commit | f7b5f8811ad0c264f6ee1c7ecb9d16f4315698ca | [log] [tgz] |
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author | baunsgaard <baunsgaard@tugraz.at> | Mon Feb 01 11:58:55 2021 +0100 |
committer | baunsgaard <baunsgaard@tugraz.at> | Mon Feb 01 11:59:07 2021 +0100 |
tree | ec0da8ea02d250a815ab3f5fba9c3b3bf767fff6 | |
parent | e57dfd0e593052bb84ee388ec516bea578b90227 [diff] |
[SYSTEMDS-2821] Python Stability There have been some startup issues in the python API, where some tests would not properly connect to the JVM. This task addresses this by introducing a retry startup of the context in case of failures.
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