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
CLOOB training (JAX) and inference (JAX and PyTorch)

cloob-training Pretrained models There are two pretrained CLOOB models in this repo at the moment, a 16 epoch and a 32 epoch ViT-B/16 checkpoint train

Katherine Crowson 64 Nov 27, 2022
End-to-end face detection, cropping, norm estimation, and landmark detection in a single onnx model

onnx-facial-lmk-detector End-to-end face detection, cropping, norm estimation, and landmark detection in a single onnx model, model.onnx. Demo You can

atksh 42 Dec 30, 2022
Automatic Data-Regularized Actor-Critic (Auto-DrAC)

Auto-DrAC: Automatic Data-Regularized Actor-Critic This is a PyTorch implementation of the methods proposed in Automatic Data Augmentation for General

89 Dec 13, 2022
Simple object detection app with streamlit

object-detection-app Simple object detection app with streamlit. Upload an image and perform object detection. Adjust the confidence threshold to see

Robin Cole 68 Jan 02, 2023
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking

StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking Datasets You can download datasets that have been pre-pr

25 May 29, 2022
Colab notebook for openai/glide-text2im.

GLIDE text2im on Colab This repository provides a Colab notebook to produce images conditioned on text prompts with GLIDE [1]. Usage Run text2im.ipynb

Wok 19 Oct 19, 2022
Denoising Normalizing Flow

Denoising Normalizing Flow Christian Horvat and Jean-Pascal Pfister 2021 We combine Normalizing Flows (NFs) and Denoising Auto Encoder (DAE) by introd

CHrvt 17 Oct 15, 2022
Details about the wide minima density hypothesis and metrics to compute width of a minima

wide-minima-density-hypothesis Details about the wide minima density hypothesis and metrics to compute width of a minima This repo presents the wide m

Nikhil Iyer 9 Dec 27, 2022
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models. Hyperactive: is very easy to lear

Simon Blanke 422 Jan 04, 2023
Official repository of the paper "A Variational Approximation for Analyzing the Dynamics of Panel Data". Mixed Effect Neural ODE. UAI 2021.

Official repository of the paper (UAI 2021) "A Variational Approximation for Analyzing the Dynamics of Panel Data", Mixed Effect Neural ODE. Panel dat

Jurijs Nazarovs 7 Nov 26, 2022
A "gym" style toolkit for building lightweight Neural Architecture Search systems

A "gym" style toolkit for building lightweight Neural Architecture Search systems

Jack Turner 12 Nov 05, 2022
The description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts.

FMFCC-A This project is the description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts. The FMFCC-A dataset is shared through BaiduCl

18 Dec 24, 2022
Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR 2018).

Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing (CVPR2018) By Zilong Huang, Xinggang Wang, Jiasi Wang, Wenyu Liu and J

Zilong Huang 245 Dec 13, 2022
A New Open-Source Off-road Environment for Benchmark Generalization of Autonomous Driving

A New Open-Source Off-road Environment for Benchmark Generalization of Autonomous Driving Isaac Han, Dong-Hyeok Park, and Kyung-Joong Kim IEEE Access

13 Dec 27, 2022
Source code of our work: "Benchmarking Deep Models for Salient Object Detection"

SALOD Source code of our work: "Benchmarking Deep Models for Salient Object Detection". In this works, we propose a new benchmark for SALient Object D

22 Dec 30, 2022
Official implementation of UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation

UTNet (Accepted at MICCAI 2021) Official implementation of UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation Introduction Transf

110 Jan 01, 2023
BiSeNet based on pytorch

BiSeNet BiSeNet based on pytorch 0.4.1 and python 3.6 Dataset Download CamVid dataset from Google Drive or Baidu Yun(6xw4). Pretrained model Download

367 Dec 26, 2022
Instance-wise Feature Importance in Time (FIT)

Instance-wise Feature Importance in Time (FIT) FIT is a framework for explaining time series perdiction models, by assigning feature importance to eve

Sana 46 Dec 25, 2022
Do you like Quick, Draw? Well what if you could train/predict doodles drawn inside Streamlit? Also draws lines, circles and boxes over background images for annotation.

Streamlit - Drawable Canvas Streamlit component which provides a sketching canvas using Fabric.js. Features Draw freely, lines, circles, boxes and pol

Fanilo Andrianasolo 325 Dec 28, 2022
Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation [3DV 2021 Oral]

Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation [3DV 2021 Oral] Learning to Disambiguate Strongly In

Zicong Fan 40 Dec 22, 2022