commit | 0084f24dc02c325a6a6eb2fe5c823f97e0db5359 | [log] [tgz] |
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author | Mark Dokter <mark@dokter.cc> | Mon Feb 22 14:00:42 2021 +0100 |
committer | Mark Dokter <mark@dokter.cc> | Wed Feb 24 00:23:18 2021 +0100 |
tree | 72c7be8329f2dc28c486563dbdd113ba6f716ed6 | |
parent | 846d1e25156ddec7187be040fb8eb92f6ad7d8e1 [diff] |
[SYSTEMDS-2826] Sparse input support for CUDA codegen * Code template handling refactor * A few code snippets from the row template that the diff didn't cleanly separate (so things might not compile/run without the other commit (7bc6379d59a0c19d881fdac8229be64d880d30cc)). Intent was to split it in smaller chunks with moderate effort.
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
Issue Tracker Jira Dashboard
Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source