pyinfra/config.yaml
Julius Unverfehrt 4507bbb4e6 init
2022-02-14 12:05:40 +01:00

58 lines
3.4 KiB
YAML

rabbitmq:
host: $RABBITMQ_HOST|localhost # RabbitMQ host address
port: $RABBITMQ_PORT|5672 # RabbitMQ host port
user: $RABBITMQ_USERNAME|user # RabbitMQ username
password: $RABBITMQ_PASSWORD|bitnami # RabbitMQ password
heartbeat: $RABBITMQ_HEARTBEAT|7200 # Controls AMQP heartbeat timeout in seconds
queues: # Names of queues for...
input: input_queue # requests to service
output: response_queue # responses by service
dead_letter: dead_letter_queue # messages that failed to process
prefetch_count: 1
retry: # Controls retry behaviour for messages the processing of which failed
enabled: $RETRY|True # Toggles retry behaviour
max_attempts: $MAX_ATTEMPTS|3 # Number of times a message may fail before being published to dead letter queue
# TODO: implement
max_interval: $MAX_INTERVAL|15000 # Increase timeout for a message every time it fails to a maximum of this value
minio:
host: $STORAGE_ENDPOINT|localhost # MinIO host address
port: $STORAGE_PORT|9000 # MinIO host port
user: $STORAGE_KEY|root # MinIO user name
password: $STORAGE_SECRET|password # MinIO user password
bucket: $STORAGE_BUCKET_NAME|redaction # MinIO bucket
azure_blob_storage:
connection_string: $STORAGE_AZURECONNECTIONSTRING|"DefaultEndpointsProtocol=https;AccountName=iqserdevelopment;AccountKey=4imAbV9PYXaztSOMpIyAClg88bAZCXuXMGJG0GA1eIBpdh2PlnFGoRBnKqLy2YZUSTmZ3wJfC7tzfHtuC6FEhQ==;EndpointSuffix=core.windows.net"
container: $STORAGE_AZURECONTAINERNAME|"image-service-v2-test-data"
sanic:
host: $SANIC_HOST|"0.0.0.0" # Sanic webserver host address
process_host: $SANIC_PROCESS_HOST|"127.0.0.1" # Sanic webserver host address for individual service processes
port: $SANIC_PORT|8080 # Sanic webserver host port
check_quantifier: $CHECK_QUANTIFIER|any # Whether all or any service instance needs to pass all checks for a passed master check
cache: false # Whether to cache readiness and health check results
logging_level_sanic: $LOGGING_LEVEL_SANIC|WARNING
service:
logging_level: $LOGGING_LEVEL_ROOT|DEBUG # Logging level for log file messages
logfile_path: $LOGFILE_PATH|null # Overwrites the default path for the service logfile (image_service/log.log)
verbose: $VERBOSE|True # Service workers print document processing progress to stdout
assert_gpu: $ASSERT_GPU|False # Whether to make a working GPU a mandatory readiness condition
run_id: $RUN_ID|fabfb1f192c745369b88cab34471aba7 # The ID of the mlflow run to load the model from
n_instances: $CONCURRENCY|1 # Number of service top loops that run in parallel (processes, not threads!)
name: $SERVICE_NAME|image-service-v2 # Name of the service in the kubernetes cluster
storage_backend: $STORAGE_BACKEND|s3 # The storage to pull files to be processed from
model_cache_file: "/root/.keras/models/efficientnetb0_notop.h5" # Workaround to intercept auto-download if model is not cached
batch_size: $BATCH_SIZE|32 # Number of images in memory simultaneously per service instance
minimum_free_memory_percentage: $MINIMUM_FREE_MEMORY_PERCENTAGE|.3 # Minimum allowed percentage of free memory
available_memory: $AVAILABLE_MEMORY|6000 # Available memory in MB
monitor_memory_usage: $MONITOR_MEMORY_USAGE|True # Whether to monitor the memory usage and kill the process when memory is insufficient
pdftron_license_key: "Knecon AG(en.knecon.swiss):OEM:DDA-R::WL+:AMS(20211029):BECC974307DAB4F34B513BC9B2531B24496F6FCB83CD8AC574358A959730B622FABEF5C7"