def initialise_prometheus(app, log=None):
if os.environ.get("PROMETHEUS_MULTIPROC_DIR", False):
metrics = GunicornInternalPrometheusMetrics(app)
if log:
log.info("Prometheus metrics enabled")
return metrics
return None
def enable_prometheus():
# Enable deployment specific code for Prometheus metrics
if os.environ.get("prometheus_multiproc_dir", False):
from prometheus_flask_exporter.multiprocess import (
GunicornInternalPrometheusMetrics, )
metrics = GunicornInternalPrometheusMetrics(app)
_LOG.info(f"Prometheus metrics enabled : {metrics}")
def initialise_prometheus(app, log=None):
# Prometheus
if os.environ.get("prometheus_multiproc_dir", False):
metrics = GunicornInternalPrometheusMetrics(app)
if log:
log.info("Prometheus metrics enabled")
return metrics
return None
def app() -> Flask:
app = create_app('myapp.config.TestConfig')
prometheus_client.REGISTRY = prometheus_client.CollectorRegistry(
auto_describe=True)
myapp_extensions.metrics = GunicornInternalPrometheusMetrics(
app=None, group_by="endpoint")
ctx = app.app_context()
ctx.push()
yield app
ctx.pop()
def activate_prometheus_exporter(app):
def mlflow_version(_: request):
return VERSION
metrics = GunicornInternalPrometheusMetrics(
app,
export_defaults=True,
defaults_prefix="mlflow",
excluded_paths=["/health"],
group_by=mlflow_version,
)
return metrics
def activate_prometheus_exporter(app):
metrics = GunicornInternalPrometheusMetrics(app, export_defaults=False)
endpoint = app.view_functions
histogram = metrics.histogram(
'mlflow_requests_by_status_and_path',
'Request latencies and count by status and path',
labels={
'status': lambda r: r.status_code,
'path': lambda: change_path_for_metric(request.path)
})
for func_name, func in endpoint.items():
if func_name in [
"_search_runs", "_log_metric", "_log_param", "_set_tag",
"_create_run"
]:
app.view_functions[func_name] = histogram(func)
return app
def create_app():
"""Create Connexion/Flask application."""
root = os.path.dirname(rhub.__path__[0])
connexion_app = connexion.App(__name__)
flask_app = connexion_app.app
flask_app.url_map.strict_slashes = False
if os.getenv('PROMETHEUS_MULTIPROC_DIR'):
GunicornInternalPrometheusMetrics(flask_app)
from . import _config
flask_app.config.from_object(_config)
parser = prance.ResolvingParser(
os.path.join(root, 'openapi', 'openapi.yml'))
connexion_app.add_api(
parser.specification,
validate_responses=True,
strict_validation=True,
pythonic_params=True,
)
# Enable CORS (Cross-Origin Resource Sharing)
# https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS
CORS(flask_app)
flask_app.cli.add_command(init_command)
db.init_app(flask_app)
migrate.init_app(flask_app, db)
try:
import coloredlogs
coloredlogs.install(level=flask_app.config['LOG_LEVEL'])
except ImportError:
logging.basicConfig(level=flask_app.config['LOG_LEVEL'])
RHUB_RETURN_INITIAL_FLASK_APP = os.getenv('RHUB_RETURN_INITIAL_FLASK_APP',
'False')
if str(RHUB_RETURN_INITIAL_FLASK_APP).lower() == 'true':
return flask_app
FlaskInjector(
app=flask_app,
injector=di,
modules=[
KeycloakModule(flask_app),
VaultModule(flask_app),
SchedulerModule(flask_app),
],
)
# Try to retrieve Tower notification webhook creds from vault
try:
with flask_app.app_context():
vault = di.get(Vault)
webhookCreds = vault.read(flask_app.config['WEBHOOK_VAULT_PATH'])
if webhookCreds:
flask_app.config['WEBHOOK_USER'] = webhookCreds['username']
flask_app.config['WEBHOOK_PASS'] = webhookCreds['password']
else:
raise Exception(
'Missing tower webhook notification credentials; '
f'{vault} {flask_app.config["WEBHOOK_VAULT_PATH"]}')
except Exception as e:
logger.error(
f'Failed to load {flask_app.config["WEBHOOK_VAULT_PATH"]} tower'
f' webhook notification credentials {e!s}.')
return flask_app
from flask import Flask
from prometheus_flask_exporter.multiprocess import GunicornInternalPrometheusMetrics
app = Flask(__name__)
metrics = GunicornInternalPrometheusMetrics(app)
@app.route('/test')
def index():
return 'Hello world'
@app.route('/error')
def error():
raise Exception('Fail')
if __name__ == '__main__':
app.run(debug=False, port=5000)
def __init__(self, app, config):
self.config = config
if os.getenv('prometheus_multiproc_dir'):
self.gunicorn_metrics = GunicornInternalPrometheusMetrics(
app, path="/metrics")
from flask import Flask
from prometheus_flask_exporter.multiprocess import GunicornInternalPrometheusMetrics
application = Flask(__name__)
metrics = GunicornInternalPrometheusMetrics(application)
# static information as metric
metrics.info('app_info', 'Application info', version='1.0.3')
@application.route('/test')
def main():
raise Exception("Crashing")
pass # requests tracked by default
if __name__ == '__main__':
application.run(debug=False, port=5000)
import os
from flask import request
from mlflow.pyfunc import scoring_server, load_model
from prometheus_flask_exporter.multiprocess import GunicornInternalPrometheusMetrics
app = scoring_server.init(load_model(os.getenv('MODEL_PATH')))
metrics = GunicornInternalPrometheusMetrics(app, defaults_prefix=os.getenv('STATSD_PREFIX'))
metrics.register_default(
metrics.counter(
'by_path_counter', 'Request count by request paths',
labels={'path': lambda: request.path}
)
)
from prometheus_flask_exporter.multiprocess import GunicornInternalPrometheusMetrics
metrics = GunicornInternalPrometheusMetrics(app=None, group_by="endpoint")
def setup_extensions(app):
metrics.init_app(app)
return app
def init_metrics(app):
myapp = GunicornInternalPrometheusMetrics(app,
registry=CollectorRegistry())