Document GPU Sharing Example (#261)

Author: bolasim

examples/gpu-sharing/README.md

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+# GPU Sharing Example
+
+This is an example of a truss that shares GPU across multiple models. This truss serves
+three models: a text to image model, an image to image model and an in-painting mode.
+Specific model can be targeted by providing `model_sub_name` in the request dictionary. Please
+refers to examples.yaml for examples.
+A model is loaded into GPU memory when needed. When a different model is invoked, then the previous
+one is offloaded and replaced with the new one. All models remain in the regular memory though, so that
+moving them to GPU memory later is faster.
+
+## Usage
+Like any other truss, you can load this truss and test in-memory then deploy it to a cloud provider as follows:
+```
+models = truss.load("./") # This loads all of the models
+models.predict({
+    "model_sub_name": "text_img", # Name of the model to sub into GPU
+    "prompt": "red dog", # The rest of the kwargs are passed as input to that model
+})
+
+import baseten
+baseten.deploy(models, model_name="Combo GPU Model", publish=True)
+```
+
+To use the included examples, please run `git lfs pull` before loading them via `models.examples()`.
+
+## Adding another model
+To add another model, do the following:
+1. Create a new file with your model class under the `model/` directory. We recommend that you copy [an existing model](./model/text_img_model.py) and modify it to suit your needs.
+2. Add the model to the registry in [`model/model.py`](./model/model.py#L16). The key you use here is what needs to be passed in as `model_sub_name` to use the models
+3. Update `config.yaml` to include any additional system or python requirements
+4. Follow the usage section for testing and deploying
+
+## Custom Weights
+All the examples in this section load the weights during the load at runtime. You may desire to load weights into the model for a variety of reasons. To do so, do the following:
+1. Create a `data/` directory parallel to `model/`
+2. Add the weights there for any of the models. You can make different sub-directories or organize as you wish.
+3. In the model file for a single model, use the following line to get a reference to this folder
+   ```
+   self._data_dir = kwargs["data_dir"]
+    ```
+4. In the `load` function, you this variable to load the weights. Here is an example from the diffusers library.
+   ```
+   self._model = StableDiffusionPipeline.from_pretrained(str(self._data_dir / "path" / "to" / "weights")).to("cuda")
+   ```