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-0.5.8.tar.gz (16.8 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-0.5.8-py3-none-any.whl (18.7 kB view details)

Uploaded Python 3

File details

Details for the file iaparc_inference-0.5.8.tar.gz.

File metadata

  • Download URL: iaparc_inference-0.5.8.tar.gz
  • Upload date:
  • Size: 16.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.15

File hashes

Hashes for iaparc_inference-0.5.8.tar.gz
Algorithm Hash digest
SHA256 fee5383493ba0a8652e2b763912083e244f437f2dfb353ecd45e760b2f34acd6
MD5 941d62d4abf7b95197d5d45f0de43067
BLAKE2b-256 ae79a6da915dd0f9da2881af3091f62fdcc4193aff7e72e0047310da408e6ff4

See more details on using hashes here.

File details

Details for the file iaparc_inference-0.5.8-py3-none-any.whl.

File metadata

File hashes

Hashes for iaparc_inference-0.5.8-py3-none-any.whl
Algorithm Hash digest
SHA256 d047c7be680d040c81e49bd919cd579536e1a65cbc73cae88a6e58dfea2b75eb
MD5 be073cd499336144082f0976025b4093
BLAKE2b-256 dacf4043a97a90734ee0bde7f60489734fd287752e8e273263fb56b477bb9774

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