AI Server Module

PySDK API Reference Guide. CLI launcher for the DeGirum AI server.

This API Reference is based on PySDK 0.20.0.

DeGirum AI server launcher and model downloader.

The functionality of this module is now exposed via PySDK CLI.

The purpose of this module is to start DeGirum AI server:

python -m degirum.server --zoo <local zoo path> [--port <server TCP port>]

Other Parameters:

Name	Type	Description
--zoo	str	Path to a local model zoo directory, containing AI models you want your AI server to serve. One possible way to fill local model zoo directory is to download models from a model zoo repo using download_models() function and provide the path to the local zoo directory as --zoo parameter.
--port	int	TCP port to bind server to. Default is 8778.

• When AI server is started, it runs indefinitely.

• If you started the server from a terminal, you may press Enter to shut it down.

• If you started the server headless (for example, as a service), then to shut down the server you need to kill the Python process which runs the server.

The module also exposes download_models() function which can be used to prepare local model zoo directory to be served by AI server:

• You first download models from the model zoo repo of your choice into some local directory of your choice by calling degirum.server.download_models function.

• Then you start the AI server providing the path to that local directory as --zoo parameter.

degirum.server.download_models(path, *, url=ZooManager._default_cloud_url, token='', **kwargs)

Download all models from a model zoo repo specified by the URL.

Parameters:

Name	Type	Description	Default
path	str	Local filesystem path to store models downloaded from a model zoo repo.	required
url	str	Zoo repo URL. If not specified, then DeGirum public model zoo URL will be used.	_default_cloud_url
token	str	Zoo repo authorization token.	''

Other Parameters:

Name	Type	Description
model_family	str	Model family name filter. When you pass a string, it will be used as search substring in the model name. For example, "yolo", "mobilenet". You may also pass re.Pattern. In this case it will do regular expression pattern search.
device	str	Target inference device -- string or list of strings of device names. If passed, only models targeting the specified device(s) will be downloaded.
precision	str	Model calculation precision -- string or list of strings of model precision labels. Possible labels: "quant", "float".
pruned	str	Model density -- string or list of strings of model density labels. Possible labels: "dense", "pruned".
runtime	str	Runtime agent type -- string or list of strings of runtime agent types. Possible types: "n2x", "tflite", "tensorrt", "openvino".