Including a transformer model in the container for AWS Lambda

To speed up cold starts of my AWS Lambda function, I tried to include the HuggingFace transformer model that the function uses into the container by writing the following in the Dockerfile:

ENV HF_HOME=${LAMBDA_TASK_ROOT}/huggingface
ENV TRANSFORMERS_CACHE=${LAMBDA_TASK_ROOT}
ENV XDG_CACHE_HOME=${LAMBDA_TASK_ROOT}
COPY .cache/torch ${LAMBDA_TASK_ROOT}/torch

The function works and CloudWatch logs indicate that the model is not being downloaded. However, the logs contain the error:

There was a problem when trying to write in your cache folder (/var/task). You should set the environment variable TRANSFORMERS_CACHE to a writable directory.

Even thought the function works, I cannot ignored the error, because the initialization stage during a cold start is still slow and I am being charged for it. The latter fact indicates that a lot of work is being done to initialize the model and that is precisely what I am trying to avoid.

When I set the environment variables to point to /tmp and copy the model there, the model is downloaded during the cold start. It looks like /tmp is cleaned before the cold start.

So, what is the correct way to include the model in the container to make the billed initialization phase during the cold start fast?

P.S. The question at SO: https://stackoverflow.com/q/77075765/2725810