Archilyse Deep Learning

Repository for the automatic inference of models from raw floor plans

Development

Requirements

• Python >= 3.10.5
• Google-Cloud-CLI

Setup

1. Install dev requirements:

   pip install -r requirements.txt

2. Login to gcloud to make sure you have access to the DVC remote repository

   gcloud auth application-default login

3. Pull the data (optional)

   dvc pull

4. Place the GitHub deploy key in docker/secrets/github.key and make sure it has right permissions chmod 400 docker/secrets/github.key
5. Place the gcloud service account credentials in docker/secrets/gce_service_account_credentials.json

UI Demo

Resources needed

1. You'll need a file named as the set variable ML_IMAGES_BUCKET_CREDENTIALS_FILE in docker/.env. This file should contain the credentials for the service account that has access to the images bucket.
2. Download necessary resources:

   make update_resources
   

Running the UI locally

1. Install Node 16.18.0, for example, with nvm:

   nvm use 16.18.0
   

2. Install dev requirements:

   make install_demo_ui_requirements
   

3. Run it locally:

   make run_demo_ui_locally
   

Running workers and api in docker

1. download necessary resources:

   make update_resources
   

2. run the workers, api and router images (router might be needed to be commented out if you are running the UI locally)

   make docker_build
   make docker_up
   

Authentication

Authentication is set up using Auth0 In order for authentication to work, you need to create a copy of .env.sample file located in the demo/ui directory into .env and fill with proper values.

Tests

Basic tests can be run with:

make tests_demo_ui

Training Workflow:

Changing docker image

1. Make changes to entrypoint etc.
2. Bump DVC_IMAGE_VERSION in .env
3. Run make dvc_detectron_docker_push

Running a training

Run the makefile recipe make remote_training and you will be prompted for the relevant parameters. Alternatively you can use the Vertex AI Interface by creating new trainings. Choose the dvc_detectron image as custom container. Once the training is completed a new branch will be created (with a currently quite cryptic branch name) and the experiment will be available on DagsHub. For changing parameters we have two options:

Option 1: Custom Commit

1. Change the config of the model under conf/detectron2/remote.yaml
2. commit your changes and push to GitHub
3. on Vertex AI run with flags --train <COMMIT_HASH>

Option 2: Command line arguments

On Vertex AI, you can provide the parameters you want to override via the -S flag, see Hydra Parameter Override and the DVC documentation.

--train_detectron <BASE_COMMIT_HASH>
-S "conf/detectron2/remote.yaml:SOLVER.MAX_ITER=100"
-S "conf/detectron2/remote.yaml:+INPUT.MAX_SIZE_TRAIN=800"
-S "conf/detectron2/remote.yaml:~DATALOADER.SAMPLER_TRAIN"

Updating the dataset

1. Change the config under conf/dataset/default.yaml
2. commit your changes and push to GitHub
3. on vertex AI / locally run with flags --dataset <COMMIT_HASH>

GitHub Actions

In order to run the GitHub actions some variables need to be set:

ML_IMAGES_SA_BASE64:

base64 encoded service account credentials for the ML images project. In order to get it you can get the service account file (json) and run:

cat <FILE_NAME>.json | base64 -w 0 > temp.base_64
gh secret set ML_IMAGES_SA_BASE64 < temp.base_64
rm temp.base_64

This is using GitHub cli, but you can also do it manually in the GitHub secrets page.

DEMO_UI_ENV_BASE64:

Similar as in the previous step. We need to have the .env file located in the demo/ui directory. Then we can run:

cat demo/ui/.env | base64 -w 0 > temp.base_64
gh secret set DEMO_UI_ENV_BASE64 < temp.base_64
rm temp.base_64