| commit | d3480c1ee032694aaf86839efb5ad656c041f16f | [log] [tgz] |
|---|---|---|
| author | Arnab Phani <phaniarnab@gmail.com> | Wed Jun 14 10:53:34 2023 +0200 |
| committer | Arnab Phani <phaniarnab@gmail.com> | Wed Jun 14 10:54:07 2023 +0200 |
| tree | 5253cbbc432c56fc497dbf7c33a41b862799e7d6 | |
| parent | 866c0fc5e05e9ac6aa97708911ec138092552ca8 [diff] |
[SYSTEMDS-3562] Save multi-statementblock checkpoints for recompiler This patch adds a temporary fix to save the position of the checkpoint instructions placed in a loop body during compilation and again place those in there during recompilation. A better fix would be to enable recompilation for that loop or the function. Closes #1844
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
