container.md

Docker

THIS IS A HIGHLY WIP STILL !!!

To build the image (which is quite heavy as of now), do the following

cd container
docker build . -t pybiscus:app.v0.5.0

If you are working under a proxy, you might need to add some argument for the buid

cd container
docker build \
--build-arg http_proxy=$HTTP_PROXY \
--build-arg https_proxy=$HTTPS_PROXY \
--build-arg no_proxy=$NO_PROXY \
. -t pybiscus:app.v0.5.0

Then, again only if you have to go through a proxy for internet access, then to download the data the different containers will need and internet access. So you need to set the file ~/.docker/config.json with the proxy config

For the client to be able to communicate with the server you need to add "server" to ne noProxy config.

{
        "proxies":{
                "default":{
                        "httpsProxy": "your_httpsProxy",
                        "httpProxy": "your_httpProxy",
                        "noProxy": "your_noProxy,server",
                }
        }
}

and voila! The docker image is aimed at running only the pybiscus_app itself. In order to facilitate the use of docker (which can be quite verbose), some scripts are available in container/scripts. To launch a local training, you just need to update container/scripts/launch_local_train.sh and container/configs/local_train.yml according to where are located your datasets and such. Then, simply run

bash container/scripts/launch_local_train.sh

It is as simple as running

docker run -t --gpus device=(some_device) -v "$(pwd)":/app/datasets pybiscus:app --help

to get the help of the app. The short version is, docker run -t pybiscus:app.v0.5.0 is equivalent to running pybiscus_app. As for the app itself, the docker image can launch either client, server or local components.

To launch a "true" Federated learning, you need first to create a docker network for the containers to communicate:

docker network create federated

then

bash container/scripts/launch_server.sh

followed by (in other terminal)

bash container/scripts/launch_client_1.sh

and

bash container/scripts/launch_client_2.sh