[Feature Proposal] Extend torch hub to better support cloud serving and edge deployment
Labels: feature, triaged, module: hub

### 🚀 The feature, motivation and pitch

**TL;DR**: Extend torch hub to better support cloud serving and edge deployment by:
1. Extend the `hubconf.py` as an entrypoint for serving and deployment, like [HuggingFace Inferece Handler](https://huggingface.co/philschmid/layoutlm-funsd/blob/main/handler.py)
2. (Optional) Allow third-party infra providers to integrate model deployment and performance reporting to the hub website.

### Background
I was a PhD researcher a while ago and am now an AI practitioner in the industry. From my personal experience and observation, the pytorch hub provides incremental value to researchers, because:
1. Researchers usually rely on conferences and their own networks to find the latest models.
2. Researchers are familiar with cloning a repo, peeking into the source and doing modifications. The simple `torch.hub.load` interface is not very much helpful to them.

However, industry community usually don't have the advantages mentioned above, therefore the hub can be a valuable information source to them. But when they choose a model, they not only want an interface to run some sample data, deployment easiness and model performance are also important factors to consider. 

That's why huggingface transformers, yolo-series, and mmdetection are very popular. 
But not all researchers like to submit their models to those repos due to the extensive customizations needed, therefore, a lightweight hubconf.py could just reduce the gap between research and production need.

### Proposed changes
`hubconf.py`: provides a reference format to share models, and acts as an interface for deployment, benchmarking and serving.

A very rough example is shown below, just for discussion purpose.

```
__model__ = ["MyModel"]

def MyModel:
  def __init__(self, pretrained=False, **kwargs): 
    self.model = build_model(pretrained=pretrained)
  
  def forward(self, x: Union[Tuple[Any, ...], torch.Tensor]) -> Union[Tuple[Any, ...], torch.Tensor]:  
    # Core tensor-in, tensor-out model function
    return self.model(x)
  
  def get_input(self): 
    # get sample data
    return preprocess(cv2.imread('assets/img1.png'))

  def inference(self, data: Dict[str, Any]) -> Dict[str, Any]:
    # optional, for model serving only
    output = self.model(preprocess(data['input']))
    return {"predictions": postprocess(output)}
```

Hub website: "Discover and publish models to a pre-trained model repository designed for research exploration." -> "For research exploration and production use". And in the future, allow third-party providers to plug in model statistics based on `hubconf.py`

**Disclaimer**
Our company is currently working on an MLOps platform to facilitate the production of PyTorch models. We see a clear benefit that a shared protocol could further reduce the friction between research and industry communities. We would like to hear feedback from PyTorch team and we are also open to take the initial effort to extend the hubconf and adopt open-source repos to fit this extended format.

### Alternatives

_No response_

### Additional context

_No response_

cc @nairbv @NicolasHug @vmoens @jdsgomes