Codabench

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=<desired 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:pass@host:port/vhost-gpu
  is_gpu: true
  docker_image: codalab/competitions-v2-compute-worker:nvidia

comp-cpu:
  hosts:
    - [email protected]
  broker_url: pyamqp://user:pass@host: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