> ## 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 3: Enable live reload

> Patch the model server with local changes

You don't have to run `truss push` every time you update your Truss during development. Instead, you can have a live reload workflow where local changes automatically are pushed to the production environment to give you a lightning-fast feedback loop.

### Watch for changes

<Tip>
  Run the `truss watch` command in a new terminal tab in the same working directory, as you'll need to leave it running while you work.
</Tip>

In a new terminal tab in the same `my-first-truss` working directory, run:

```sh
truss watch
```

Now, as you update your Truss, the changes will be patched onto the model server on the remote host.

### Use `truss watch` while working

In the next three steps, we'll deploy a basic ML model to our model server. Check your `truss watch` tab and model logs on Baseten after each step to see the changes you make locally reflected live on your model server.

Leave `truss watch` running in this new terminal tab for the rest of the tutorial. You can use `Control-C` to exit `truss watch` and stop automatically pushing changes.

<RequestExample>
  ```python model/model.py
  class Model:
      def __init__(self, **kwargs):
          self._model = None

      def load(self):
          pass

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

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