An open source ML system for the end-to-end data science lifecycle

Clone this repo:
  1. bc372e7 [SYSTEMDS-3418] Fix block size handling replace operations by Matthias Boehm · 3 days ago main
  2. 8488788 [SYSTEMDS-3418] Fix missing large block support in replace operations by Matthias Boehm · 3 days ago
  3. 89720cc [MINOR] Mark additional unary ops for multi-threaded operations by Matthias Boehm · 3 days ago
  4. 674b4e5 [MINOR] Fix corrupted -stats output (unchecked federated statistics) by Matthias Boehm · 5 days ago
  5. 4a62c52 [SYSTEMDS-3417] Fix integer overflow in fast-buffered-input-stream by Matthias Boehm · 5 days ago

Apache SystemDS

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

Build Documentation Component Test Application Test Function Test Python Test