https://www.anaconda.com/download/success
Run once to initialize terminal for virtual environments:
conda init
conda config --add channels conda-forge
conda create --name=pyoccenv python=3.10
conda activate pyoccenv
conda install -c conda-forge pythonocc-core=7.8.1
conda deactivate
conda env remove --name pyoccenv
conda env list
run_local.bat
- Install Docker
- Docker Desktop: https://www.docker.com/
- Docker CLI: https://www.docker.com/products/cli/
-
Install Google Cloud CLI: https://cloud.google.com/sdk/docs/install
-
Authenticate with Google Cloud and log in to project:
gcloud auth login
gcloud config set project [PROJECT_NAME]
- Enable Google Cloud Run service:
gcloud services enable run.googleapis.com
- You may need to connect the project to a billing account to enable services like Google Cloud Run.
- Enable Artifact Registry and create repository: https://console.cloud.google.com/artifacts
gcloud services enable artifactregistry.googleapis.com
gcloud artifacts repositories create py-occ-server --repository-format=docker --location=us-central1
- Build container image:
docker build -t py-occ-server .
- Deploy on local: http://localhost:8000/
docker run --env-file .env -p 8000:8000 py-occ-server
- Give build a tag to register with Google Artifact Registry
docker tag py-occ-server us-central1-docker.pkg.dev/[PROJECT_NAME]/py-occ-server/py-occ-server:latest
- Push latest image to Google Artifact Registry
docker push us-central1-docker.pkg.dev/[PROJECT_NAME]/py-occ-server/py-occ-server:latest
- Deploy latest image on Google Cloud Run with these settings (keep remaining default):
gcloud run deploy py-occ-server --image=[LOCATION]-docker.pkg.dev/[PROJECT_ID]/py-occ-server/py-occ-server:latest --platform=managed --region=us-central1 --allow-unauthenticated --port=8000
Once deployed, API will be accessible at the URL specified on the service details page: https://console.cloud.google.com/run/detail/us-central1/py-occ-server/
To modify your deployment settings, click the Edit & Deploy New Version button on the service details page. Here you can modify the deployment settings including adding your environmental variables as described below.
The following variables must be in the environment. For local development, place them in a file called .env in the root of the project.
S3_BUCKET_NAME=[the name of the S3 bucket to save generated geometry]
AWS_ACCESS_KEY_ID=[your aws key]
AWS_SECRET_ACCESS_KEY=[your key secret]
S3_REGION=[the region where the S3 bucket is hosted, ex. 'us-east-1']
When deploying to the cloud, specify the environment variables as part of the employment. For production environment, an additional variable is needed:
ENV=PROD