MNIST digit classification using DSPy framework with support for various LLM backends. Configure optimizers and training iterations for optimal performance.

• DSPy-powered MNIST classification with support for multiple LLM backends
• Flexible optimizer selection (MIPROv2, BootstrapFewShot)
• Configurable training iterations and worker threads
• Model selection (supports any LLM compatible with DSPy)
• Response caching for faster iterations

git clone https://github.com/tom-doerr/mnist_dspy.git
cd mnist_dspy
pip install -r requirements.txt

mnist_dspy/
├── mnist_trainer.py    # Main training script with optimizer selection
├── mnist_data.py       # MNIST data loading and preprocessing
└── mnist_dspy.py       # DSPy model definitions

Train a model with specific optimizer and iterations:

# Use MIPROv2 optimizer with light optimization
python mnist_trainer.py --optimizer MIPROv2 --iterations 1 --model your-llm-model --auto light

# Use MIPROv2 with medium optimization and caching disabled
python mnist_trainer.py --optimizer MIPROv2 --iterations 1 --model your-llm-model --auto medium --no-cache

# Use BootstrapFewShot optimizer with custom number of workers
python mnist_trainer.py --optimizer BootstrapFewShot --iterations 1 --model your-llm-model --num-workers 50

• --optimizer: Choose between 'MIPROv2' or 'BootstrapFewShot' (default: MIPROv2)
• --iterations: Number of optimization iterations (default: 1)
• --model: Model identifier for your LLM
• --auto: Optimization intensity for MIPROv2 ['light', 'medium', 'heavy'] (default: light)
• --num-workers: Number of worker threads (default: 100)
• --no-cache: Disable LLM response caching

The trainer:

• Uses a subset of MNIST data for faster experimentation
• Processes 10,000 training examples
• Evaluates on 200 test examples
• Supports multi-threaded evaluation

Distributed under the MIT License. See LICENSE for more information.