Is your feature request related to a problem? Please describe.
Motivation: Automating selection of deep neural network training pipelines with the following specific requirements:

• Re-implement AutoML modules and pipeline under monai/apps/automl;
• Implement module demo in tutorials;
• Use monai.bundle to provide users with easy-to-read Python scripts and options to call the functions in bash and native python.

Describe the solution you'd like
Progress: #4765
Usage Proposal
https://github.com/mingxin-zheng/tutorials/blob/auto3d-1.0/auto3d/README.md

Tasks

• Encapsulate data analysis module in a Python class. The module shall find data and label from user inputs and generate a summary (dictionary) of data stats. The summary includes
  • file names, list, number of files;
  • dataset summary (basic information, image dimensions, number of classes, etc.);
  • individual data information (spacing, image size, number and size of the regions, etc.).
• Encapsulate the configuration module in a Python class. The module shall use the data stats dict/file to generate task related directory and Python code scripts (train.py/main.py/infer.py) for each specific algorithm
  • Implement an algorithm selection class to select algorithms for next step. The class is aimed to enable future smart algorithm selection;
  • It should use the monai.bundle to provide a native Python programming experience to the user
  • Note: for the current 1.0 release aim, only one network will be implemented
• Encapsulate the multi-fold training processes in a class, or implement them in the pipeline class (see below)
• Encapsulate the ensemble module in a class
• Develop a new pipeline class to run all or selective modules sequentially, and provide results (segmentation predictions) to the user
  • Develop a pipeline for users to directly adopt trained/searched models for testing purpose (model infernece and ensemble) with their own datasets;
• Python scripts in tutorials to demonstrate each of the modules and the pipeline class.