curriculumguard 0.1.1

Training-time adaptive data curriculum for PyTorch with safety guards

🛡 CurriculumGuard

Training-Time Data Control for PyTorch

CurriculumGuard is an open-source training-time control system that dynamically adapts which samples a model sees during training using live learning dynamics — while enforcing stability via rollback-based safety guards.

Models and optimizers are controlled.
Hyperparameters are tuned.
But the data stream itself has been ignored — until now.

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🔥 Why CurriculumGuard?

Modern datasets are:

• Noisy
• Imbalanced
• Web-scraped
• Non-stationary

CurriculumGuard introduces a missing layer in ML infrastructure:

Adaptive Data Curriculum with Stability-First Control

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⚙ Installation

pip install curriculumguard

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🚀 Quick Start

1️⃣ Dataset must return sample IDs

def __getitem__(self, idx):
    return idx, data, label

2️⃣ Wrap training loop

from curriculum_guard.core.guard import CurriculumGuard
from curriculum_guard.sampler.adaptive_sampler import AdaptiveSampler

guard = CurriculumGuard(train_dataset)

for epoch in range(epochs):
    sampler = AdaptiveSampler(train_dataset, guard.bucketer.bucketize(), guard.weights)
    loader = DataLoader(train_dataset, sampler=sampler)

    for ids, x, y in loader:
        logits = model(x)
        loss   = criterion(logits, y)
        guard.profiler.update(ids, loss.detach(), logits.detach(), y)
        loss.mean().backward()
        optimizer.step()
        optimizer.zero_grad()

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🧠 Signals Observed

Signal	Meaning
EMA loss	Sample difficulty
Loss variance	Label noise
Prediction entropy	Model uncertainty
Forgetting events	Harmful samples
Exposure count	Over-training risk

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🛡 Safety

CurriculumGuard enforces rollback when instability or regression is detected.

Policy is advisory. Safety is authoritative.

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📊 Benchmarks

Task	Baseline	CurriculumGuard
AG News + Noise	68%	74%
FashionMNIST 35% noise	84%	87.5%
Fraud Recall	slow	fast high recall
Continual Drift	fragile	stable

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📜 License

MIT