| commit | 10c2165e13faccf5ddf398d8d0ecabc953e583ab | [log] [tgz] |
|---|---|---|
| author | Shafaq Siddiqi <shafaq.siddiqi@tugraz.at> | Thu May 12 13:54:25 2022 +0200 |
| committer | Shafaq Siddiqi <shafaq.siddiqi@tugraz.at> | Thu May 12 13:54:25 2022 +0200 |
| tree | 6f8601c582a7a5ce8aa68b0c4ee220d08983864a | |
| parent | a0987e536a2be71d16d64ac64e9873206083e49b [diff] |
[SYSTEMDS-3376] Adding apply_pipeline() builtin for cleaning pipelines API - fit_pipeline() executes the best pipeline on train and test data while apply_pipeline() transforms the test data using the internal states of best pipeline without re-executing the best pipeline and keeping the train data around.
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
