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SURFsara (@sara-nl)

sara-nl

Stars
478
Global Org. Rank 24,516 (Top 8 %)
Registered over 12 years ago
Most used languages

Python
34.2 %

Jupyter Notebook
24.1 %

Shell
12.7 %

C
7.6 %

JavaScript
6.3 %

C++
3.8 %

Cuda 2.5 %

HTML
2.5 %

Roff 1.3 %

CSS
1.3 %

Fortran
1.3 %

Scala
1.3 %

Perl
1.3 %
Location 🇳🇱 Netherlands
Country Total Rank 1,253
Country Ranking

Cuda 12

Roff 20

Fortran
37

Jupyter Notebook
100

Scala
166

Perl
225

Python
379

Shell
402

JavaScript
528

C
587

C++
664

CSS
1,172

HTML
1,770

js-webdav-client

A low-level webDAV client library written in/for JavaScript

3D-VQ-VAE-2

3D VQ-VAE-2 for high-resolution CT scan synthesis

attention-sampling-pytorch

This is a PyTorch implementation of the paper: "Processing Megapixel Images with Deep Attention-Sampling Models".

multi-scale-tools

2D-VQ-AE-2

2D Vector-Quantized Auto-Encoder for compression of Whole-Slide Images in Histopathology

eth-oam

Scripts and templates related to Ethernet OAM

DDLBench

Distributed Deep Learning Benchmark Suite

Parallel-and-GPU-programming-in-Python

Jupyter Notebook

prace_jacobi

2D Jacobi solver paralleled with MPI and OpenMP (template for the PRACE course)

BeeHub

SURFsara's BeeHub File Sharing Service

saraGAN

SURFsara GAN library

PraceHPML2021

Study materials (code, slides) for the PRACE High Performance Machine Learning Workshop 2021

Jupyter Notebook

iRODS-RDM-Compute-training

Course material to teach the integration of RDM and compute workflows.

data-exchange

MLonHPC_May2023

Contains the material for the Machine Learning on HPC systems course on 16-05-2023

Jupyter Notebook

Covid19-HPML

prace_topologies

MPI topologies, METIS decomposition, unbalanced workload (template for the PRACE course)

SURF-segmentation

Jupyter Notebook

elixir-dl

Material for the ELIXIR Excelerate Deep Learning for Life Sciences workshop

Jupyter Notebook

PraceHPML2020

Jupyter Notebook

UvA_ML_2021

Course materials for the UvA ML introduction course

Jupyter Notebook

jupyter-bigdata-notebooks

Notebooks for the big data group

Jupyter Notebook

hathi-client

Client software and configuration for the Hathi Hadoop cluster

PRACE-MPI-OpenMP

Performance-Analysis-Framework-guide

Cook book for using several performance analysis and measurement packages on Cartesius (Dutch National Supercomputer Center)

Packed-Data-Formats

SURFace

Beneath the SURFace: An MRI-like View into the Life of a 21st Centry Datacenter

Jupyter Notebook

jupyter-bigdata-docker

Dockerfiles for notebook environments

QuantumComputing

Tools and tutorials for quantum computing

Jupyter Notebook

IntroHPC_CBM

Material for the course Introduction to HPC

course-module-snellius-fs

Course module materials for Snellius filesystems

griddocs

Grid documentation

Intro-to-HPC

PRACE-advanced-dl

clouddocs

HPC Cloud documentation

singularity-course

Beginner's course on Singularity containers

surfsara-dmf-irods-client

surfsara-dmf-irods-client

VU_HPC

Training material for the VU HPC course

Jupyter Notebook

automl

GridScripts

PraceHPML2022

Course material for Prace Course "Introduction to High Performance Machine learning"

Jupyter Notebook

prace_advanced_mpi_openmp

Slides for the "MPI and OpenMP in Scientific Software Development" course given by SURF

ISC-2024-EAR-tutorial

Energy Management and Optimization with EAR

dmp_feature_matrix

Data Management platform features and evaluation criterion

matlabmpi

PRACE-intro-dl

Jupyter Notebook

jupyter

Jupyter documentation

irods-ansible

HPML-course-materials

Contains all course materials from the HPML group

Jupyter Notebook

Omnimatch

3D template matching in tomographic data

SpiderScripts

Welcome! These are a few scripts that we use at SURFsara to work with Spider.

research-data-exchange

energy-efficient-computing

Training material for energy efficent computing

computer-aided-retrosynthesis

This repository contains code belonging to the work done on computer aided retrosynthesis within the SURF HPML team.

Tue_HPDL_2024

Contains the material for the TU/e Deep Learning on HPC course on 16-17 April 2024.

Jupyter Notebook

dCachedocs

Here you find documentation about dCache at SURFsara

researchdrivedocs

spparks_hpc

Running SPPARKS (Stochastic Parallel Particle Kinetic Simulator) on HPC system

spiderdocs

Official repository for the SURF Spider platform user documentation

color-information

iRODS-RDM-HPC-course

Course about basic iRODS icommands in a data processing pipelines

sdrdocs

Documentation for the Data Repository of SURF

mpi-examples

A collection of mpi examples

QuantizedGAN

AngleGAN quantization

PRACE-GPU-Portability

OSSC_test

Documentation and issue reporting for the OSSC test phase

workshop-template

SURF workshop template adapted from the Carpentries

dsdocs

Data Services Documentation

picas-profile

copyright-ml

backupscripts

Some scripts to back up systems with Duplicity.

objectstoredocs

Swift and S3 documentation

surfsara-json-parser-cpp

JSON parser, path expression and object update in C++

swiftdocs

Here you find documentation about the SWIFT data storage service

WSI_Captioning

Whole Slide Image Caption Generation from Pre-trained CLIP latents for the ExaMode project.

PerlTopos

A Topos client library in Perl

GoodPracticesML

Jupyter Notebook

ska2grid

SKA processing on the SURFsara Grid as part of the AENEAS project

parallel-image-classification

opennaas-dc-gui

Example interface for operating the OpenNaaS REST interface.

shell-scripts

A place to put SURFsara company wide shell scripts. For questions or access please contact onnozweers.

ML_Erasmus

Jupyter Notebook

dcgmi_top

rdm-training-datasupporters-april-2024

Research Data Management training for data supporters

MLonHPC_2day_Okt2023

Contains the material for the 2-day High Performance Deep Learning course on 05-09-2023 & 06-09-2023

Jupyter Notebook

jupyter-bigdata

[deprecated] Jupyter notebook environment for the big data workshops

SURFBoard

Large-scale HPC infrastructures are enablers for scientific research in many domains. The recent advances in machine learning (ML)have led to an ever-increasing demand for computation power, as well as the design of complex operational workflows. Understanding the performance and efficiency of these workflows is key to productivity, knowledge and model sharing, and energy efficiency. We propose the design of a container-based framework for reproducible performance analysis.

Jupyter Notebook

SupercomputingOne