Stub process is unhealthy and it will be restarted

I’m getting Stub process is unhealthy and it will be restarted repeatedly when calling infer, after which the server restarts. I have deployed triton server on GKE with 3 models.

1st time when I infer model1 I get this error, 2nd and consequent hits don’t give this error. But if I infer model2 after getting successful result from model1 then again this error pops up and so on for model3.

logs:

responses.append(self.triton_client.infer(
      File "/home/swapnesh/triton/triton_env/lib/python3.8/site-packages/tritonclient/grpc/__init__.py", line 1086, in infer
        raise_error_grpc(rpc_error)
      File "/home/swapnesh/triton/triton_env/lib/python3.8/site-packages/tritonclient/grpc/__init__.py", line 61, in raise_error_grpc
        raise get_error_grpc(rpc_error) from None
    tritonclient.utils.InferenceServerException: [StatusCode.INTERNAL] Failed to process the request(s) for model instance 'damage_0', message: Stub process is not healthy.

I’m loading 3 models, using the python backend and custom triton image (converted detectron models) which I’ve built using this Dockerfile:

FROM nvcr.io/nvidia/tritonserver:21.10-py3

RUN pip3 install torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1 && \
    pip3 install pillow

Also, while running triton server locally using docker, I had to increase the shm-size as it was giving error to increase it from 64MB. On Kubernetes its a little tricky, to use emptyDir with Memory medium. My yaml looks like this:

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: triton-mms
  name: triton-mms
spec:
  replicas: 1
  selector:
    matchLabels:
      app: triton-mms
  template:
    metadata:
      labels:
        app: triton-mms
    spec:
      containers:
      - image: <custom triton image>
        command: ["/bin/sh", "-c"]
        args: ["tritonserver --model-repository=<gcs model repo>"]
        imagePullPolicy: IfNotPresent
        name: triton-mms
        ports:
        - containerPort: 8000
          name: http-triton
        - containerPort: 8001
          name: grpc-triton
        - containerPort: 8002
          name: metrics-triton
        env:
        - name: GOOGLE_APPLICATION_CREDENTIALS
          value: /secret/gcp-creds.json
        resources:
          limits:
            memory: 5Gi
            nvidia.com/gpu: 1
          requests:
            memory: 5Gi
            nvidia.com/gpu: 1
        volumeMounts:
        - mountPath: /dev/shm
          name: dshm
        - name: vsecret
          mountPath: "/secret"
          readOnly: true
      volumes:
      - name: dshm
        emptyDir:
          medium: Memory
          sizeLimit: "1024Mi"
      - name: vsecret
        secret:
          secretName: gcpcreds

Never faced this issue before and I’m thinking it might be related to shared memory as I’ve never seen that error too.

[s-rog]

@Tabrizian we managed to get the model working in torchscript (torch backend) and no longer experience this issue

[zhyj3038]

just enlarge the Memory of container in Kubernetes will solve it