| commit | e0966e739177c751f4e2cdbb6cae0bd50f2f7ef5 | [log] [tgz] |
|---|---|---|
| author | lachezar-n <lachezar.nikolov99@gmail.com> | Sun Sep 22 11:39:36 2024 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Sun Sep 22 12:08:20 2024 +0200 |
| tree | 167a42f1ed42d98d33c3bbb2929af27cc472db4d | |
| parent | 49acf287f5c538b7e531eb229d6b45b40528e5af [diff] |
[SYSTEMDS-1780] Improved cost estimator for resource elasticity Closes #2108.
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
