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
librarypandas
librarynumpy
librarytqdm
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
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()
print(model.stats)
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.