competitions-v2

Overview

Codabench

(formerly Codalab Competitions v2)

Installation

$ cp .env_sample .env
$ docker-compose up -d
$ docker-compose exec django ./manage.py migrate
$ docker-compose exec django ./manage.py generate_data
$ docker-compose exec django ./manage.py collectstatic --noinput

You can now login as username "admin" with password "admin" at http://localhost:8000

If you ever need to reset the database, use the script ./reset_db.sh

Running tests

# Non "end to end tests"
$ docker-compose exec django py.test -m "not e2e"

# "End to end tests" (a shell script to launch a selenium docker container)
$ ./run_selenium_tests.sh

# If you are on Mac OSX it is easy to watch these tests, no need to install
# anything just do:
$ open vnc://0.0.0.0:5900

# And login with password "secret"

Example competitions

The repo comes with a couple examples that are used during tests:

v2 test data

src/tests/functional/test_files/submission.zip
src/tests/functional/test_files/competition.zip

v1.5 legacy test data

src/tests/functional/test_files/submission15.zip
src/tests/functional/test_files/competition15.zip

Other Codalab Competition examples

https://github.com/codalab/competition-examples/tree/master/v2/

Building compute worker

To build the normal image:

docker build -t codalab/competitions-v2-compute-worker:latest -f Dockerfile.compute_worker .

To build the GPU version:

docker build -t codalab/competitions-v2-compute-worker:nvidia -f Dockerfile.compute_worker_gpu .

Updating the image

docker push codalab/competitions-v2-compute-worker

Worker setup

# install docker
$ curl https://get.docker.com | sudo sh
$ sudo usermod -aG docker $USER

# >>> reconnect <<<

Start CPU worker

Make a file .env and put this in it:

# Queue URL
BROKER_URL=
   
    

# Location to store submissions/cache -- absolute path!
HOST_DIRECTORY=/your/path/to/codabench/storage

# If SSL is enabled, then uncomment the following line
#BROKER_USE_SSL=True

   

NOTE /your/path/to/codabench -- this path needs to be volumed into /codabench on the worker, as you can see below.

$ docker run \
    -v /your/path/to/codabench/storage:/codabench \
    -v /var/run/docker.sock:/var/run/docker.sock \
    -d \
    --env-file .env \
    --restart unless-stopped \
    --log-opt max-size=50m \
    --log-opt max-file=3 \
    codalab/competitions-v2-compute-worker:latest 

Start GPU worker

nvidia installation instructions

$ nvidia-docker run \
    -v /your/path/to/codabench/storage:/codabench \
    -v /var/run/docker.sock:/var/run/docker.sock \
    -v /var/lib/nvidia-docker/nvidia-docker.sock:/var/lib/nvidia-docker/nvidia-docker.sock \
    -d \
    --env-file .env \
    --restart unless-stopped \
    --log-opt max-size=50m \
    --log-opt max-file=3 \
    codalab/competitions-v2-compute-worker:nvidia 

Worker management

Outside of docker containers install Fabric like so:

pip install fab-classic==1.17.0

Create a server_config.yaml in the root of this repository using:

cp server_config_sample.yaml server_config.yaml

Below is an example server_config.yaml that defines 2 roles comp-gpu and comp-cpu, one with gpu style workers (is_gpu and the nvidia docker_image) and one with cpu style workers

comp-gpu:
  hosts:
    - [email protected]
    - [email protected]
  broker_url: pyamqp://user:[email protected]:port/vhost-gpu
  is_gpu: true
  docker_image: codalab/competitions-v2-compute-worker:nvidia

comp-cpu:
  hosts:
    - [email protected]
  broker_url: pyamqp://user:[email protected]:port/vhost-cpu
  is_gpu: false
  docker_image: codalab/competitions-v2-compute-worker:latest

You can of course create your own docker_image and specify it here.

You can execute commands against a role:

❯ fab -R comp-gpu status
..
[[email protected]] out: CONTAINER ID        IMAGE                                           COMMAND                  CREATED             STATUS              PORTS               NAMES
[[email protected]] out: 1d318268bee1        codalab/competitions-v2-compute-worker:nvidia   "/bin/sh -c 'celery …"   2 hours ago         Up 2 hours                              hardcore_greider
..

