commit | dcdb21eed45b3ad6655ebee475009b1888d81345 | [log] [tgz] |
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author | baunsgaard <baunsgaard@tugraz.at> | Mon Oct 26 14:13:46 2020 +0100 |
committer | baunsgaard <baunsgaard@tugraz.at> | Tue Oct 27 11:34:30 2020 +0100 |
tree | 73af4f0026f3714eed2818bcb350e85ef288b0e6 | |
parent | ad590ddebfa3328a4260c5320f957115cc203211 [diff] |
[SYSTEMDS-2705] Federated Writer This commit add the ability to save a federated matrix, But only one that has UID 0. If the UID is 0 on the remote then we know that it is a matrix, that have just been read from a file. Therefore we can without modifying the remote workers save our json directly, and read normally this file. Example: X = federated(addresses=...,ranges=...) write(X, "X_fed.json", format="federated") After this you can read the federated matrix, and it will allocate as federated using the workers. read("X_fed.json")
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.
Quick Start Install, Quick Start and Hello World
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