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aitomatic

aitomatic library to interact with Aitomatic product

  • 1.3.1
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WebModel Library User Manual

The WebModel library is a tool for building, tuning, and inference of models that are built with the Aitomatic system. The target users of this library are AI Engineers who use the Aitomatic system.

Requirements

  • Python 3.9 or higher
  • requests library
  • pandas library
  • numpy library
  • tqdm library

Installation

The WebModel library can be installed using pip:

pip install 'aitomatic>=1.2.0' --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple

Quick Start

To get started, you can create a WebModel object by passing in the model name and API token:

from aitomatic.api.web_model import WebModel

# load model
API_ACCESS_TOKEN = '<API_ACCESS_TOKEN>'
project_name="<project name>"
model_name = "<model name>"

model = WebModel(api_token=API_ACCESS_TOKEN, project_name=project_name, model_name=model_name)
model.load()

# view model training statistics and info
print(model.stats)

# run model inference
data = {'X':  my_dataframe}
response = model.predict({'X': data})
print(response['predictions'])

Methods

The WebModel class provides several methods for working with the model:

  • Constructor

    model_names = WebModel.get_model_names(api_token="YOUR_API_TOKEN", project_name="MyProject")
    
  • Load model load set up the model ready by loading all parameter from Aitomatic model repo.

    model.load()
    
    • Return the model with loaded params
  • Predict The predict method takes a dictionary as input with the data you want to make predictions on. The input data should be a pandas DataFrame, Series, or numpy array with the key "X". The method returns a dictionary with the predictions, with the key "predictions".

    response = model.predict(input_data={'X': df})
    
    • input_data: input data for prediction, dictionary with data under key 'X'
    • Return: result of the prediction call in a dictionary where the actual result is under prediction key
  • Tuning tune_model is a statis method to generate multiple versions of a given model with the set of input params

    tune_model(
        project_name=PROJECT_NAME,
        base_model=BASE_MODEL_NAME,
        conclusion_tuning_range=conclusion_threshold_ranges,
        ml_tuning_params=ML_MODELS_PARAMS,
        output_model_df_path='tuning.parquet',
        wait_for_tuning_to_complete=True,
        prefix="[HUNG7]",
    )
    
    • project_name: A string containing the name of the Aitomatic project to use.
    • base_model:A string containing the name of the base model to use.
    • conclusion_tuning_range: A dictionary specifying the range of values to use for the final layer of the tuned model.
    • ml_tuning_params: A dictionary specifying the AutoML tuning parameters to use.
    • output_model_df_path: A string specifying the path to save the resulting DataFrame containing the tuned model's hyperparameters and performance.
    • wait_for_tuning_to_complete: A boolean specifying whether to wait for the tuning process to complete before returning. Default is True.
    • prefix: A string containing a prefix to add to the name of the new model. Default is "finetune".
    • Return A Pandas DataFrame containing the hyperparameters and performance of the tuned model.
  • Log model metrics log_metrics is to save the model metric after evaluation

    model.log_metrics("accuracy", 0.95)
    
  • Get models in project static

    model_names = WebModel.get_model_names(api_token="YOUR_API_TOKEN", project_name="MyProject")
    
    • The api_token A string containing the access token for the Aitomatic API. If not provided, the AITOMATIC_API_TOKEN environment variable will be used.
    • The project_name A string containing the name of the Aitomatic project to use. If not provided, the AITOMATIC_PROJECT_ID environment variable will be used.
    • Return a list of the names of all models in the specified project.

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