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
TensorFlow (v2.7.0) benchmark results on an M1 Macbook Air 2020 laptop (macOS Monterey v12.1).

M1-tensorflow-benchmark TensorFlow (v2.7.0) benchmark results on an M1 Macbook Air 2020 laptop (macOS Monterey v12.1). I was initially testing if Tens

particle 2 Jan 05, 2022
Reviving Iterative Training with Mask Guidance for Interactive Segmentation

This repository provides the source code for training and testing state-of-the-art click-based interactive segmentation models with the official PyTorch implementation

Visual Understanding Lab @ Samsung AI Center Moscow 406 Jan 01, 2023
ReferFormer - Official Implementation of ReferFormer

The official implementation of the paper: Language as Queries for Referring Video Object Segmentation Language as Queries for Referring Video Object S

Jonas Wu 232 Dec 29, 2022
Task-related Saliency Network For Few-shot learning

Task-related Saliency Network For Few-shot learning This is an official implementation in Tensorflow of TRSN. Abstract An essential cue of human wisdo

1 Nov 18, 2021
Simulating Sycamore quantum circuits classically using tensor network algorithm.

Simulating the Sycamore quantum supremacy circuit This repo contains data we have obtained in simulating the Sycamore quantum supremacy circuits with

Feng Pan 46 Nov 17, 2022
A large dataset of 100k Google Satellite and matching Map images, resembling pix2pix's Google Maps dataset.

Larger Google Sat2Map dataset This dataset extends the aerial ⟷ Maps dataset used in pix2pix (Isola et al., CVPR17). The provide script download_sat2m

34 Dec 28, 2022
Real-time 3D multi-person detection made easy with OpenPose and the ZED

OpenPose ZED This sample show how to simply use the ZED with OpenPose, the deep learning framework that detects the skeleton from a single 2D image. T

blanktec 5 Nov 06, 2020
[ArXiv 2021] One-Shot Generative Domain Adaptation

GenDA - One-Shot Generative Domain Adaptation One-Shot Generative Domain Adaptation Ceyuan Yang*, Yujun Shen*, Zhiyi Zhang, Yinghao Xu, Jiapeng Zhu, Z

GenForce: May Generative Force Be with You 46 Dec 19, 2022
Creating a custom CNN hypertunned architeture for the Fashion MNIST dataset with Python, Keras and Tensorflow.

custom-cnn-fashion-mnist Creating a custom CNN hypertunned architeture for the Fashion MNIST dataset with Python, Keras and Tensorflow. The following

Danielle Almeida 1 Mar 05, 2022
Effective Use of Transformer Networks for Entity Tracking

Effective Use of Transformer Networks for Entity Tracking (EMNLP19) This is a PyTorch implementation of our EMNLP paper on the effectiveness of pre-tr

5 Nov 06, 2021
The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering"

The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering"

Ren Yurui 261 Jan 09, 2023
Source code for TACL paper "KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation".

KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation Source code for TACL 2021 paper KEPLER: A Unified Model for Kn

THU-KEG 138 Dec 22, 2022
Multi-Objective Reinforced Active Learning

Multi-Objective Reinforced Active Learning Dependencies wandb tqdm pytorch = 1.7.0 numpy = 1.20.0 scipy = 1.1.0 pycolab == 1.2 Weights and Biases O

Markus Peschl 6 Nov 19, 2022
Analyzing basic network responses to novel classes

novelty-detection Analyzing how AlexNet responds to novel classes with varying degrees of similarity to pretrained classes from ImageNet. If you find

Noam Eshed 34 Oct 02, 2022
Official PyTorch code for the paper: "Point-Based Modeling of Human Clothing" (ICCV 2021)

Point-Based Modeling of Human Clothing Paper | Project page | Video This is an official PyTorch code repository of the paper "Point-Based Modeling of

Visual Understanding Lab @ Samsung AI Center Moscow 64 Nov 22, 2022
The Face Mask recognition system uses AI technology to detect the person with or without a mask.

Face Mask Detection Face Mask Detection system built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect

Rohan Kasabe 4 Apr 05, 2022
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

ELECTRA Introduction ELECTRA is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using

Google Research 2.1k Dec 28, 2022
OpenAi's gym environment wrapper to vectorize them with Ray

Ray Vector Environment Wrapper You would like to use Ray to vectorize your environment but you don't want to use RLLib ? You came to the right place !

Pierre TASSEL 15 Nov 10, 2022
PyTorch implementations of Top-N recommendation, collaborative filtering recommenders.

PyTorch implementations of Top-N recommendation, collaborative filtering recommenders.

Yoonki Jeong 129 Dec 22, 2022
[CVPR2021 Oral] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

UP-DETR: Unsupervised Pre-training for Object Detection with Transformers This is the official PyTorch implementation and models for UP-DETR paper: @a

dddzg 430 Dec 23, 2022