Spaces:
Sleeping
Sleeping
File size: 2,855 Bytes
c0ba94e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | import os
from prometheus_client import Counter, Histogram
from prometheus_fastapi_instrumentator import Instrumentator, metrics
NAMESPACE = os.environ.get("METRICS_NAMESPACE", "cardiotrack")
SUBSYSTEM = os.environ.get("METRICS_SUBSYSTEM", "api")
instrumentator = Instrumentator(
should_group_status_codes=True,
should_ignore_untemplated=True,
should_instrument_requests_inprogress=True,
excluded_handlers=["/metrics"],
inprogress_name="fastapi_inprogress",
inprogress_labels=True,
)
instrumentator.add(
metrics.request_size(
should_include_handler=True,
should_include_method=True,
should_include_status=True,
metric_namespace=NAMESPACE,
metric_subsystem=SUBSYSTEM,
)
)
instrumentator.add(
metrics.response_size(
should_include_handler=True,
should_include_method=True,
should_include_status=True,
metric_namespace=NAMESPACE,
metric_subsystem=SUBSYSTEM,
)
)
instrumentator.add(
metrics.latency(
should_include_handler=True,
should_include_method=True,
should_include_status=True,
metric_namespace=NAMESPACE,
metric_subsystem=SUBSYSTEM,
buckets=[0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5],
)
)
instrumentator.add(
metrics.requests(
should_include_handler=True,
should_include_method=True,
should_include_status=True,
metric_namespace=NAMESPACE,
metric_subsystem=SUBSYSTEM,
)
)
prediction_counter = Counter(
name=f"{NAMESPACE}_{SUBSYSTEM}_predictions_total",
documentation="Total number of prediction requests",
labelnames=["prediction_type", "endpoint"],
)
prediction_result_counter = Counter(
name=f"{NAMESPACE}_{SUBSYSTEM}_prediction_results_total",
documentation="Count of prediction results by class",
labelnames=["prediction_class", "endpoint"],
)
model_error_counter = Counter(
name=f"{NAMESPACE}_{SUBSYSTEM}_model_errors_total",
documentation="Total number of model loading or prediction errors",
labelnames=["error_type", "endpoint"],
)
explanation_counter = Counter(
name=f"{NAMESPACE}_{SUBSYSTEM}_explanations_total",
documentation="Total number of explanation requests",
labelnames=["status", "endpoint"],
)
batch_size_histogram = Histogram(
name=f"{NAMESPACE}_{SUBSYSTEM}_batch_size",
documentation="Distribution of batch prediction sizes",
labelnames=["endpoint"],
buckets=[1, 5, 10, 20, 50, 100, 200, 500],
)
prediction_processing_time = Histogram(
name=f"{NAMESPACE}_{SUBSYSTEM}_prediction_processing_seconds",
documentation="Time spent on prediction processing (excluding HTTP overhead)",
labelnames=["prediction_type", "endpoint"],
buckets=[0.01, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0, 30.0, 60.0, 120.0],
)
|