Hi @Paul , thanks to reply.
My setup is like your, I coded a loop with posting to docker in python (like docs CPU - Roboflow Docs), in the POST (made with request module in python). I passed image and for every post I added a sleep 300 ms, specific code I used in loop is like next.
def pred_request(path_img,confidence,overlap,model_version,model_proyect):
url = "http://localhost:9001"
api_key = "yourkey"
url = f"{url}/{model_proyect}/{model_version}"
params = {
"api_key": api_key,
"confidence": confidence,
"overlap":overlap
}
files = {'file': open(path_img, 'rb')}
res = requests.post(url, params=params,files=files)
return res.json()
with
#extract frame from video to send
result =[];
for frame in video:
result.append(pred_request(frame, confidence, overlap,model_version,model_proyect))
time.sleep(0.3);
#process result
All Request is serial (after one request is finished, I sent another request)
I used only one custom model for detect object (electrical object like insulators, cut strands etc)
I downloaded the new img and I tried with the new parameter like
sudo docker run --name sofia-docker --net=host --mount source=roboflow,target=/cache roboflow/roboflow-inference-server-cpu --memory="30g" --storage-opt size=15G -e DISABLE_INFERENCE_CACHE=true
and disk space not increasing like before, but ram memory used still increase and is not free up after (like before at the start of discuss).
In the server, I noticed only a few process relationated with uvicorn, this eat memory ram constantly and after 3 hours (imgs attached), the memory use increase from 2G to 8G and only that process relationated with docker (cause if I´m going to stop the docker, then its memory is free). Last number of POST request sended was around 12 thousand in that 3 hours
