| commit | e473bb22db53fbe5b68c91c0a6240974bcd2553c | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Fri Apr 05 18:00:13 2024 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Fri Apr 05 18:00:30 2024 +0200 |
| tree | 03d574e0799c53e6a43438ce7b978fd1dbbb33e5 | |
| parent | ecb53edea03a8175f5077b0758503979f2921646 [diff] |
[MINOR] Cleanup buffer pool handling of new data objects The patch removes redundant compaction logic, which might cause multiple compactions to MCSR (before conversion to CSR during shallow serialize), and handling of all blocks in the buffer pool on writing a single larger than buffer pool object.
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
