| commit | f8acaab0c755dca0bf2e63c3f4d8279bc5effcaa | [log] [tgz] |
|---|---|---|
| author | Arnab Phani <phaniarnab@gmail.com> | Thu Jul 06 10:58:48 2023 +0200 |
| committer | Arnab Phani <phaniarnab@gmail.com> | Thu Jul 06 10:58:48 2023 +0200 |
| tree | 0f0511f5150c208d629c9fd5c3ba8eb114153de4 | |
| parent | f0f8f0c19083114be5383d91ebbe80d362f6abbe [diff] |
[SYSTEMDS-3594] Multi-level reuse of RDDs This patch extends the multi-level reuse framework to support functions and statement blocks returning RDDs. Similar to instruction-level reuse, we persist the function outputs on the second call. Based on if the original instruction is shuffle-based, we also reuse the function output locally. Closes #1858
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
