batch transform fails to install

I'm using `HuggingFaceModel` from sagemaker inference toolkit to create batch transform jobs for large scale inference data pipelines. I started by looking at the example and walk through here: https://huggingface.co/docs/sagemaker/inference

I regularly run into an issue where about 1/2 of submitted jobs will fail at around the 35 min mark.

Looking at the logs what I see is:

```
ValueError("failed to install required packages")
```
This is the same error across all of the failures.

I run a lot of batch transform jobs on production scale data, and I'm wondering if we're simply running into issues with throttling from pip.

Is there an approach to leverage the `HuggingFaceModel` container creation, but then reuse the registered model within Sagemaker so that we're not re-creating the container with each new execution?

I have looked around and do not see any examples like this.

This is the general approach we have for creating the batch transform jobs
```
from sagemaker.huggingface.model import HuggingFaceModel
hub = {"HF_TASK": "summarization"}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
model_data=model_s3_location,
role=role,
transformers_version="4.17",
pytorch_version="1.10",
py_version="py38",
env=hub,
)

batch_job = huggingface_model.transformer(
instance_count=instance_count,
instance_type=instance_type,
strategy="SingleRecord",
output_path=output_path,
assemble_with="Line",
)

formatted_time = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")

batch_job.transform(
data=input_path,
content_type="application/json",
split_type="Line",
logs=False,
wait=False,
job_name=f"my-batch-job-batch-{batch}-shard-{shard}-{formatted_time}",
model_client_config={"InvocationsMaxRetries": 3, "InvocationsTimeoutInSeconds": 600},
)
```

Start with the documented HuggingFaceModel batch-transform flow and inspect the logs around the repeated "failed to install required packages" error. Reproduce the intermittent failures if possible, then determine whether model reuse avoids recreating the container; done means either reliable batch jobs or a documented supported reuse approach.

Written by the indexing model from the issue text.