commit | cbdfb9286286b7fd1684666a529bb4cc4ec213b4 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Sat Jan 09 22:20:56 2021 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Jan 09 22:20:56 2021 +0100 |
tree | 05ac854786b8988683f5a266a1df823b6ca7c456 | |
parent | 83e5eefd4f30095f05d8dfc549a2db1aae66f766 [diff] |
[SYSTEMDS-2788] Fix list-append validation, write error handling This patch fixes an issue of invalid list-append validation. For lists appends, we can append objects of any size and thus ignore inferred dimensions (from cbind/rbind). However, if the dimensions of the appended matrix where known, the existing validation code incorrectly raised errors (for unknown dimensions only warnings, that's why it did not show up in other scripts). Furthermore, this also improves the error handling if due to script-level mistakes we compile a spark write instruction for lists.
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