commit | e581b5a6248b56a70e18ffe6ba699e8142a2d679 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Mon Jul 20 21:37:21 2020 +0200 |
committer | Matthias Boehm <mboehm7@gmail.com> | Mon Jul 20 21:37:21 2020 +0200 |
tree | b9b3bbc9c00ba18533232b717038159df453572f | |
parent | 586e910a1f6d728ab2fc80ea716cf0545946bb19 [diff] |
[SYSTEMDS-2575] Fix eval function calls (incorrect pinning of inputs) This patch fixes an issue of indirect eval function calls where wrong input variable names led to missing pinning of inputs and thus too eager cleanup of these variables (which causes crashes if the inputs are used in other operations of the eval call). The fix is simple. We avoid such inconsistent construction and invocation of fcall instructions by using a narrower interface and constructing the materialized names internally in the fcall.
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
Status and Build: SystemDS is still in pre-alpha status. The original code base was forked from Apache SystemML 1.2 in September 2018. We will continue to support linear algebra programs over matrices, while replacing the underlying data model and compiler, as well as substantially extending the supported functionalities. Until the first release, you can build your own snapshot via Apache Maven: mvn clean package -P distribution
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