| commit | d0597c0f62ca35dc6f99235bd7cfffa2421c6ab4 | [log] [tgz] |
|---|---|---|
| author | baunsgaard <baunsgaard@tu-berlin.de> | Wed May 17 10:24:38 2023 +0200 |
| committer | baunsgaard <baunsgaard@tu-berlin.de> | Wed May 17 12:38:45 2023 +0200 |
| tree | 875225465d9f6de3d8a7bc1a5afd1e5d30d565a5 | |
| parent | a47d266dfd4d4901b4694b3965502d1ae3915ff3 [diff] |
[SYSTEMDS-3490] Compressed Transform Tests This commit update the compressed tests to 100% coverage and fixes some edge cases in binning and hashing. Closes #1826
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
