commit | 7dcf6fe424e80901852785a4a0bf7065c15973bb | [log] [tgz] |
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author | Mark Dokter <mark@dokter.cc> | Thu Jun 17 01:42:42 2021 +0200 |
committer | Mark Dokter <mark@dokter.cc> | Thu Jun 17 01:42:42 2021 +0200 |
tree | 064f14b5a99a2138a33abaaa4a6352fef4e2d7f6 | |
parent | a17b5114cef70ab92f8f461326e294b2fda0614f [diff] |
[MINOR] CUDA 10.2 PTX and spoof cuda helper binaries (Win/Lin/64) Build command for your reference: rm -rf target/build-cuda ; cmake -S src/main/cuda -B target/build-cuda -G "Ninja Multi-Config" ; cmake --build target/build-cuda/ --target install --config Release
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