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Introducing Trainer

OpenTrace v0.2 introduces the powerful Trainer module, a comprehensive framework for training and optimizing AI agents with advanced algorithms and evaluation capabilities.

🎯 Overview

The Trainer module provides a unified interface for: - Algorithm Selection: Choose from various optimization algorithms - Evaluation Metrics: Comprehensive performance assessment - Learning Strategies: Advanced training methodologies - Data Loading: Efficient batch processing and data management

🚀 Key Features

Advanced Algorithms

The Trainer supports multiple state-of-the-art optimization algorithms:

from opto.trainer import Trainer
from opto.trainer.algorithms import BeamSearchAlgorithm, UCBSearch

# Initialize trainer with beam search
trainer = Trainer(
    algorithm=BeamSearchAlgorithm(beam_size=10),
    evaluator=your_evaluator,
    max_iterations=100
)

# Or use UCB for exploration-exploitation balance
trainer = Trainer(
    algorithm=UCBSearch(confidence=0.95),
    evaluator=your_evaluator
)

Comprehensive Evaluation

Built-in evaluators provide detailed performance metrics:

from opto.trainer.evaluators import MultiMetricEvaluator

evaluator = MultiMetricEvaluator([
    'accuracy',
    'latency', 
    'cost',
    'robustness'
])

trainer = Trainer(
    algorithm=your_algorithm,
    evaluator=evaluator
)

Data Loading and Preprocessing

Efficient data handling for training workflows:

from opto.trainer.loader import DataLoader

loader = DataLoader(
    batch_size=32,
    shuffle=True,
    preprocessing_fn=your_preprocessing
)

trainer.fit(loader)

📊 Training Workflows

Basic Training Loop

# Simple training setup
trainer = Trainer(
    algorithm=BeamSearchAlgorithm(),
    evaluator=your_evaluator,
    logger=ConsoleLogger()
)

# Train your agent
results = trainer.fit(
    train_data=train_loader,
    validation_data=val_loader,
    epochs=50
)

print(f"Best performance: {results.best_score}")

Advanced Configuration

# Advanced trainer with custom settings
trainer = Trainer(
    algorithm=UCBSearch(
        confidence=0.95,
        exploration_weight=0.1
    ),
    evaluator=MultiMetricEvaluator(['accuracy', 'efficiency']),
    logger=MLFlowLogger(),
    early_stopping=True,
    patience=10
)

# Custom training callbacks
trainer.add_callback('on_epoch_end', custom_callback)
trainer.fit(data_loader)

🔧 Customization

Custom Algorithms

Extend the framework with your own algorithms:

from opto.trainer.algorithms import Algorithm

class CustomAlgorithm(Algorithm):
    def __init__(self, custom_param=1.0):
        self.custom_param = custom_param

    def suggest(self, history):
        # Your custom suggestion logic
        return suggested_parameters

    def update(self, parameters, score):
        # Update algorithm state
        pass

Custom Evaluators

Create domain-specific evaluation metrics:

from opto.trainer.evaluators import Evaluator

class DomainSpecificEvaluator(Evaluator):
    def evaluate(self, agent_output, ground_truth):
        # Your custom evaluation logic
        return {
            'custom_metric': score,
            'additional_info': metadata
        }

📈 Integration with Existing Code

The Trainer seamlessly integrates with your existing OpenTrace workflows:

import opto

@opto.trace
def my_agent(query):
    # Your existing agent code
    return response

# Wrap with trainer for optimization
trainer = Trainer(
    target_function=my_agent,
    algorithm=BeamSearchAlgorithm(),
    evaluator=your_evaluator
)

# Optimize your agent
optimized_agent = trainer.optimize()

🎯 Best Practices

  1. Start Simple: Begin with basic algorithms and gradually add complexity
  2. Monitor Training: Use comprehensive logging and visualization tools
  3. Validate Frequently: Implement robust validation strategies
  4. Experiment: Try different algorithms and hyperparameters
  5. Scale Gradually: Start with small datasets and scale up

📚 Learn More

Ready to start training? Check out our comprehensive trainer tutorial for hands-on examples!