learnware/docs/components/model.rst

.. _model:
================================
Model
================================

A learnware is a well-performed trained model with a specification, where the model is an indispensable component of the learnware.

In this section, we will introduce the model module implemented within ``learnware`` package. We will first introduce the ``BaseModel``, which defines the standard format for models in the ``learnware`` package. Following that, we will introduce the ``ModelContainer``, which implements model deployment in conda virtual environments and Docker containers.

BaseModel
======================================

The ``BaseModel`` class is a fundamental component of the learnware package which provides standardized interface for model training, prediction and fine-tuning.
This class is created to make it easier for users to submit learnwares to the market.
It helps ensure that submitted models follow a clear set of rules and requirements.

All user models should inherit the ``BaseModel`` class. Here's a more detailed explanation of key components:

- ``input_shape``: Specify the shape of the input features your model expects.
- ``output_shape``: Define the shape of the output predictions generated by your model.
- ``predict``: Implement the predict method to make predictions using your model.
- ``fit`` (optional): Use the fit method for training a model with input data and labels.
- ``finetune`` (optional): Utilize the finetune method for further adjusting pre-existing models sourced from the market.

By adhering to these standards, the compatibility and quality of submitted learnwares in the market are ensured.

ModelContainer
======================================

The ``ModelContainer`` class is an essential component of the learnware package, designed to facilitate the management, deployment, and execution of machine learning models within a containerized environment.
It inherits from the ``BaseModel`` class and extends its functionality to encapsulate model deployment and execution.

ModelCondaContainer
---------------------

The ``ModelCondaContainer`` class is an extension of the ``ModelContainer`` class within the learnware package.
Its primary purpose is to enable the management, deployment, and execution of machine learning models in a containerized environment, with a specific focus on using Conda virtual environments.
This class inherits functionality from ``ModelContainer`` while providing additional capabilities related to Conda-based model execution.

Specifically, the ``ModelCondaContainer`` supports the automatic creation of new Conda virtual environments based on the ``requirements.txt`` file (for pip installation) or ``environment.yaml`` file (for Conda installation) included within the learnware itself.
It also installs the environment dependencies of the learnware, enabling it to run.

ModelDockerContainer
---------------------

The ``ModelDockerContainer`` class is a specialized extension of the ``ModelContainer`` class within the learnware package.
It is designed to manage, deploy, and execute machine learning models within a containerized environment, specifically using Docker containers.
This class inherits functionality from ``ModelContainer`` and enhances it with features related to Docker-based model execution.

Compared to ``ModelCondaContainer``, ``ModelDockerContainer`` confines the model's execution within a Docker container.
It installs the learnware's virtual environment inside the Docker container, isolating the learnware's execution from the host machine, thus enhancing the security of the learnware.

Similar to the ``ModelCondaContainer`` class, the ``ModelDockerContainer`` class also supports both types of environment dependency files for learnware: ``requirements.txt`` for pip-based installation and ``environment.yaml`` for conda-based installation.
It automates the creation of Docker containers and the installation of learnware's environment dependencies within the container, enabling the learnware to run.