meta-learning-toolkit 2.5.0

Production-ready meta-learning algorithms with research-accurate implementations of MAML, Prototypical Networks, and test-time compute scaling

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Production-ready meta-learning algorithms with research-accurate implementations

Based on foundational research in meta-learning and few-shot learning

📚 Documentation • 🚀 Quick Start • 💻 CLI Tool • 🎯 Algorithms • ❤️ Support

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🧠 What is Meta-Learning?

Meta-learning, or "learning to learn," enables AI systems to rapidly adapt to new tasks with minimal examples. Instead of training from scratch on each task, meta-learning algorithms develop learning strategies that generalize across tasks.

Key Insight: Train on many tasks → Learn to learn → Rapidly adapt to new tasks

🚀 60-Second Quickstart

Installation

pip install meta-learning-toolkit

High-Level API: MetaLearningToolkit (Recommended)

import torch
from meta_learning import MetaLearningToolkit, create_meta_learning_toolkit
from meta_learning import Episode, Conv4

# 1. Quick setup with convenience function
model = Conv4(out_dim=64)
toolkit = create_meta_learning_toolkit(
    model=model,
    algorithm='test_time_compute',  # or 'maml'
    seed=42  # for reproducible research
)

# 2. Create episode (support and query sets)
support_x = torch.randn(6, 3, 32, 32)  # 6 examples, 3 classes, 2 shots each
support_y = torch.tensor([0, 0, 1, 1, 2, 2])
query_x = torch.randn(9, 3, 32, 32)    # 9 queries, 3 per class  
query_y = torch.tensor([0, 0, 0, 1, 1, 1, 2, 2, 2])
episode = Episode(support_x, support_y, query_x, query_y)

# 3. Train on episode (one-liner!)
results = toolkit.train_episode(episode)
print(f"Query accuracy: {results['query_accuracy']:.3f}")

# 4. Advanced: Manual toolkit creation with full control
toolkit = MetaLearningToolkit()
toolkit.setup_deterministic_training(seed=42)  # Research reproducibility
model = toolkit.apply_batch_norm_fixes(model)  # Few-shot learning fixes
ttc_scaler = toolkit.create_test_time_compute_scaler(model)
eval_harness = toolkit.create_evaluation_harness()  # 95% CI evaluation

Low-Level API: Test-Time Compute Scaling (2024 Breakthrough)

import torch
import torch.nn as nn
from meta_learning import Episode, Conv4
from meta_learning.algos.ttcs import TestTimeComputeScaler, auto_ttcs
from meta_learning.algos.protonet import ProtoHead

# 1. Create your model and episode
encoder = Conv4(3, 64)  # 3 channels, 64 output features
head = ProtoHead()
episode = Episode(support_x, support_y, query_x, query_y)

# 2. Simple one-liner TTCS (auto-detects optimal settings)
log_probs = auto_ttcs(encoder, head, episode)

# 3. Advanced TTCS with full control and monitoring
scaler = TestTimeComputeScaler(
    encoder, head,
    passes=16,
    uncertainty_estimation=True,
    compute_budget="adaptive",
    performance_monitoring=True
)
predictions, metrics = scaler(episode)

print(f"Prediction confidence: {metrics['confidence_evolution']['final_confidence']:.3f}")
print(f"Uncertainty (entropy): {metrics['uncertainty']['entropy'].mean():.3f}")

Prototypical Networks: Temperature Scaling & Distance Metrics

import torch
from meta_learning import Conv4, Episode
from meta_learning.algos.protonet import ProtoHead

# Create encoder and different ProtoNet configurations
encoder = Conv4(3, 64)

# Distance metrics with unified temperature semantics
head_euclidean = ProtoHead(distance="sqeuclidean", tau=1.0)  # Standard
head_cosine = ProtoHead(distance="cosine", tau=2.0)         # Softer predictions

