| commit | a1cb1df2ce1543a9278886252691c95a8e7917a0 | [log] [tgz] |
|---|---|---|
| author | Mark Dokter <mark@dokter.cc> | Thu Jul 20 21:33:16 2023 +0200 |
| committer | Mark Dokter <mark@dokter.cc> | Thu Jul 20 21:33:54 2023 +0200 |
| tree | 0c366270a0a7b2c8078bdff1262bc2139e160f37 | |
| parent | 4ebd5aada924999d07dce7160e5c61f7cd26c47b [diff] |
[MINOR] Further apache.commons.lang3 changes A recently merged PR added two occurrences of apache.commons.lang version 2 imports which makes compilation fail on newer systems that don't have this old package in their cache.
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
