Python backend stuck at TRITONBACKEND_ModelInstanceInitialize

Description I wanna start the python backend following the example. But the container stucks at

=============================
== Triton Inference Server ==
=============================

NVIDIA Release 22.04 (build 36821869)
Triton Server Version 2.21.0

Copyright (c) 2018-2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.

Various files include modifications (c) NVIDIA CORPORATION & AFFILIATES.  All rights reserved.

This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license

WARNING: The NVIDIA Driver was not detected.  GPU functionality will not be available.
   Use the NVIDIA Container Toolkit to start this container with GPU support; see
   https://docs.nvidia.com/datacenter/cloud-native/ .

WARNING: [Torch-TensorRT] - Unable to read CUDA capable devices. Return status: 35
I0504 12:25:52.440894 1 libtorch.cc:1381] TRITONBACKEND_Initialize: pytorch
I0504 12:25:52.441090 1 libtorch.cc:1391] Triton TRITONBACKEND API version: 1.9
I0504 12:25:52.441100 1 libtorch.cc:1397] 'pytorch' TRITONBACKEND API version: 1.9
W0504 12:25:52.441171 1 pinned_memory_manager.cc:236] Unable to allocate pinned system memory, pinned memory pool will not be available: CUDA driver version is insufficient for CUDA runtime version
I0504 12:25:52.441209 1 cuda_memory_manager.cc:115] CUDA memory pool disabled
I0504 12:25:52.442350 1 model_repository_manager.cc:1077] loading: resnet:1
I0504 12:25:52.547089 1 python.cc:1769] Using Python execution env /models/resnet/../my-pytorch.tar.gz
I0504 12:25:52.547228 1 python.cc:2054] TRITONBACKEND_ModelInstanceInitialize: resnet_0 (CPU device 0)

My machine dose not have GPU. The confg

name: "resnet"
backend: "python"

input [
  {
    name: "INPUT0"
    data_type: TYPE_FP32
    dims: [ -1, 3, 224, 224 ]
  }
]

output [
  {
    name: "OUTPUT0"
    data_type: TYPE_FP32
    dims: [ -1, 1000 ]
  }
]

instance_group [{
  count: 1
  kind: KIND_CPU
}]

parameters: {
  key: "EXECUTION_ENV_PATH",
  value: {string_value: "$$TRITON_MODEL_DIRECTORY/../my-pytorch.tar.gz"}
}

Triton Information nvcr.io/nvidia/tritonserver:22.04-pyt-python-py3

Are you using the Triton container or did you build it yourself? docker container

[rmccorm4]

@Tabrizian filed DLIS-3765

[Tabrizian]

I tried a small example locally and it did return an error if there wasn’t enough shared memory. @rmccorm4 Could you please file a ticket for this issue so that I can take a closer look? Thanks.