| commit | 9f82e9f675c567c7f80360f3814e3a19138d7a62 | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Wed Jan 24 22:36:55 2024 +0100 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Wed Jan 24 22:37:31 2024 +0100 |
| tree | 1e14d5259d5caf3dc50dbad135e20ec397c64bb2 | |
| parent | b34c9891d9662189124a612b8880f3c4e9b09996 [diff] |
[SYSTEMDS-3668] Fix ultra-sparse tsmm for CSR sparse blocks There were flaky component tests, which originated from the shared matrix blocks being converted from MCSR to CSR and then the new ultra-sparse tsmm failing due to incorrect index handling. This patch generalizes the tsmm implementation for all sparse blocks.
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
