| commit | ca72e4b03fc36dc7faf1f206e9261f02198ef8b0 | [log] [tgz] |
|---|---|---|
| author | Arnab Phani <phaniarnab@gmail.com> | Thu Apr 06 15:25:24 2023 +0200 |
| committer | Arnab Phani <phaniarnab@gmail.com> | Thu Apr 06 15:25:24 2023 +0200 |
| tree | 51664982a9b86eb9ac81bae9997215d5768376a5 | |
| parent | 85370c7b1d920f0fcfebfef37fffb664ef79131d [diff] |
[SYSTEMDS-2947] Re-enable lineage cache eviction from GPU to host This patch adds code to be able to copy a cached pointer to a cached matrix block. The plan is to conditionally evict cached entries to host based of the score (compute time and #hits, #misses) while recycling. Currently, the eviction is disabled as we do not have a way to measure the elapsed time of the GPU kernels due to their asynchronous nature. Closes #1802
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
