| tag | fe96450858da0c920631fb02a9d594921d0be307 | |
|---|---|---|
| tagger | Janardhan Pulivarthi <janardhan@apache.org> | Thu Mar 07 03:46:49 2024 +0000 |
| object | 1130afe0f3423d5415a016b15fd86cbb714cb3bf |
[maven-release-plugin] copy for tag 3.2.0-rc2
| commit | 1130afe0f3423d5415a016b15fd86cbb714cb3bf | [log] [tgz] |
|---|---|---|
| author | Janardhan Pulivarthi <janardhan@apache.org> | Thu Mar 07 03:46:47 2024 +0000 |
| committer | Janardhan Pulivarthi <janardhan@apache.org> | Thu Mar 07 03:46:47 2024 +0000 |
| tree | 369048809132b70e238912a0c834340ca61ebba2 | |
| parent | 5c223e27feb7baee90d1016a7bb333877e35a636 [diff] |
[maven-release-plugin] prepare release 3.2.0-rc2
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
