model_fn and input_fn called multiple times

I am using the prebuilt SageMaker SKLearn container (https://github.com/aws/sagemaker-scikit-learn-container) version 0.20.0.
In the entry_point,I include a script which carries out the batch transformation job.
```py
def model_fn():
...

def input_fn():
...

def predict_fn():
'''
A long running process to preprocess the data before calling model
https://aws.amazon.com/blogs/machine-learning/preprocess-input-data-before-making-predictions-using-amazon-sagemaker-inference-pipelines-and-scikit-learn/
'''
time.sleep(60 * 11) # sleep for 11 mins to simulate a long running process
....

def output_fn():
....
```

I noticed that the model_fn() was called multiple times in the cloudwatch log
```
21:11:43 model_fn called /opt/ml/model 0.3710819465747405
21:11:43 model_fn called /opt/ml/model 0.1368146211634631
21:11:44 model_fn called /opt/ml/model 0.09153953459183728
```

The input_fn() was also called multiple times
```
20:41:31 input_data application/json 0.3936440317990033 {
20:51:30 input_data application/json 0.4852180186010707 {
21:01:30 input_data application/json 0.9954036507047136 {
21:11:30 input_data application/json 0.0806271844985188 {
```
Precisely, it's called every 10 minutes.

I used ml.m4.xlarge, BatchStrategy = SingleRecord and SplitType of None. I also used the environmental variable SAGEMAKER_MODEL_SERVER_TIMEOUT = '9999' to overcome the 60s timeout. I expected that the model_fn or input_fn would only be called once, but in this case, they were called multiple times. In the end, the container crashed with "Internal Server Error".

I saw a similar related issue before https://github.com/awslabs/amazon-sagemaker-examples/issues/341 where the model_fn was called on each invocation. But in this case, there is no /invocations, the model_fn, input_fn, predict_fn, and output_fn were called multiple time. In the end, the container *crashed* with Internal Server Error.

Start with the entry-point script and the CloudWatch timestamps for model_fn, input_fn, predict_fn, and output_fn, then inspect the SageMaker scikit-learn container behavior around the 10-minute calls and SAGEMAKER_MODEL_SERVER_TIMEOUT. Done means reproducing or explaining the repeated calls and the resulting Internal Server Error, with the expected behavior and any required container or configuration change identified.

Written by the indexing model from the issue text.