# Run AI Stress Testing on Models with Multiple Files

The first step to running a stress test on a model is defining a python file to wrap
your model as defined in our section in Lite:
[How To/Specify A Model](../cli/specify_model.md).
This model wrapper file imports all additional assets required for the model and
defines a `predict_dict` or `predict_df` function.

When uploading the model files and the model wrapper `model.py` to S3, you must
structure the bucket with the wrapper at the root of the base path and all other
model files within that base directory according to how you referenced them
locally.  For instance, in the following bucket

```
s3://examples/models
│
└───model_1
│   │   model.py
│   │   model.pkl
│   │
│   └───assets
│       │   preprocessor.py
│       │   aux_file.dat
│       │   ...
│   
└───model_2
|   │   model.py
|   │   model.sav
|   |   ...
|
└───common
    │   preprocessor.py
    |   ...
```

if you use `s3://examples/models/model_1/model.py` in your test
configuration's model path, the test runner will copy all files from its base
`s3://examples/models/model_1/` to a local folder. The `model.py` can thus refer
to assets within `cur_dir`; e.g in your `model.py` you refer to the `model.pkl` file
as follows

```
from pathlib import Path

cur_dir = Path(__file__).absolute().parent

MODEL_NAME = 'model.pkl'
MODEL_PATH = cur_dir / MODEL_NAME
```

This allows you to write your wrapper file relative to the directory it lives within
and then upload that entire directory to the appropriate location in your S3 bucket.

However, any files that are required outside of this base path must be referred to
with an absolute s3 path within `model.py`. For instance if you want to use
`s3://examples/models/common/preprocessor.py` in your `model.py`, you must use that
full s3 path in order to refer to that library because it is not within the base path
`s3://examples/models/model_1` of your model wrapper.