commit | 4b46b1f11c235bf998acfb3c42e1b255d853ea13 | [log] [tgz] |
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author | Olga <ovcharenko.folga@gmail.com> | Fri Jan 29 23:35:48 2021 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Jan 30 01:19:18 2021 +0100 |
tree | 94d21c6b825031fdae894ee9a2f320ad4e4490bd | |
parent | ef0cc71a75d32c977889bde5191c61617dbb0927 [diff] |
[SYSTEMDS-2810] Improved serialization of transform encoders The transform encode and apply encoders/decoders carry potentially large meta data such as the recode maps (dictionaries). For this reason, this patch adds dedicated serialization and deserialization code paths, to bypass the default java serialization and thus, avoid serializing temporary internal data structures and unnecessarily bloated representations. This serialization framework is now implicitly used in respective spark and federated instructions. Closes #1171.
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