OmniTensor provides a sophisticated infrastructure for fine-tuning custom AI models leveraging its decentralized compute network. This guide explores advanced techniques for optimizing pre-trained models to suit specific use cases within the OmniTensor ecosystem.
Overview
Fine-tuning allows developers to adapt pre-existing models to specific domains or tasks, significantly reducing the time and resources required compared to training from scratch. OmniTensor's distributed fine-tuning pipeline enables efficient and scalable model optimization across its decentralized network.
Key Components
Distributed Fine-Tuning Orchestrator
Adaptive Learning Rate Scheduler
Gradient Accumulation and Sharding
Automated Hyperparameter Optimization
Cross-Validation on Decentralized Data
Fine-Tuning Process
1. Preparing Your Model and Data
Initialize your fine-tuning job with the OmniTensor SDK:
Copy from omnitensor import FineTuningJob, ModelRepository, DatasetManager
base_model = ModelRepository.get("gpt-3-small")
custom_dataset = DatasetManager.load("my_domain_specific_data")
job = FineTuningJob(
base_model=base_model,
dataset=custom_dataset,
task_type="text_classification"
)
2. Configuring Fine-Tuning Parameters
Set up advanced fine-tuning parameters:
Copy from omnitensor.finetuning import AdaptiveLearningRate, GradientAccumulation
job.set_hyperparameters(
epochs=10,
batch_size=32,
learning_rate=AdaptiveLearningRate(
initial_lr=5e-5,
warmup_steps=1000,
decay_strategy="cosine"
),
gradient_accumulation=GradientAccumulation(
num_micro_batches=4
)
)
3. Implementing Custom Loss Functions
Define a custom loss function for your specific task:
Copy import torch
from omnitensor.finetuning import LossFunction
class CustomFocalLoss(LossFunction):
def __init__(self, alpha=0.25, gamma=2.0):
self.alpha = alpha
self.gamma = gamma
def calculate(self, predictions, targets):
bce_loss = torch.nn.functional.binary_cross_entropy_with_logits(predictions, targets, reduction='none')
pt = torch.exp(-bce_loss)
focal_loss = self.alpha * (1 - pt) ** self.gamma * bce_loss
return focal_loss.mean()
job.set_loss_function(CustomFocalLoss(alpha=0.3, gamma=2.5))
4. Leveraging Distributed Training
Utilize OmniTensor's distributed training capabilities:
Copy from omnitensor.finetuning import DistributedConfig
job.set_distributed_config(DistributedConfig(
num_nodes=5,
gpu_per_node=4,
communication_backend="nccl"
))
5. Implementing Custom Callbacks
Create custom callbacks for monitoring and intervention during training:
Copy from omnitensor.finetuning import Callback
class EarlyStoppingCallback(Callback):
def __init__(self, patience=3, min_delta=0.001):
self.patience = patience
self.min_delta = min_delta
self.best_score = float('inf')
self.counter = 0
def on_epoch_end(self, epoch, logs):
current_score = logs['val_loss']
if current_score < self.best_score - self.min_delta:
self.best_score = current_score
self.counter = 0
else:
self.counter += 1
if self.counter >= self.patience:
self.model.stop_training = True
job.add_callback(EarlyStoppingCallback(patience=5, min_delta=0.0005))
6. Automated Hyperparameter Optimization
Leverage OmniTensor's automated hyperparameter tuning:
Copy from omnitensor.finetuning import HyperparameterOptimization, HyperparameterSpace
hp_space = HyperparameterSpace(
learning_rate={"min": 1e-5, "max": 1e-3, "scale": "log"},
batch_size=[16, 32, 64, 128],
num_layers=[2, 3, 4, 5]
)
hp_optimization = HyperparameterOptimization(
strategy="bayesian",
num_trials=50,
metric_to_optimize="val_f1_score",
direction="maximize"
)
job.enable_hp_optimization(hp_space, hp_optimization)
7. Cross-Validation on Decentralized Data
Implement cross-validation across the decentralized network:
Copy from omnitensor.finetuning import DecentralizedCrossValidation
cross_val = DecentralizedCrossValidation(
num_folds=5,
stratify=True,
shuffle=True
)
job.set_cross_validation(cross_val)
8. Launching the Fine-Tuning Job
Start the fine-tuning process:
Copy fine_tuned_model = job.run()
print(f"Fine-tuning completed. Model accuracy: {fine_tuned_model.evaluate()}")
Advanced Techniques
1. Progressive Layer Unfreezing
Gradually unfreeze layers during fine-tuning:
Copy from omnitensor.finetuning import ProgressiveUnfreezing
unfreezing = ProgressiveUnfreezing(
initial_frozen_layers=12,
unfreeze_every_n_epochs=2
)
job.set_layer_unfreezing(unfreezing)
2. Knowledge Distillation
Implement knowledge distillation for model compression:
Copy from omnitensor.finetuning import KnowledgeDistillation
distillation = KnowledgeDistillation(
teacher_model=ModelRepository.get("gpt-3-large"),
temperature=2.0,
alpha=0.5
)
job.enable_knowledge_distillation(distillation)
3. Adversarial Training
Enhance model robustness with adversarial training:
Copy from omnitensor.finetuning import AdversarialTraining
adversarial_config = AdversarialTraining(
attack_type="pgd",
epsilon=0.01,
alpha=0.001,
num_steps=10
)
job.enable_adversarial_training(adversarial_config)
Monitoring and Analytics
Utilize OmniTensor's real-time monitoring tools during fine-tuning:
Copy from omnitensor.monitoring import FineTuningMonitor
monitor = FineTuningMonitor(job)
monitor.start()
# Fine-tuning process runs here
metrics = monitor.get_metrics()
print(f"Training loss: {metrics.train_loss}")
print(f"Validation accuracy: {metrics.val_accuracy}")
print(f"GPU utilization: {metrics.gpu_utilization}%")
monitor.stop()
Last updated 4 months ago