diff --git a/config/pyinfra.toml b/config/pyinfra.toml new file mode 100644 index 0000000..ecfb512 --- /dev/null +++ b/config/pyinfra.toml @@ -0,0 +1,44 @@ +[metrics.prometheus] +enabled = true +prefix = "redactmanager_image_service" + +[tracing.opentelemetry] +enabled = true +endpoint = "http://otel-collector-opentelemetry-collector.otel-collector:4318/v1/traces" +service_name = "redactmanager_image_service" +exporter = "otlp" + +[webserver] +host = "0.0.0.0" +port = 8080 + +[rabbitmq] +host = "localhost" +port = 5672 +username = "" +password = "" +heartbeat = 60 +# Has to be a divider of heartbeat, and shouldn't be too big, since only in these intervals queue interactions happen (like receiving new messages) +# This is also the minimum time the service needs to process a message +connection_sleep = 5 +input_queue = "request_queue" +output_queue = "response_queue" +dead_letter_queue = "dead_letter_queue" + +[storage] +backend = "s3" + +[storage.s3] +bucket = "redaction" +endpoint = "http://127.0.0.1:9000" +key = "" +secret = "" +region = "eu-central-1" + +[storage.azure] +container = "redaction" +connection_string = "" + +[storage.tenant_server] +public_key = "" +endpoint = "http://tenant-user-management:8081/internal-api/tenants" \ No newline at end of file diff --git a/config/settings.toml b/config/settings.toml new file mode 100644 index 0000000..e2f66dc --- /dev/null +++ b/config/settings.toml @@ -0,0 +1,28 @@ +[logging] +level = "INFO" + +[service] +# Print document processing progress to stdout +verbose = false +batch_size = 16 +mlflow_run_id = "fabfb1f192c745369b88cab34471aba7" + +# These variables control filters that are applied to either images, image metadata or service_estimator predictions. +# The filter result values are reported in the service responses. For convenience the response to a request contains a +# "filters.allPassed" field, which is set to false if any of the values returned by the filters did not meet its +# specified required value. +[filters] +# Minimum permissible prediction confidence +min_confidence = 0.5 + +# Image size to page size ratio (ratio of geometric means of areas) +[filters.image_to_page_quotient] +min = 0.05 +max = 0.75 + +# Image width to height ratio +[filters.image_width_to_height_quotient] +min = 0.1 +max = 10 + + diff --git a/image_prediction/config.py b/image_prediction/config.py index 98d2af1..e0cfa8b 100644 --- a/image_prediction/config.py +++ b/image_prediction/config.py @@ -1,46 +1,6 @@ -"""Implements a config object with dot-indexing syntax.""" +from pathlib import Path +from pyinfra.config.loader import load_settings -from envyaml import EnvYAML - -from image_prediction.locations import CONFIG_FILE - - -def _get_item_and_maybe_make_dotindexable(container, item): - ret = container[item] - return DotIndexable(ret) if isinstance(ret, dict) else ret - - -class DotIndexable: - def __init__(self, x): - self.x = x - - def get(self, item, default=None): - try: - return _get_item_and_maybe_make_dotindexable(self.x, item) - except KeyError: - return default - - def __getattr__(self, item): - return _get_item_and_maybe_make_dotindexable(self.x, item) - - def __repr__(self): - return self.x.__repr__() - - def __getitem__(self, item): - return self.__getattr__(item) - - -class Config: - def __init__(self, config_path): - self.__config = EnvYAML(config_path) - - def __getattr__(self, item): - if item in self.__config: - return _get_item_and_maybe_make_dotindexable(self.__config, item) - - def __getitem__(self, item): - return self.__getattr__(item) - - -CONFIG = Config(CONFIG_FILE) +local_root_path = Path(__file__).parents[1] +CONFIG = load_settings(root_path=local_root_path, settings_path="config") diff --git a/image_prediction/utils/logger.py b/image_prediction/utils/logger.py index 58f6022..f65443c 100644 --- a/image_prediction/utils/logger.py +++ b/image_prediction/utils/logger.py @@ -1,27 +1,4 @@ -import logging +import kn_utils -from image_prediction.config import CONFIG - - -def make_logger_getter(): - logger = logging.getLogger("imclf") - logger.propagate = False - - handler = logging.StreamHandler() - handler.setLevel(CONFIG.service.logging_level) - - log_format = "%(asctime)s %(levelname)-8s %(message)s" - formatter = logging.Formatter(log_format, datefmt="%Y-%m-%d %H:%M:%S") - - handler.setFormatter(formatter) - logger.addHandler(handler) - - logger.setLevel(CONFIG.service.logging_level) - - def get_logger(): - return logger - - return get_logger - - -get_logger = make_logger_getter() +# TODO: remove this module and use the `get_logger` function from the `kn_utils` package. +get_logger = kn_utils.get_logger diff --git a/poetry.lock b/poetry.lock index 9c8a9d3..80e4911 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,13 +2,13 @@ [[package]] name = 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"gitlab-research" } kn-utils = { version = "0.2.7", source = "gitlab-research" } dvc = "^2.34.0" dvc-ssh = "^2.20.0" diff --git a/src/serve.py b/src/serve.py index 1c18cec..8652f5b 100644 --- a/src/serve.py +++ b/src/serve.py @@ -1,17 +1,16 @@ -from image_prediction import logger -from image_prediction.config import Config -from image_prediction.locations import CONFIG_FILE +from sys import stdout + +from kn_utils.logging import logger +from pyinfra.examples import start_standard_queue_consumer +from pyinfra.queue.callback import make_download_process_upload_callback + +from image_prediction.config import CONFIG from image_prediction.pipeline import load_pipeline from image_prediction.utils.banner import load_banner from image_prediction.utils.process_wrapping import wrap_in_process -from pyinfra import config -from pyinfra.payload_processing.processor import make_payload_processor -from pyinfra.queue.queue_manager import QueueManager -PYINFRA_CONFIG = config.get_config() -IMAGE_CONFIG = Config(CONFIG_FILE) - -logger.setLevel(PYINFRA_CONFIG.logging_level_root) +logger.remove() +logger.add(sink=stdout, level=CONFIG.logging.level) # A component of the processing pipeline (probably tensorflow) does not release allocated memory (see RED-4206). @@ -19,18 +18,16 @@ logger.setLevel(PYINFRA_CONFIG.logging_level_root) # Workaround: Manage Memory with the operating system, by wrapping the processing in a sub-process. # FIXME: Find more fine-grained solution or if the problem occurs persistently for python services, @wrap_in_process -def process_data(data: bytes) -> list: - pipeline = load_pipeline(verbose=IMAGE_CONFIG.service.verbose, batch_size=IMAGE_CONFIG.service.batch_size) +def process_data(data: bytes, _message: dict) -> list: + pipeline = load_pipeline(verbose=CONFIG.service.verbose, batch_size=CONFIG.service.batch_size) return list(pipeline(data)) def main(): logger.info(load_banner()) - process_payload = make_payload_processor(process_data, config=PYINFRA_CONFIG) - - queue_manager = QueueManager(PYINFRA_CONFIG) - queue_manager.start_consuming(process_payload) + callback = make_download_process_upload_callback(process_data, CONFIG) + start_standard_queue_consumer(callback, CONFIG) if __name__ == "__main__":