commit | f3c9118d78a9f2e38d0c2c54874dcb5adf428744 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Mon Aug 31 22:29:15 2020 +0200 |
committer | Matthias Boehm <mboehm7@gmail.com> | Mon Aug 31 22:40:54 2020 +0200 |
tree | e00b5609bab603c942541e6b85025e3dd76d7340 | |
parent | a8bbe1a601a4fe228650f63cbb7255d53998d33a [diff] |
[SYSTEMDS-2652] Fix function deep copy for eval fcalls (for predicates) This patch fixes an issue of the deep copy of FOR statement blocks if eval requires to keep the unoptimized functions. Before we introduced eval, this deep copy was only used for uncontended recompilation in parfor and thus, predicate hops (from, to, increment) were only copied if necessary. However, this is invalid for the creation of unoptimized functions from which otherwise no valid execution plans can be compiled. This led to missing predicate variables because the predicate instructions were missing.
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.
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Documentation: SystemDS Documentation
Python Documentation Python SystemDS Documentation
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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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