commit | 69d55cd9d73884303ce983d29606eae574f2964e | [log] [tgz] |
---|---|---|
author | Sebastian Baunsgaard <baunsgaard@tu-berlin.de> | Fri Apr 05 23:19:21 2024 +0200 |
committer | Sebastian Baunsgaard <baunsgaard@tu-berlin.de> | Fri Apr 05 23:21:07 2024 +0200 |
tree | 8427f2d909e84daffccf501aad8a2a52a5101518 | |
parent | e473bb22db53fbe5b68c91c0a6240974bcd2553c [diff] |
[MINOR] Fix SYSDS_QUIET
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.
Resource | Links |
---|---|
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