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# Contents and Descriptions


The Financial Fraud Training generates the following model and configuration files under the directory structure shown in the following:

## Structure and Folders

```sh
python_backend_model_repository
└── prediction_and_shapley
 ├── 1
 │   ├── embedding_based_xgboost.json
 │   ├── model.py
 │   └── state_dict_gnn_model.pth
 └── config.pbtxt
```


<Note>The `python_backend_model_repository` directory will be created under the path specified by `"output_dir"` in the training configuration file.</Note>



### `python_backend_model_repository`
Root folder for your NVIDIA Dynamo-Triton Python backend models.


---


### `prediction_and_shapley`
A model repository folder inside `python_backend_model_repository` that contains model code and artifacts.


* **Version Subdirectory (`1`)**  
  This subdirectory represents version `1` of the model. NVIDIA Dynamo-Triton requires each version of the model to reside in its own folder named with the version number.


  - **`embedding_based_xgboost.json`**  
    Serialized XGBoost model file containing configuration and trained parameters for embedding-based predictions.


  - **`model.py`**  
    Core Python script containing the model's prediction logic and Shapley value calculations.


  - **`state_dict_gnn_model.pth`**  
    PyTorch model file (using `.pth` extension) containing the trained weights for the GNN (Graph Neural Network) component.


* **`config.pbtxt`**  
  The configuration file required by the Triton Inference Server. It specifies details, such as input/output tensor shapes, data types, and other model-related parameters.


---


## How to Use


1. **Deploy on  NVIDIA Dynamo-Triton**
   Mount the `python_backend_model_repository` folder into your NVIDIA Dynamo-Triton Server, and make sure the folder structure looks correct.




```sh
python_backend_model_repository
└── prediction_and_shapley
      ├── 1
      │   ├── ...
      └── config.pbtxt
```




2. **Check `config.pbtxt`**
Verify that `config.pbtxt` accurately reflects your model’s I/O specifications. Adjust any shapes, batch sizes, or other parameters as required.


3. **Start NVIDIA Dynamo-Triton Server**
Launch the NVIDIA Dynamo-Triton Inference Server (example command shown below; adapt paths and arguments as needed):


```bash
 !docker run --gpus all -d -p {HTTP_PORT}:{HTTP_PORT} -p {GRPC_PORT}:{GRPC_PORT} -v /path/to/python_backend_model_repository:/models --name tritonserver {TRITON_IMAGE} tritonserver --model-repository=/models
```




4. **Send Inference Requests**
Use an HTTP or gRPC client to send inference requests to the NVIDIA Dynamo-Triton. The model will automatically load and serve both predictions and Shapley values.
The following section details how to pass data to models deployed on the NVIDIA Dynamo-Triton Inference Server.