I'm attempting to deploy a local tensorflow model to a sagemaker endpoint. I want to include custom inference code to transform the input data. Given the model is trained and already located in an S3 bucket, I can run the following, which correctly deploys the model to an endpoint:

```
tensorflow_model = Model(
        model_data=saved_model,
        entry_point='src/development/document_matcher/inference.py',
        source_dir ='./src/development/document_matcher',      
        role=role, 
        framework_version=tf_framework_version
        )

```

The inference file is working correctly; however, I also need the vocabulary for the model to transform the incoming data correctly. This is what I'm trying to upload with source\_dir.

My Directory structure is as follows:

```
- src
 - development
  -document_matcher
   - inference.py
   - total_vocab.pkl

```

I need to have total\_vocab.pkl accessible to my inference script when it runs. However, inference.py is unable to find it. Am I misunderstanding how sagemaker works?