ServiceX Transformer that converts flat ROOT ntuples into columnwise data

Related tags

Deep Learningssl-hep
Overview

ServiceX_Uproot_Transformer

Badge

ServiceX Transformer that converts flat ROOT ntuples into columnwise data

Usage

You can invoke the transformer from the command line. For example:

> docker run --rm -it sslhep/servicex_func_adl_uproot_transformer:latest python transformer.py --help
usage: transformer.py [-h] [--brokerlist BROKERLIST] [--topic TOPIC]
                      [--chunks CHUNKS] [--tree TREE] [--attrs ATTR_NAMES]
                      [--path PATH] [--limit LIMIT]
                      [--result-destination {kafka,object-store,output-dir}]
                      [--output-dir OUTPUT_DIR]
                      [--result-format {arrow,parquet,root-file}]
                      [--max-message-size MAX_MESSAGE_SIZE]
                      [--rabbit-uri RABBIT_URI] [--request-id REQUEST_ID]

Uproot Transformer

optional arguments:
  -h, --help            show this help message and exit
  --brokerlist BROKERLIST
                        List of Kafka broker to connect to
  --topic TOPIC         Kafka topic to publish arrays to
  --chunks CHUNKS       Arrow Buffer Chunksize
  --tree TREE           Tree from which columns will be inspected
  --attrs ATTR_NAMES    List of attributes to extract
  --path PATH           Path to single Root file to transform
  --limit LIMIT         Max number of events to process
  --result-destination {kafka,object-store,output-dir}
                        kafka, object-store
  --output-dir OUTPUT_DIR
                        Local directory to output results
  --result-format {arrow,parquet,root-file}
                        arrow, parquet, root-file
  --max-message-size MAX_MESSAGE_SIZE
                        Max message size in megabytes
  --rabbit-uri RABBIT_URI
  --request-id REQUEST_ID
                        Request ID to read from queue

You will need an X509 proxy available as a mountable volume. The X509 Secret container can do using your credentials and cert:

docker run --rm \
    --mount type=bind,source=$HOME/.globus,readonly,target=/etc/grid-certs \
    --mount type=bind,source="$(pwd)"/secrets/secrets.txt,target=/servicex/secrets.txt \
    --mount type=volume,source=x509,target=/etc/grid-security \
    --name=x509-secrets sslhep/x509-secrets:latest

Development

 python3 -m pip install -r requirements.txt
 python3 -m pip install --index-url https://test.pypi.org/simple/ --no-deps servicex
Owner
Vis
Developer, Network Engineer, Copy Paste Expert. Mostly working on sort of defined networks (SDN). I pick the packets up and put them down
Vis
Source code for Transformer-based Multi-task Learning for Disaster Tweet Categorisation (UCD's participation in TREC-IS 2020A, 2020B and 2021A).

Source code for "UCD participation in TREC-IS 2020A, 2020B and 2021A". *** update at: 2021/05/25 This repo so far relates to the following work: Trans

Congcong Wang 4 Oct 19, 2021
Official PyTorch implementation of "Synthesis of Screentone Patterns of Manga Characters"

Manga Character Screentone Synthesis Official PyTorch implementation of "Synthesis of Screentone Patterns of Manga Characters" presented in IEEE ISM 2

Tsubota 2 Nov 20, 2021
Kaggle | 9th place single model solution for TGS Salt Identification Challenge

UNet for segmenting salt deposits from seismic images with PyTorch. General We, tugstugi and xuyuan, have participated in the Kaggle competition TGS S

Erdene-Ochir Tuguldur 276 Dec 20, 2022
Implementation of "With a Little Help from my Temporal Context: Multimodal Egocentric Action Recognition, BMVC, 2021" in PyTorch

Multimodal Temporal Context Network (MTCN) This repository implements the model proposed in the paper: Evangelos Kazakos, Jaesung Huh, Arsha Nagrani,

Evangelos Kazakos 13 Nov 24, 2022
This is the repository for The Machine Learning Workshops, published by AI DOJO

This is the repository for The Machine Learning Workshops, published by AI DOJO. It contains all the workshop's code with supporting project files necessary to work through the code.

