Axect/DeepLearning_Tutorial2

PyTorch Template Project

This project provides a flexible template for PyTorch-based machine learning experiments. It includes configuration management, logging with Weights & Biases (wandb), hyperparameter optimization with Optuna, and a modular structure for easy customization and experimentation.

Project Structure

• config.py: Defines the RunConfig and OptimizeConfig classes for managing experiment configurations and optimization settings.
• main.py: The entry point of the project, handling command-line arguments and experiment execution.
• model.py: Contains the model architecture (currently an MLP).
• util.py: Utility functions for data loading, device selection, training, and analysis.
• configs/run_template.yaml: Template for run configuration.
• configs/optimize_template.yaml: Template for optimization configuration.
• analyze.py: Script for analyzing completed runs and optimizations, utilizing functions from util.py.

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/pytorch_template.git
   cd pytorch_template

2. Install the required packages:

   # Use pip
   pip install torch wandb rich beaupy polars numpy optuna matplotlib scienceplots
   
   # Or Use uv with sync requirements.txt (recommended)
   uv pip sync requirements.txt
   
   # Or Use uv (fresh install)
   uv pip install -U torch wandb rich beaupy polars numpy optuna matplotlib scienceplots

3. (Optional) Set up a Weights & Biases account for experiment tracking.

Usage

1. Configure your experiment by modifying configs/run_template.yaml or creating a new YAML file based on it.

2. (Optional) Configure hyperparameter optimization by modifying configs/optimize_template.yaml or creating a new YAML file based on it.

3. Run the experiment:

   python main.py --run_config path/to/run_config.yaml [--optimize_config path/to/optimize_config.yaml]

   If --optimize_config is provided, the script will perform hyperparameter optimization using Optuna.

4. Analyze the results:

   python analyze.py

Configuration

Run Configuration (run_template.yaml)

• project: Project name for wandb logging
• device: Device to run on (e.g., 'cpu', 'cuda:0')
• net: Model class to use
• optimizer: Optimizer class
• scheduler: Learning rate scheduler class
• epochs: Number of training epochs
• batch_size: Batch size for training
• seeds: List of random seeds for multiple runs
• net_config: Model-specific configuration
• optimizer_config: Optimizer-specific configuration
• scheduler_config: Scheduler-specific configuration

Optimization Configuration (optimize_template.yaml)

• study_name: Name of the optimization study
• trials: Number of optimization trials
• seed: Random seed for optimization
• metric: Metric to optimize
• direction: Direction of optimization ('minimize' or 'maximize')
• sampler: Optuna sampler configuration
• pruner: (Optional) Pruner configuration
• search_space: Definition of the hyperparameter search space

Customization

• Custom model: Modify or add models in model.py. Models should accept a hparams argument as a dictionary, with keys matching the net_config parameters in the run configuration YAML file.

• Custom data: Modify the load_data function in util.py. The current example uses Cosine regression. The load_data function should return train and validation datasets compatible with PyTorch's DataLoader.

• Custom training: Customize the Trainer class in util.py by modifying step, train_epoch, val_epoch, and train methods to suit your task. Ensure that train returns val_loss or a custom metric for proper hyperparameter optimization.

Features

• Configurable experiments using YAML files
• Integration with Weights & Biases for experiment tracking
• Hyperparameter optimization using Optuna
• Support for multiple random seeds
• Flexible model architecture (currently MLP)
• Device selection (CPU/CUDA)
• Learning rate scheduling
• Analysis tools for completed runs and optimizations

Analysis

The analyze.py script utilizes functions from util.py to analyze completed runs and optimizations. Key functions include:

• select_group: Select a run group for analysis
• select_seed: Select a specific seed from a run group
• select_device: Choose a device for analysis
• load_model: Load a trained model and its configuration
• load_study: Load an Optuna study
• load_best_model: Load the best model from an optimization study

These functions are defined in util.py and used within analyze.py.

To use the analysis tools:

1. Run the analyze.py script:

   python analyze.py
   

2. Follow the prompts to select the project, run group, and seed (if applicable).

3. The script will load the selected model and perform basic analysis, such as calculating the validation loss.

4. You can extend the main() function in analyze.py to add custom analysis as needed, utilizing the utility functions from util.py.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is provided as a template and is intended to be freely used, modified, and distributed. Users of this template are encouraged to choose a license that best suits their specific project needs.

For the template itself:

• You are free to use, modify, and distribute this template.
• No attribution is required, although it is appreciated.
• The template is provided "as is", without warranty of any kind.

When using this template for your own project, please remember to:

1. Remove this license section or replace it with your chosen license.
2. Ensure all dependencies and libraries used in your project comply with their respective licenses.

For more information on choosing a license, visit choosealicense.com.

Appendix

PFL (Predicted Final Loss) Pruner

Overview

The PFL pruner is a custom pruner that helps optimize hyperparameter search by early stopping unpromising trials. It maintains top k trials based on validation loss and prunes trials if their predicted final loss is worse than the worst saved PFL.

Key Features

• Maintains top k trials based on validation loss
• Predicts final loss using loss history
• Supports multiple random seeds
• Compatible with Optuna's pruning interface

Configuration

In your optimize_template.yaml, configure the pruner under the pruner section:

pruner:
  name: pruner.PFLPruner
  kwargs:
    n_startup_trials: 10    # Number of trials to run before pruning starts
    n_warmup_epochs: 10     # Number of epochs to run before pruning can occur
    top_k: 10              # Number of best trials to maintain
    target_epoch: 50       # Target epoch for final loss prediction

Parameters

• n_startup_trials: Number of trials to run before pruning starts
• n_warmup_epochs: Number of epochs to wait before pruning can occur within each trial
• top_k: Number of best trials to maintain for comparison
• target_epoch: Target epoch number used for final loss prediction

How It Works

1. For the first n_startup_trials, all trials run without pruning
2. Within each trial, no pruning occurs during the first n_warmup_epochs
3. After warmup:
   • The pruner maintains a list of top k trials based on validation loss
   • For each trial, it predicts the final loss using the loss history
   • If a trial's predicted final loss is worse than all saved trials, it is pruned