commit | f93cd5d4330ed55fffd33a6c8edf392f9bbf897d | [log] [tgz] |
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author | Lukas Erlbacher <lukas.erlbacher97@gmail.com> | Thu Oct 21 22:54:42 2021 +0200 |
committer | arnabp <arnab.phani@tugraz.at> | Fri Oct 22 00:03:35 2021 +0200 |
tree | a65ca1e0ab5e2388e5a5830f4477f23b9a000b15 | |
parent | 924a958343bb7d61cbf9d3c56f48ef76e7034fa9 [diff] |
[SYSTEMDS-2972] Single interface for multi- and single-threaded transformations This patch introduces the Transformable Interface which defines the needed functions for the Encoders. It is implemented by the MatrixBlock and FrameBlock. This interface merges code paths for Multi- and Single-threaded operations and makes the code more readable. There is still redundant code in SparseApply Tasks. Those can be removed in the future but at the moment they help in debugging. Furthermore, this patch reworked the missing value handling in the encoders. Closes #1405, closes #1418
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