Tutorial: pytorch_template

This project provides a reusable template for building and running PyTorch machine learning experiments. It helps structure your code for training models, automatically finding the best hyperparameters using Optuna, and analyzing the results. Everything is managed through simple configuration files (YAML), making experiments easy to define, reproduce, and modify.

Source Repository: pytorch_template

flowchart TD
    A0["Configuration Management (RunConfig / OptimizeConfig)
"]
    A1["Experiment Execution Orchestration (main.py)
"]
    A2["Training Loop (Trainer)
"]
    A3["Model Definition (model.py)
"]
    A4["Hyperparameter Optimization (Optuna Integration)
"]
    A5["Pruning Strategy (PFLPruner)
"]
    A6["Analysis Utilities (analyze.py / util.py)
"]
    A0 -- "Provides Settings For" --> A1
    A0 -- "Specifies" --> A3
    A1 -- "Initiates Training Loop (vi..." --> A2
    A1 -- "Initiates Hyperparameter Op..." --> A4
    A2 -- "Trains/Evaluates" --> A3
    A2 -- "Reports Values To" --> A5
    A4 -- "Drives Training Trials" --> A2
    A4 -- "Uses" --> A5
    A6 -- "Loads Configuration" --> A0
    A6 -- "Loads Model" --> A3
    A6 -- "Loads Study Results" --> A4

Chapters

1. Configuration Management (RunConfig / OptimizeConfig)
2. Experiment Execution Orchestration (main.py)
3. Training Loop (Trainer)
4. Model Definition (model.py)
5. Hyperparameter Optimization (Optuna Integration)
6. Pruning Strategy (PFLPruner)
7. Analysis Utilities (analyze.py / util.py)

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