Examples

This directory contains examples of how to use the functionality offered by the mmlib.

Approaches to save and recover models

to execute all examples we use a MongoDB, in all examples the MongoDB is started using docker
if you don't have docker installed you have to either install it or slightly adjust the examples
in baseline_save.py we provide an example of how to save and recover a model using the baseline approach
for all other approaches we do not give explict examples and refer to our test for the appraoches

Probing Tool

We provide some basic examples to see the different use cases of the probing tool

Create a probe summary for a given model

probe_store.py - Creates and stores a probe summary of the training process of a GoogLeNet.
execution: python probe_store.py --path <optional path to store probe summary>

Create new summary and compare to given one

probe_load_compare.py - Creates a probe summary of the training process of a GoogLeNet and compares it to a stored probe summary
execution: python probe_load_compare.py --path <path to the already stored probe summary>
note: To generate and store a probe summary to compare to use the probe_store.py script.

Extensive example

probe_example.py - Shows extensively how the probe functionality offered by the mmlib can be used to make the PyTorch implementation of GoogLeNet reproducible. It runs the following steps:
- simple summary
  - creates a probe summary for the inference mode and prints the representation
- probe inference
  - creates two instances of the same model
  - creates inference mode probe summaries (covering forward path) for them
  - compares the probe summaries
- probe training
  - creates two instances of the same model
  - creates training mode probe summaries (covering forward and backward path)
  - compares the probe summaries
- probe reproducible training
  - creates two instances of the same model
  - uses set_deterministic functionality offered by the mmlib to make the training process of both models reproducible
  - creates training mode probe summaries (covering forward and backward path)
  - compares the probe summaries
  - compares both models using the methods blackbox_model_equal, whitebox_model_equal, and model_equal offered by the mmlib.