> ## Documentation Index
> Fetch the complete documentation index at: https://baseten-philip-copy-0226.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Model with system packages

> Deploy a model with both Python and system dependencies

<Card title="View on Github" icon="github" href="https://github.com/basetenlabs/truss-examples/tree/main/10-using-system-packages" />

In this example, we build a Truss with a model that requires specific system packages.

To add system packages to your Truss, you can add a `system_packages` key to your config.yaml file,
for instance:
To add system packages to your model serving environment, open config.yaml and
update the system\_packages key with a list of apt-installable Debian packages:

```yaml config.yaml
system_packages:
 - tesseract-ocr
```

For this example, we use the [LayoutLM Document QA](https://huggingface.co/impira/layoutlm-document-qa) model,
a multimodal model that answers questions about provided invoice documents. This model requires a system
package, tesseract-ocr, which needs to be included in the model serving environment.

# Setting up the model.py

For this model, we use the HuggingFace transformers library, and the document-question-answering task.

```python model/model.py
from transformers import pipeline


class Model:
    def __init__(self, **kwargs) -> None:
        self._model = None

    def load(self):
        self._model = pipeline(
            "document-question-answering",
            model="impira/layoutlm-document-qa",
        )

    def predict(self, model_input):
        return self._model(model_input["url"], model_input["prompt"])
```

# Setting up the config.yaml file

The main items that need to be configured in the config.yaml file are requirements
and `system_packages` sections.

```yaml config.yaml
environment_variables: {}
external_package_dirs: []
model_metadata:
    example_model_input: {"url": "https://templates.invoicehome.com/invoice-template-us-neat-750px.png", "prompt": "What is the invoice number?"}
model_name: LayoutLM Document QA
python_version: py39
```

Specify the versions of the Python requirements that are needed.
<Note>Always pin exact versions for your Python dependencies. The ML/AI space moves fast, so you want to have an up-to-date version of each package while also being protected from breaking changes.</Note>

```yaml config.yaml
requirements:
- Pillow==10.0.0
- pytesseract==0.3.10
- torch==2.0.1
- transformers==4.30.2
resources:
  cpu: "4"
  memory: 16Gi
  use_gpu: false
  accelerator: null
secrets: {}
```

The system\_packages section is the other important bit here, you can
add any package that's available via `apt` on Debian.

```yaml config.yaml
system_packages:
- tesseract-ocr
```

# Deploy the model

```bash
$ truss push
```

You can then invoke the model with:

```
$ truss predict -d '{"url": "https://templates.invoicehome.com/invoice-template-us-neat-750px.png", "prompt": "What is the invoice number?"}'
```

<RequestExample>
  ```python model/model.py
  from transformers import pipeline


  class Model:
      def __init__(self, **kwargs) -> None:
          self._model = None

      def load(self):
          self._model = pipeline(
              "document-question-answering",
              model="impira/layoutlm-document-qa",
          )

      def predict(self, model_input):
          return self._model(model_input["url"], model_input["prompt"])
  ```

  ```yaml config.yaml
  environment_variables: {}
  external_package_dirs: []
  model_metadata:
      example_model_input: {"url": "https://templates.invoicehome.com/invoice-template-us-neat-750px.png", "prompt": "What is the invoice number?"}
  model_name: LayoutLM Document QA
  python_version: py39
  requirements:
  - Pillow==10.0.0
  - pytesseract==0.3.10
  - torch==2.0.1
  - transformers==4.30.2
  resources:
    cpu: "4"
    memory: 16Gi
    use_gpu: false
    accelerator: null
  secrets: {}
  system_packages:
  - tesseract-ocr
  ```
</RequestExample>