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config.yaml
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config.yaml
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---
### general ###
#repository: n02291 # The name of the docker Repository to use.
#registry: https://index.docker.io/v1/ # The URL of the Docker Registry to use.
#runtime_image_name: ml_template_runtime_env # The image name of the runtime image which Jenkins uses to run used within the Jenkins pipelines
#runtime_image_tag: 0.2.0
#production_image_name: ml_template_production_env # The name you want the final image to have
#production_image_tag: 0.0.1
#docker_socket_path: "/var/run/docker.sock" # default linux path
#model_path: "model_bin" # the folder where the trained model should be stored
#model_name: "model.pkl" # the name the trained model will have (.pkl is mandatory)
#github_project_name: "machine-learning-microservice-template"
prediction_test_case_path: tests/prediction/example_classification1.json # the path incl. the filename to a given dummy request to test your model
### Jenkins ###
#docker_credentials_id: docker # The ID of the User used to access the Docker Registry. The credentials of this user must be provided by Jenkins!
#scm_credentials_id: github # The ID of the User used to access your SCM. The credentials of this user must be provided by Jenkins!
### Helm ###
# Deployment
#deployment_name: ml-microservice # how should the k8s deployment be named
#deployment_label: ml-microservice # what label you want to give to the deployment
#container_name: ml-microservice # How should the containers be named within k8s
#image_name: prod_ml_microservice
#image_tag: 0.0.1
#container_port: 8000 # the port which should be exposed by the container (the port where the webservice is running)
#replicas: 2
# Service
#service_name: ml-service
#service_type: NodePort
#service_port: 8000 # the port where the app is running
#service_node_port: 30000 # the port which is exposed publicly
### Logging ###
# MongoDB
#enable_mongodb_logging: True # if true, you have to provide the following env parameters
# mongo_db_name: ml-service # each service gets it's own mongodb Database, how should this db be named?
# mongo_db_url: localhost
# mongo_db_port: 27017
# mongodb_logging_collection_name: prediction_logs
# timezone: Europe/Berlin # for possible values see: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568
# EFK-Stack
#enable_fluentd_logging: True # if you set this to true, you prob. also want to set ID_NAME, set also the following env params
# fluentd_url: localhost # where is fluentd running?
# fluentd_port: 24224
# fluentd_base_log_name: machine-learning-microservice-template
# fluentd_log_n_best: 2 # if predict_proba, logs the n classes with highest probabilities
### API ###
#ID_NAME: ID # if set, an additional ID Parameter, which is used for logging, can be passed during requests, can be empty
### Source ###
######
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