haitongli/knowledge-distillation-pytorch
Knowledge Distillation Pytorch
This project is a framework for implementing knowledge distillation and managing deep learning experiments within the PyTorch ecosystem. It provides a structured environment for training compact student models to mimic the output distributions of larger teacher models, aiming to improve inference efficiency.
The framework distinguishes itself by decoupling model architectures from loss functions, allowing for flexible composition of teacher and student components. It integrates automated hyperparameter grid search capabilities to systematically identify optimal training configurations, which are managed through external file serialization to ensure consistency across experimental runs.
Beyond its core distillation and optimization functions, the toolkit includes comprehensive lifecycle management for training sessions. This includes persistent checkpointing for fault recovery, real-time progress monitoring, and the aggregation of performance metrics into structured summaries for comparative analysis.
Features
- Model Distillation Frameworks - Provides a PyTorch-based toolkit for training compact student models to mimic the performance of larger teacher models.
- Model Distillation Methods - Enables training compact student models to mimic the behavior of larger teacher models for improved inference efficiency.
- Teacher-Student Distillation - Trains compact student models by minimizing divergence between their output distributions and those of larger teacher models.
- Deep Learning Experiment Trackers - Organizes training configurations and tracks performance metrics to ensure reproducible research across sessions.
- Hyperparameter Optimization Tools - Automates systematic searches across parameter ranges to identify the most effective settings for deep learning models.
- Training Lifecycle Management - Manages the end-to-end training lifecycle, including checkpointing and metric logging for reliable session monitoring.
- Training Configuration Management - Centralizes training settings in external files to ensure consistency and eliminate complex command-line arguments.
- Training Progress Monitoring - Provides real-time progress indicators and logging dashboards to maintain visibility into training experiments.
- Hyperparameter Optimization - Automates systematic hyperparameter searches to identify optimal settings and synthesize performance metrics.
- Modular Architectures - Constructs training pipelines by decoupling model architectures from loss functions for flexible component swapping.
- Training Checkpoint Persistence - Persists model weights and training states to disk to enable fault recovery and experiment reproducibility.
- Automated Parameter Searches - Automates systematic grid searches across parameter ranges to identify optimal training configurations.
- Configuration File Serializers - Loads training parameters from structured external files to decouple configuration from source code.
- Experiment Result Comparators - Aggregates metrics from multiple training runs into structured summaries to facilitate direct comparison of model configurations.
Star history
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