❯ fab -R comp-gpu update
..
(updates workers)

See available commands with fab -l

Owner
CodaLab
CodaLab
Official Implementation of PCT

Official Implementation of PCT Prerequisites python == 3.8.5 Please make sure you have the following libraries installed: numpy torch=1.4.0 torchvisi

32 Nov 21, 2022
Code for generating the figures in the paper "Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?"

Code for running simulations for the paper "Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Lin

Matthew Farrell 1 Nov 22, 2022
exponential adaptive pooling for PyTorch

AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling Abstract Pooling layers are essential building blocks of Convolutional Ne

Alexandros Stergiou 55 Jan 04, 2023
Extracting and filtering paraphrases by bridging natural language inference and paraphrasing

nli2paraphrases Source code repository accompanying the preprint Extracting and filtering paraphrases by bridging natural language inference and parap

Matej Klemen 1 Mar 09, 2022
This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.

Multimodal Deep Learning 🎆 🎆 🎆 Announcing the multimodal deep learning repository that contains implementation of various deep learning-based model

Deep Cognition and Language Research (DeCLaRe) Lab 398 Dec 30, 2022
Official Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021)

TDEER 🦌 🦒 Official Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021) Overview TDEE

33 Dec 23, 2022
Official PyTorch Implementation of paper "Deep 3D Mask Volume for View Synthesis of Dynamic Scenes", ICCV 2021.

Deep 3D Mask Volume for View Synthesis of Dynamic Scenes Official PyTorch Implementation of paper "Deep 3D Mask Volume for View Synthesis of Dynamic S

Ken Lin 17 Oct 12, 2022
3D-Reconstruction 基于深度学习方法的单目多视图三维重建

基于深度学习方法的单目多视图三维重建 Part I 三维重建 代码:Part1 技术文档:[Markdown] [PDF] 原始图像:Original Images 点云结果:Point Cloud Results-1

HMT_Curo 19 Dec 26, 2022
Code for ICCV2021 paper PARE: Part Attention Regressor for 3D Human Body Estimation

PARE: Part Attention Regressor for 3D Human Body Estimation [ICCV 2021] PARE: Part Attention Regressor for 3D Human Body Estimation, Muhammed Kocabas,

Muhammed Kocabas 277 Jan 03, 2023
Bayesian Optimization using GPflow

Note: This package is for use with GPFlow 1. For Bayesian optimization using GPFlow 2 please see Trieste, a joint effort with Secondmind. GPflowOpt GP

GPflow 257 Dec 26, 2022
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

LSTM Neural Network for Time Series Prediction LSTM built using the Keras Python package to predict time series steps and sequences. Includes sine wav

Jakob Aungiers 4.1k Jan 02, 2023
Compares various time-series feature sets on computational performance, within-set structure, and between-set relationships.

feature-set-comp Compares various time-series feature sets on computational performance, within-set structure, and between-set relationships. Reposito

Trent Henderson 7 May 25, 2022
Tensorflow 2 Object Detection API kurulumu, GPU desteği, custom model hazırlama

Tensorflow 2 Object Detection API Bu tutorial, TensorFlow 2.x'in kararlı sürümü olan TensorFlow 2.3'ye yöneliktir. Bu, görüntülerde / videoda nesne a

46 Nov 20, 2022
Py-FEAT: Python Facial Expression Analysis Toolbox

Py-FEAT is a suite for facial expressions (FEX) research written in Python. This package includes tools to detect faces, extract emotional facial expressions (e.g., happiness, sadness, anger), facial

Computational Social Affective Neuroscience Laboratory 147 Jan 06, 2023
A PyTorch based deep learning library for drug pair scoring.

Documentation | External Resources | Datasets | Examples ChemicalX is a deep learning library for drug-drug interaction, polypharmacy side effect and

AstraZeneca 597 Dec 30, 2022
Pansharpening by convolutional neural networks in the full resolution framework

Z-PNN: Zoom Pansharpening Neural Network Pansharpening by convolutional neural networks in the full resolution framework is a deep learning method for

20 Nov 24, 2022
A Small and Easy approach to the BraTS2020 dataset (2D Segmentation)

BraTS2020 A Light & Scalable Solution to BraTS2020 | Medical Brain Tumor Segmentation (2D Segmentation) Developed the segmentation models for segregat

Gunjan Haldar 0 Jan 19, 2022
TRIQ implementation

TRIQ Implementation TF-Keras implementation of TRIQ as described in Transformer for Image Quality Assessment. Installation Clone this repository. Inst

Junyong You 115 Dec 30, 2022
Prometheus exporter for Cisco Unified Computing System (UCS) Manager

prometheus-ucs-exporter Overview Use metrics from the UCS API to export relevant metrics to Prometheus This repository is a fork of Drew Stinnett's or

Marshall Wace 6 Nov 07, 2022
Official code of "R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network."

R2RNet Official code of "R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network." Jiang Hai, Zhu Xuan, Ren Yang, Yutong Hao, Fengzhu

77 Dec 24, 2022