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.