Can not run two tensorrt models (two dockers) on same GPU

Description

I have an AI module including 2 docker containers: the first container has 2 CNN models, the second one has 3 CNN models. It works well with native TensorFlow. But when I convert all CNN models to tf-trt format, I only run one of two containers, second containers with message:

I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6043 MB memory:  -> device: 0, name: Tesla T4, pci bus id: 0000:00:1e.0, compute capability: 7.5
 I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
 E tensorflow/compiler/tf2tensorrt/utils/trt_logger.cc:42] DefaultLogger coreReadArchive.cpp (41) - Serialization Error in verifyHeader: 0 (Version tag does not match. Note: Current Version: 96, Serialized Engine Version: 97)
 E tensorflow/compiler/tf2tensorrt/utils/trt_logger.cc:42] DefaultLogger INVALID_STATE: std::exception
 E tensorflow/compiler/tf2tensorrt/utils/trt_logger.cc:42] DefaultLogger INVALID_CONFIG: Deserialize the cuda engine failed.

Environment

TensorRT Version: nvcr.io/nvidia/tensorrt:20.09
GPU Type: T4
TensorFlow Version: 2.4.2

Hi,
We recommend you to check the below samples links in case of tf-trt integration issues.
https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html#samples
https://docs.nvidia.com/deeplearning/tensorrt/quick-start-guide/index.html#framework-integration
https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html#integrate-ovr
https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html#usingtftrt

If issue persist, We recommend you to reach out to Tensorflow forum.
Thanks!