Install this package as follows:

pip install -e "git+https://github.com/sara-nl/cortExchange#egg=cortexchange"

You can then initialize the predictor as follows:

from cortexchange.architecture import get_architecture, Architecture

StopPredictor: type(Architecture) = get_architecture("surf/StopPredictor")
predictor = StopPredictor(device="cpu", model_name="name_of_the_model")

If you want to use a different remote host, or specify a new cache path, you can do so as follows:

The model cache will be used to store the to-be-downloaded models. This will then reuse downloaded models on subsequent runs of the program.

from cortexchange.wdclient import init_downloader

init_downloader(
    url="https://surfdrive.surf.nl/files/public.php/webdav/",
    login="webdavlogin",
    password="password",
    cache="/your/new/cache/path/.cache"
) 

You can run prediction on the given model as follows:

data = predictor.prepare_data(input_path="/your/input/file.fits")
predictor.predict(data)

You can add new models to the code through the CLI if it uses existing architecture, or create a PR following the below instructions.

We have a Architecture class which has the following two methods that need to be implemented:

def prepare_data(self, data: Any) -> torch.Tensor:
    ...
    
    
def predict(self, data: torch.Tensor) -> Any:
    ...

The prepare data ideally takes a path from which files are read, which would then automatically allow for seamless integration with the cortexchange-cli tool.

There are also some optional methods that can be implemented:

@staticmethod
def add_argparse_args(parser: argparse.ArgumentParser) -> None:
    ...


def __init__(self, model_name: str, device: str, *args, your_additional_args=0, **kwargs):
    ...

Please ensure that your implementation of the __init__ function can take an arbitrary amount of additional arguments. We pass all the arguments parsed by the argparser to the __init__ function. Adding your model-specific argparser arguments should be done in the add_argparse_args method.

In the CLI we have a few tools: run, upload-weights, upload-architecture, list-group, and create-group. Upload new models with an existing architecture easily with the upload tool. List existing models in a group with list-group. run will download and run a single sample quickly. This can be used to pre-download models, and see if they run on the hardware you have available. The programmatical approach is better to use for integration with existing workflows, as then you can keep the model weights loaded between inference passes through the model. Uploading new architecture is possible temporarily through the CLI tool as well. This will zip the code and upload under the given name. For longer term support, please create a PR with your model architecture, as this will add versioning combined with the rest of the repository.

You can create a .env file where you are running containing the following two variables which are used for authentication to webdav:

WD_LOGIN=code
WD_PASSWORD=password

This will override the default read-only values. It has lower priority than values passed through the terminal.