0200 |
| tree | 1c777866dc325249df8017867ae8dcf59b23a964 | |
| parent | 52aa43187bd4bb8e9f64eac8e765e2d8b715cdab [diff] |
[SYSTEMDS-3592] Frame Compress This commit adds a compression pipeline for frames to first analyze a sample, that then is used to determine compression of individual columns. The distinct estimation tools of the matrix compression frame work is used. Next step is parallelization of the compression. Closes #1856
Overview: SystemDS is an open source ML 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
Issue Tracker Jira Dashboard
Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source
