feat(metrics): add /metrics which returns prometheus metrics

Author: co42

src/huggingface_inference_toolkit/webservice_starlette.py

@@ -7,7 +7,7 @@
 from starlette.responses import PlainTextResponse, Response
 from starlette.routing import Route
 
-from huggingface_inference_toolkit.async_utils import async_handler_call
+from huggingface_inference_toolkit.async_utils import MAX_CONCURRENT_THREADS, MAX_THREADS_GUARD, async_handler_call
 from huggingface_inference_toolkit.const import (
     HF_FRAMEWORK,
     HF_HUB_TOKEN,
@@ -69,6 +69,18 @@ async def health(request):
     return PlainTextResponse("Ok")
 
 
+# Report Prometheus metrics
+# inf_batch_current_size: Current number of requests being processed
+# inf_queue_size: Number of requests waiting in the queue
+async def metrics(request):
+    batch_current_size = MAX_CONCURRENT_THREADS - MAX_THREADS_GUARD.value
+    queue_size = MAX_THREADS_GUARD.statistics().tasks_waiting
+    return PlainTextResponse(
+        f"inf_batch_current_size {batch_current_size}\n" +
+        f"inf_queue_size {queue_size}\n"
+    )
+
+
 async def predict(request):
     try:
         # extracts content from request
@@ -132,6 +144,7 @@ async def predict(request):
         routes=[
             Route(_health_route, health, methods=["GET"]),
             Route(_predict_route, predict, methods=["POST"]),
+            Route("/metrics", metrics, methods=["GET"]),
         ],
         on_startup=[prepare_model_artifacts],
     )
@@ -143,6 +156,7 @@ async def predict(request):
             Route("/health", health, methods=["GET"]),
             Route("/", predict, methods=["POST"]),
             Route("/predict", predict, methods=["POST"]),
+            Route("/metrics", metrics, methods=["GET"]),
         ],
         on_startup=[prepare_model_artifacts],
     )