> ## 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.

# Step 7: Publish your Truss

> Push your Truss to production-ready infrastructure

When you're happy with your Truss, it's time to publish it to production. This re-builds the model server on production-ready infrastructure.

Before publishing your Truss, you can turn off `truss watch` as it only patches models under development, not published models.

### Publish your Truss

To publish your Truss, run:

```sh
truss push --publish
```

Re-building your model server takes more time than patching it; it'll be a moment until the new server is ready to be called.

### Call the published model

Once the new model server is live, call it with `truss predict`:

**Invocation**

```sh
truss predict --published -d '"Truss is awesome!"'
```

**Response**

```json
[
  {
    "label": "POSITIVE",
    "score": 0.999873161315918
  }
]
```

### Review your learning

In this tutorial, you learned how to:

1. Create a Truss
2. Connect your local development environment to a model server
3. Deploy a [basic text classification model](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline)
4. Publish your model to production

For more step-by-step instructions, move on to the [Truss 201 tutorial](/learn/llms/init). Or, to find an example that matches your use case, see the [Truss examples docs](/examples).

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


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

      def load(self):
          self._model = pipeline("text-classification")

      def predict(self, model_input):
          return self._model(model_input)
  ```

  ```yaml config.yaml
  environment_variables: {}
  model_name: My First Truss
  requirements:
    - torch==2.0.1
    - transformers==4.30.0
  resources:
    accelerator: null
    cpu: "1"
    memory: 2Gi
    use_gpu: false
  secrets: {}
  system_packages: []
  ```
</RequestExample>