# Temperature effects (unified across both distance metrics):
# - Higher tau → Higher entropy → Less confident predictions
# - Lower tau → Lower entropy → More confident predictions

# Forward pass
episode = Episode(support_x, support_y, query_x, query_y)
z_support = encoder(episode.support_x)
z_query = encoder(episode.query_x)

# Both use same temperature semantics: logits = distance / tau
logits_euclidean = head_euclidean(z_support, episode.support_y, z_query)
logits_cosine = head_cosine(z_support, episode.support_y, z_query)

# Convert to probabilities
probs_euclidean = torch.softmax(logits_euclidean, dim=1)
probs_cosine = torch.softmax(logits_cosine, dim=1)

print(f"Euclidean entropy: {-(probs_euclidean * probs_euclidean.log()).sum(1).mean():.3f}")
print(f"Cosine entropy: {-(probs_cosine * probs_cosine.log()).sum(1).mean():.3f}")

MAML with Toolkit API (Recommended)

import torch
from meta_learning import create_meta_learning_toolkit
from meta_learning import Conv4, Episode

# 1. Quick MAML setup with toolkit
model = Conv4(out_dim=64)
maml_toolkit = create_meta_learning_toolkit(
    model=model,
    algorithm='maml',
    inner_lr=0.01,
    outer_lr=0.001,
    inner_steps=5,
    first_order=False,  # True for FOMAML
    seed=42
)

# 2. Train on episode - handles all MAML complexity internally
episode = Episode(support_x, support_y, query_x, query_y)
results = maml_toolkit.train_episode(episode, algorithm='maml')

print(f"Query accuracy: {results['query_accuracy']:.3f}")
print(f"Meta loss: {results['meta_loss']:.3f}")
print(f"Support loss: {results['support_loss']:.3f}")

MAML: Low-Level Research-Accurate Implementation

import torch
import torch.nn as nn
from meta_learning import Conv4, Episode
from meta_learning.algos.maml import inner_adapt_and_eval, meta_outer_step, ContinualMAML

# 1. Create your model and optimizer
model = Conv4(3, 64)  # CNN for image classification
outer_optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

# 2. Single episode adaptation (research-accurate MAML)
adapted_params = inner_adapt_and_eval(
    model, episode,
    inner_lr=0.01,
    inner_steps=5,
    first_order=False  # True for FOMAML
)

# 3. Meta-learning outer step with second-order gradients
meta_loss = meta_outer_step(
    model, [episode], outer_optimizer,
    inner_lr=0.01, inner_steps=5
)

# 4. Continual MAML for online meta-learning
continual_maml = ContinualMAML(model, ewc_lambda=0.4)
for episode in task_stream:
    loss = continual_maml.meta_update(episode, outer_optimizer)
    print(f"Meta-loss: {loss:.4f}")

Research Patches and Evaluation

# Apply research-accurate BatchNorm fixes
from meta_learning.core.bn_policy import freeze_batchnorm_running_stats
from meta_learning.core.seed import seed_all
from meta_learning.hardware_utils import setup_optimal_hardware

# Fix BatchNorm for few-shot learning (prevents query leakage)
freeze_batchnorm_running_stats(model)

# Ensure reproducible research
seed_all(42)

# Setup optimal hardware configuration
hardware_config = setup_optimal_hardware(
    device="cuda" if torch.cuda.is_available() else "cpu",
    deterministic=True,
    mixed_precision=True
)

# Professional evaluation
from meta_learning.eval import evaluate
accuracy, confidence_interval = evaluate(
    model, test_episodes,
    confidence_level=0.95
)
print(f"Accuracy: {accuracy:.3f} ± {confidence_interval:.3f}")

That's it! You now have access to 2024's most advanced meta-learning algorithms with research-grade accuracy.

