[Please Help!!] Error when hosting tensorflow endpoint in script mode

I tried to build my own tensorflow algorithm in `train.py` by adopting from `mnist-2.py` (available in [amazon-sagemaker-examples/sagemaker-python-sdk/tensorflow_script_mode_training_and_serving/](https://github.com/aws/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/tensorflow_script_mode_training_and_serving)) and pass it as entry_point using the pre-built deep learning image. The training job completed with warning and there is an error in deploying the model.

Below is the main function in `train.py`:
```
if __name__ == "__main__":
args, unknown = _parse_args()

train_data, train_labels = _load_training_data(args.train)
eval_data, eval_labels = _load_testing_data(args.train)

print('Training model for {} epochs and {} batch size..\n\n'.format(args.epochs, args.batch_size))

classifier = model(train_data, train_labels, eval_data, eval_labels, epochs=args.epochs, batch_size=args.batch_size)

if args.current_host == args.hosts[0]:
# save model in SaveModel format
classifier.save(os.path.join(args.sm_model_dir, "nn_model.h5"))

# save model in Keras h5 format
classifier.save(os.path.join(args.sm_model_dir, "nn_classifier"))
```

The `estimator` created in the notebook instance is as follows:
```
estimator = TensorFlow(entry_point='train.py',
role=sagemaker.get_execution_role(),
distribution={"parameter_server": {"enabled": True}},
image_uri='763104351884.dkr.ecr.us-west-2.amazonaws.com/tensorflow-training:2.6.0-cpu-py38-ubuntu20.04',
training_steps= 100,
evaluation_steps= 100,
instance_count=2,
instance_type='ml.m5.4xlarge',
hyperparameters={
'epochs': EPOCHS,
'batch_size': BATCH_SIZE
})
```

Here are the problems I encountered:
1. Warnining: no model artifact is saved under path /opt/ml/model. However, I did find a `model.tar.gz` in the output folder of this training job in S3.
> 2021-10-29 23:48:58 Uploading - Uploading generated training model
> 2021-10-29 23:48:58 Completed - Training job completed
> 2021-10-29 23:48:48,662 sagemaker_tensorflow_container.training INFO master algo-1 is down, stopping parameter server
> 2021-10-29 23:48:48,663 sagemaker_tensorflow_container.training WARNING No model artifact is saved under path /opt/ml/model. Your training job will not save any model files to S3.
> For details of how to construct your training script see:
https://sagemaker.readthedocs.io/en/stable/using_tf.html#adapting-your-local-tensorflow-script
> 2021-10-29 23:48:48,663 sagemaker-training-toolkit INFO Reporting training SUCCESS

2. When deploying the estimator after `.fit`, by running
```
predictor = estimator.deploy(initial_instance_count=1, instance_type='ml.m5.xlarge')
```
- It first pops out below message
> update_endpoint is a no-op in sagemaker>=2.
> See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.
I don't know how the `update_endpoint()` would cause an issue given it's still at creating stage and how to implement the `sagemaker.predictor.Predictor.update_endpoint()` mentioned in the refernce link in my `train.py` script or the notebook.

- After half an hour of creating the endpoint it fails by showing
> UnexpectedStatusException: Error hosting endpoint tensorflow-training-2021-10-29-23-53-53-781: Failed. > Reason: The primary container for production variant AllTraffic did not pass the ping health check. Please check CloudWatch logs for this endpoint..

So I also checked the CloudWatch logs for this endpoint and they all show the same event of
> exec: "serve": executable file not found in $PATH

Can anyone help me with these problems? Many thanks in advance!