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Training Strategies¤

Advanced training strategies and patterns for Artifex generative models.

Overview¤

This guide covers advanced training strategies for optimizing your generative model training workflows.

Gradient Accumulation¤

Accumulate gradients over multiple steps to simulate larger batch sizes.

from artifex.generative_models.training import Trainer, TrainingConfig

config = TrainingConfig(
    num_epochs=100,
    batch_size=16,
    gradient_accumulation_steps=4,  # Effective batch size: 64
)

trainer = Trainer(model=model, training_config=config)
trainer.train(train_data)

Mixed Precision Training¤

Use bfloat16 for faster training while maintaining accuracy.

import jax

# Enable mixed precision
config = TrainingConfig(
    dtype="bfloat16",
    num_epochs=100,
)

trainer = Trainer(model=model, training_config=config)

Distributed Training¤

Scale training across multiple GPUs or TPUs.

import jax

# Detect available devices
devices = jax.local_devices()
print(f"Available devices: {len(devices)}")

# Data parallel training
config = TrainingConfig(
    num_epochs=100,
    distributed=True,
)

Curriculum Learning¤

Progressive training from simple to complex samples.

def create_curriculum_loader(dataset, epoch, max_epochs):
    """Create data loader with curriculum difficulty."""
    difficulty = min(1.0, epoch / (max_epochs * 0.5))
    return filter_by_difficulty(dataset, max_difficulty=difficulty)