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-0.5.7.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-0.5.7-py3-none-any.whl (18.7 kB view details)

Uploaded Python 3

File details

Details for the file iaparc_inference-0.5.7.tar.gz.

File metadata

  • Download URL: iaparc_inference-0.5.7.tar.gz
  • Upload date:
  • Size: 16.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.15

File hashes

Hashes for iaparc_inference-0.5.7.tar.gz
Algorithm Hash digest
SHA256 c5470fc50f1a3ae52fc91626acf82016d47a9edc57dcb4da98aacecc953e4b6c
MD5 fe86e599643052bcf9f7ed589bb792bc
BLAKE2b-256 606ef32b9c70e132b6e36eceec4f22b784baa60c86695704858c88fd9ad7bb7f

See more details on using hashes here.

File details

Details for the file iaparc_inference-0.5.7-py3-none-any.whl.

File metadata

File hashes

Hashes for iaparc_inference-0.5.7-py3-none-any.whl
Algorithm Hash digest
SHA256 74d53ca28b6681907e8e34136998635aa526e337e8143df379f35317a7916c61
MD5 7fb465cc6aead75b78fefccbb2b16475
BLAKE2b-256 edf97e5f2a128d96fb623b686d3abfbda4be3ec63d96860c103ad70ec4e9bedd

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