commit | 3d97048e457a00af9288a9940f962392df3abcbc | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Sat Oct 31 18:05:54 2020 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Oct 31 18:18:52 2020 +0100 |
tree | 5356fe184b16f815d8df200cf7219735f00abe75 | |
parent | f308d56c560df52785e52456ece60ac74de49672 [diff] |
[SYSTEMDS-2679] Fix dim size propagation for federated data ops This patch fixes two issues that where encountered in federated parameter servers. First, we fixed null pointer executions in the cleanup of parallelized RDDs for the case of redundant cleanups. Second, we fixed the size propagation for federated DataOps which so far was never refreshed during recompilation after initial parsing. Each of these fixes alone would already solve the reported bug, but the fixed size propagation is important for all federated use cases in default hybrid execution mode.
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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Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source