commit | 4dedddcbf626799d91507a84987ee7f9c9e6a2bb | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Sun Nov 15 19:12:40 2020 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sun Nov 15 21:04:15 2020 +0100 |
tree | 55e1e6224a36f03af49f464250169ce4a4b371b2 | |
parent | 4d8ec5dd64922441f0acc452eee3e49bee0653cd [diff] |
[SYSTEMDS-2733] Fix federated append, right-indexing, as.frame, tests This patch fixes various issues of federated instructions and meta data handling in order to allow full federated ML pipelines. * Append: fix for missing federated append (retain row-partitioning), correct federated ranges, and only partial broadcasting * Right Indexing: fix for correct federated ranges, and only indexing for relevant ranges * Transformencode: fix for consistent recode maps, and ranges * Matrix-Frame casts: fix for consistent dimension meta data * Tests: more reasonable fed worker startup time, configurable constant, fixed output buffering for tests that process the buffer
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