competitions-v2

Last update: Dec 02, 2022

Related tags

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

competitions-v2

Related tags

Overview

Codabench

(formerly Codalab Competitions v2)

Installation

Running tests

Example competitions

v2 test data

v1.5 legacy test data

Other Codalab Competition examples

Building compute worker

Worker setup

Start CPU worker

Start GPU worker

Worker management

Owner

CodaLab

Code and models for "Pano3D: A Holistic Benchmark and a Solid Baseline for 360 Depth Estimation", OmniCV Workshop @ CVPR21.

Official Implementation for HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing

PyTorch implementation of "Dataset Knowledge Transfer for Class-Incremental Learning Without Memory" (WACV2022)

Projecting interval uncertainty through the discrete Fourier transform

PointCNN: Convolution On X-Transformed Points (NeurIPS 2018)

Next-gen Rowhammer fuzzer that uses non-uniform, frequency-based patterns.

Neural network chess engine trained on Gary Kasparov's games.

Explanatory Learning: Beyond Empiricism in Neural Networks

How Effective is Incongruity? Implications for Code-mix Sarcasm Detection.

A curated list and survey of awesome Vision Transformers.

Arabic Car License Recognition. A solution to the kaggle competition Machathon 3.0.

Event-forecasting - Event Forecasting Algorithms With Python

A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset.

Face-Recognition-Attendence-System - This face recognition Attendence system using Python

Code accompanying the paper Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs (Chen et al., CVPR 2020, Oral).

Code for the paper Learning the Predictability of the Future

Python Implementation of Chess Playing AI with variable difficulty

Deep learning with TensorFlow and earth observation data.

RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids

Plato: A New Framework for Federated Learning Research