Skip to main content

Inference service package for IAPARC

Project description

iaparc_inference

PyPI version PyPI - License

The IA Parc inference plugin allows developers to easily integrate their inference pipeline into IA Parc's production module.

Installation

pip install iaparc-inference

Usage

  • If your inference pipeline support batching:

    from iaparc_inference import IAPListener
    
    # Define a callback to query your inference pipeline
    # To load your model only once it is recommended to use a class:
    class MyModel:
        def __init__(self, model_path: str):
            ## Load your model in pytorch, tensorflow or any other backend
        
        def batch_query(batch: list, parameters: Optional) -> list:
            ''' execute your pipeline on a batch input
                Note:   "parameters" is an optional argument.
                        It can be used to handle URL's query parameters
                        It's a list of key(string)/value(string) dictionaries
            '''
    
    if __name__ == '__main__':
        # Initiate your model class
        my_model = MyModel("path/to/my/model")
    
        # Initiate IAParc listener
        listener = IAPListener(my_model.batch_query)
        # Start the listener
        listener.run()
    
  • If your inference pipeline do not support batching:

    from iaparc_inference import IAPListener
    
    # Define a callback to query your inference pipeline
    # To load your model only once it is recommended to use a class:
    class MyModel:
        def __init__(self, model_path: str):
            ## Load your model in pytorch, tensorflow or any other backend
        
        def single_query(one_input, parameters: Optional):
            ''' execute your pipeline on a single input
                Note:   "parameters" is an optional argument.
                        It can be used to handle URL's query parameters
                        It's a key(string)/value(string) dictionary
            '''
    
    if __name__ == '__main__':
        # Initiate your model class
        my_model = MyModel("path/to/my/model")
    
        # Initiate IAParc listener
        listener = IAPListener(my_model.single_query, batch=1)  # Note that batch size is forced to 1 here
        # Start the listener
        listener.run()
    

Features

  • Dynamic batching
  • Autoscalling
  • Support both synchronous and asynchronous queries
  • Data agnostic

License

This project is licensed under the Apache License Version 2.0 - see the Apache LICENSE file for details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

iaparc_inference-0.6.4.tar.gz (16.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

iaparc_inference-0.6.4-py3-none-any.whl (18.8 kB view details)

Uploaded Python 3

File details

Details for the file iaparc_inference-0.6.4.tar.gz.

File metadata

  • Download URL: iaparc_inference-0.6.4.tar.gz
  • Upload date:
  • Size: 16.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.10.16

File hashes

Hashes for iaparc_inference-0.6.4.tar.gz
Algorithm Hash digest
SHA256 11ff7c29eb6787de7e8a15f6df6b610c5d2d9a5c9c8bc326a440ddd53e82e853
MD5 a6864842723fce27b78c0edb86f44b8c
BLAKE2b-256 b28a125c775deaaa1faa2fd917ea1d993d04a29a6037660461eebd125ddbed8e

See more details on using hashes here.

File details

Details for the file iaparc_inference-0.6.4-py3-none-any.whl.

File metadata

File hashes

Hashes for iaparc_inference-0.6.4-py3-none-any.whl
Algorithm Hash digest
SHA256 f73ef61dc4933a5635e9550598a2c04fdf8ddcc98f380efaae0be9937c380450
MD5 f396ed113bfc986517dfc680c5066f0e
BLAKE2b-256 7beb27008b0c18a0f9239482efe915268df091819af01c2c50f0472f0f2e0dd9

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page