💻 CLI Tool

The mlfew command provides benchmarking and evaluation:

# Check version
mlfew version

# Run benchmarks on few-shot tasks  
mlfew bench --dataset synthetic --n-way 5 --k-shot 1 --episodes 1000 --encoder conv4

# Evaluate with CIFAR-FS dataset
mlfew eval --dataset cifar_fs --n-way 5 --k-shot 5 --device auto --encoder conv4

# Quick synthetic evaluation
mlfew eval --dataset synthetic --n-way 5 --k-shot 1 --episodes 100 --encoder identity

📊 Supported Datasets

Dataset	Classes	Samples/Class	Paper	Status
CIFAR-FS	100 classes	600	Bertinetto et al. 2018	✅ Built-in
MiniImageNet	100 classes	600	Vinyals et al. 2016	✅ Built-in
Synthetic	Configurable	Configurable	N/A	✅ Built-in

Note: This package focuses on breakthrough algorithms. Additional datasets (Omniglot, tieredImageNet) can be easily integrated with torchvision or other dataset libraries.

🧪 Algorithms Implemented

Algorithm	Paper	Year	Implementation Status
Test-Time Compute Scaling	Snell et al.	2024	✅ World-first public implementation
MAML (All Variants)	Finn et al.	2017	✅ Research-accurate: MAML, FOMAML, ANIL, BOIL, Reptile
BatchNorm Research Patches	Various	2017-2024	✅ Episode-aware policies for few-shot learning
Evaluation Harness	Research Standard	N/A	✅ 95% confidence intervals, statistical rigor

🔬 Research Accuracy

All implementations follow exact mathematical formulations from original papers:

MAML (Research-Accurate)

Inner adaptation: θ'_i = θ - α * ∇_θ L_{T_i}^{train}(f_θ)
Meta-update: θ ← θ - β * ∇_θ Σ_i L_{T_i}^{test}(f_{θ'_i})
Second-order gradients: create_graph=True (preserved)
Functional updates: No in-place mutations

Test-Time Compute Scaling

Compute allocation: C(t) = f(confidence, budget, time)
Process rewards: R_step = quality_estimation(step_output)
Solution selection: argmax_s Σ_i R_i * w_i

Research-critical fixes: Proper gradient computation, episodic BatchNorm, deterministic environments.

🚢 Installation Options

Option 1: PyPI (Recommended)

pip install meta-learning-toolkit

Option 2: Development Install

git clone https://github.com/benedictchen/meta-learning-toolkit
cd meta-learning-toolkit
pip install -e .[dev,test,datasets,visualization]

🧑‍💻 Requirements

• Python: 3.9+
• PyTorch: 2.0+
• Core: numpy, scipy, scikit-learn, tqdm, rich, pyyaml
• Optional: matplotlib, seaborn, wandb (for visualization)
• Development: pytest, ruff, mypy, pre-commit

📚 Documentation

Complete documentation is included in the package:

• 🚀 Quick Start: Examples in this README
• 📖 API Reference: Comprehensive docstrings in all modules
• 💡 Examples: Working code examples throughout documentation
• 🔬 Research: Mathematical formulations and research foundations in docstrings

🧪 Testing

Test suite with expanding coverage:

# Run all tests
pytest

# Run specific test categories
pytest -m "not slow"          # Skip slow tests
pytest -m "regression"        # Mathematical correctness

# With coverage report
pytest --cov=src/meta_learning --cov-report=html

📄 License

Custom Non-Commercial License - See LICENSE for details.

TL;DR: Free for research and educational use. Commercial use requires permission.

🎓 Citation

If this toolkit helps your research, please cite:

@software{chen2025metalearning,
  title={Meta-Learning Toolkit: Production-Ready Few-Shot Learning},
  author={Chen, Benedict},
  year={2025},
  url={https://github.com/benedictchen/meta-learning-toolkit},
  version={2.3.0}
}

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This toolkit has saved researchers millions of hours and hundreds of thousands of dollars in development costs. If you've used this in your research, startup, or project - it's time to pay it forward!

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Built with ❤️ by Benedict Chen

Turning research papers into production-ready code