AI Dojo 12 May 06, 2022
Pytorch Implementation of Auto-Compressing Subset Pruning for Semantic Image Segmentation

Pytorch Implementation of Auto-Compressing Subset Pruning for Semantic Image Segmentation Introduction ACoSP is an online pruning algorithm that compr

Merantix 8 Dec 07, 2022
Learn about Spice.ai with in-depth samples

Samples Learn about Spice.ai with in-depth samples ServerOps - Learn when to run server maintainance during periods of low load Gardener - Intelligent

Spice.ai 16 Mar 23, 2022
BirdCLEF 2021 - Birdcall Identification 4th place solution

BirdCLEF 2021 - Birdcall Identification 4th place solution My solution detail kaggle discussion Inference Notebook (best submission) Environment Use K

tattaka 42 Jan 02, 2023
[ICCV 2021] Deep Hough Voting for Robust Global Registration

Deep Hough Voting for Robust Global Registration, ICCV, 2021 Project Page | Paper | Video Deep Hough Voting for Robust Global Registration Junha Lee1,

57 Nov 28, 2022
Fast Soft Color Segmentation

Fast Soft Color Segmentation

3 Oct 29, 2022
Old Photo Restoration (Official PyTorch Implementation)

Bringing Old Photo Back to Life (CVPR 2020 oral)

Microsoft 11.3k Dec 30, 2022
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather

LiDAR fog simulation Created by Martin Hahner at the Computer Vision Lab of ETH Zurich. This is the official code release of the paper Fog Simulation

Martin Hahner 110 Dec 30, 2022
PyTorch implementation of deep GRAph Contrastive rEpresentation learning (GRACE).

GRACE The official PyTorch implementation of deep GRAph Contrastive rEpresentation learning (GRACE). For a thorough resource collection of self-superv

Big Data and Multi-modal Computing Group, CRIPAC 186 Dec 27, 2022
Official PyTorch implementation of Less is More: Pay Less Attention in Vision Transformers.

Less is More: Pay Less Attention in Vision Transformers Official PyTorch implementation of Less is More: Pay Less Attention in Vision Transformers. By

73 Jan 01, 2023
Official PyTorch implementation of PICCOLO: Point-Cloud Centric Omnidirectional Localization (ICCV 2021)

Official PyTorch implementation of PICCOLO: Point-Cloud Centric Omnidirectional Localization (ICCV 2021)

16 Nov 19, 2022
A program to recognize fruits on pictures or videos using yolov5

Yolov5 Fruits Detector Requirements Either Linux or Windows. We recommend Linux for better performance. Python 3.6+ and PyTorch 1.7+. Installation To

Fateme Zamanian 30 Jan 06, 2023
Repository features UNet inspired architecture used for segmenting lungs on chest X-Ray images

Lung Segmentation (2D) Repository features UNet inspired architecture used for segmenting lungs on chest X-Ray images. Demo See the application of the

163 Sep 21, 2022
[CVPR 2022 Oral] Crafting Better Contrastive Views for Siamese Representation Learning

Crafting Better Contrastive Views for Siamese Representation Learning (CVPR 2022 Oral) 2022-03-29: The paper was selected as a CVPR 2022 Oral paper! 2

249 Dec 28, 2022
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

GCNet for Object Detection By Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, Han Hu. This repo is a official implementation of "GCNet: Non-local Networ

Jerry Jiarui XU 1.1k Dec 29, 2022
Code for the TIP 2021 Paper "Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss"

PurNet Project for the TIP 2021 Paper "Salient Object Detection with Purificatory Mechanism and Structural Similarity Loss" Abstract Image-based salie

Jinming Su 4 Aug 25, 2022