commit | 3315eb62ab593f4526f307a6037a8d80109559db | [log] [tgz] |
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author | Mark Dokter <mark@dokter.cc> | Thu Feb 25 13:00:42 2021 +0100 |
committer | Mark Dokter <mark@dokter.cc> | Thu Feb 25 13:00:42 2021 +0100 |
tree | 9e71b11cab61ab5cf242706bbbb49bca9fde7f03 | |
parent | 44e42945541150d7e870c5b89e79c085d63b4e78 [diff] |
[MINOR] Correcting a logic error while deciding to avoid execution on empty input in SpoofCUDACellwise
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