.env.sample

#           /\              Make sure to
#          /  \             replicate the ones
#         / || \            you need on Cloud Run
#        /  ||  \           also in your
#       /   ||   \          .env.yaml file
#      /    ..    \         so that it also works
#     /____________\        on Cloud Run




# Storage locations (only if you use it)
# LOCAL_DATA_PATH=raw_data
# LOCAL_REGISTRY_PATH=models

# Data source (only if you use it)
# Replace with 'cloud' to source from cloud (depending on how you write your functions)
# DATA_SOURCE=local
# MODEL_TARGET=local

# GCP Project
GCP_PROJECT=project-id-123456
GCP_REGION=europe-west1

# On GCP console, create a new service account for your project, create a new key,
# save it on your local drive (make sure its .gitignored!), and add the path to it here
# GOOGLE_APPLICATION_CREDENTIALS=credentials.json

# Cloud Storage (only if you use it)
# BUCKET_NAME=your-bucket-name
# Folder in your bucket where raw_data is stored
# RAW_DATA_LOCATION=raw_data
# Folder in your bucket where your model is stored
# MODELS_LOCATION=models

# BigQuery (only if you use it)
# BQ_REGION=EU
# DATASET=your_dataset

# Compute Engine (only if you use it)
# INSTANCE=instance_name

# Model Lifecycle (only if you use it)
# MLFLOW_TRACKING_URI=https://mlflow.lewagon.ai
# MLFLOW_EXPERIMENT=project_name_experiment_<user.github_nickname>
# MLFLOW_MODEL_NAME=project_name_<user.github_nickname>

# Docker
DOCKER_LOCAL_PORT=8080
DOCKER_REPO_NAME=docker
DOCKER_IMAGE_NAME=api