commit | 6dd66b8257b43146fe3cd31bab61f3595184928a | [log] [tgz] |
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author | arnabp <arnab.phani@tugraz.at> | Sat Jan 02 22:55:37 2021 +0100 |
committer | arnabp <arnab.phani@tugraz.at> | Thu Jan 07 15:18:26 2021 +0100 |
tree | 4a2af414db0d12382f47fb5a5b385739f2821e8f | |
parent | b7ddd5764854def8a84d093312f5a895b07eaa6e [diff] |
[SYSTEMDS-2784] Enable lineage-based reuse in federated workers This patch builds the initial infrastructure for lineage based reuse in federated workers. Changes include: - Lineage tracing InitFEDInstruction - Lineage trace READ and PUT requests. For PUT, lineageitem hash is sent with the request, which will be replaced by Adler32 in future commits. - Disable compiler assisted optimizations for lineage-based reuse (e.g. mark for caching) for the workers. - Testing infrastructure.
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 renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source