commit | 4739e46ee6d7d01c74c62fe31f98e5b880d5f709 | [log] [tgz] |
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author | Mark Dokter <mark@dokter.cc> | Tue Oct 06 17:41:38 2020 +0200 |
committer | Mark Dokter <mark@dokter.cc> | Wed Oct 21 18:08:34 2020 +0200 |
tree | 3eef6b4d7408a14071aa4316abf398e17effcc69 | |
parent | dfa528532254aff2fb709f42564d57271407b0f2 [diff] |
[MINOR] Run script remote debugging and various fixes * specify -r to start a debugging server that waits for connections * help text format changes * print SystemDS parameter list upon request (explicit -help parameter) * print test class and method name to log level INFO when running junit tests * make gpu/stats toggles in pom and AutomatedTestBase work as they should
Overview: SystemDS is a versatile 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