graft-pytorch 0.1.7

Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling

GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling

A PyTorch implementation of smart sampling for efficient deep learning training.

Overview

GRAFT uses gradient information and feature decomposition to select the most informative samples during training, reducing computation time while maintaining model performance.

Features

• Smart sample selection using gradient-based importance scoring
• Multi-architecture support (ResNet, ResNeXT, EfficientNet, BERT)
• Dataset compatibility (CIFAR10/100, TinyImageNet, Caltech256, Medical datasets)
• Experiment tracking with Weights & Biases integration
• Carbon footprint tracking with eco2AI
• Efficient training with reduced computational overhead

Installation

From PyPI (Recommended)

pip install graft-pytorch

With optional dependencies

# For experiment tracking
pip install graft-pytorch[tracking]

# For development
pip install graft-pytorch[dev]

# Everything
pip install graft-pytorch[all]

From Source

git clone https://github.com/ashishjv1/GRAFT.git
cd GRAFT
pip install -e .

Quick Start

Command Line Interface

# Install and train with smart sampling
pip install graft-pytorch

# Basic training with GRAFT sampling on CIFAR-10
graft-train \
    --numEpochs=200 \
    --batch_size=128 \
    --device="cuda" \
    --optimizer="sgd" \
    --lr=0.1 \
    --numClasses=10 \
    --dataset="cifar10" \
    --model="resnet18" \
    --fraction=0.5 \
    --select_iter=25 \
    --warm_start

Python API

import torch
from graft import ModelTrainer, TrainingConfig
from graft.utils.loader import loader

# Load your dataset
trainloader, valloader, trainset, valset = loader(
    dataset="cifar10",
    trn_batch_size=128,
    val_batch_size=128
)

# Configure training with GRAFT
config = TrainingConfig(
    numEpochs=100,
    batch_size=128,
    device="cuda" if torch.cuda.is_available() else "cpu",
    model_name="resnet18",
    dataset_name="cifar10",
    trainloader=trainloader,
    valloader=valloader,
    trainset=trainset,
    optimizer_name="sgd",
    lr=0.1,
    fraction=0.5,         # Use 50% of data per epoch
    selection_iter=25,    # Reselect samples every 25 epochs
    warm_start=True       # Train on full data initially
)

# Train with smart sampling
trainer = ModelTrainer(config, trainloader, valloader, trainset)
train_stats, val_stats = trainer.train()

print(f"Best validation accuracy: {val_stats['best_acc']:.2%}")

Advanced Usage

from graft import feature_sel, sample_selection
import torch.nn as nn

# Custom model and data selection
model = MyCustomModel()
data3 = feature_sel(dataloader, batch_size=128, device="cuda")

# Manual sample selection
selected_indices = sample_selection(
    dataloader, data3, model, model.state_dict(),
    batch_size=128, fraction=0.3, select_iter=10,
    numEpochs=200, device="cuda", dataset="custom"
)

Functionality Overview

Core Components

1. Smart Sample Selection

• sample_selection(): Selects most informative samples using gradient-based importance
• feature_sel(): Performs feature decomposition for efficient sampling
• Reduces training time by 30-50% while maintaining model performance

2. Supported Models

• Vision Models: ResNet, ResNeXt, EfficientNet, MobileNet, FashionCNN
• Language Models: BERT for sequence classification
• Custom Models: Easy integration with any PyTorch model

3. Dataset Support

• Computer Vision: CIFAR-10/100, TinyImageNet, Caltech256
• Medical Imaging: Integration with MedMNIST datasets
• Custom Datasets: Support for any PyTorch DataLoader

4. Training Features

• Dynamic Sampling: Adaptive sample selection during training
• Warm Starting: Begin with full dataset, then switch to sampling
• Experiment Tracking: Built-in WandB integration
• Carbon Tracking: Monitor environmental impact with eco2AI

Configuration Parameters

Parameter	Description	Default	Options
numEpochs	Training epochs	200	Any integer
batch_size	Batch size	128	32, 64, 128, 256+
device	Computing device	"cuda"	"cpu", "cuda"
model	Model architecture	"resnet18"	"resnet18/50", "resnext", "efficientnet"
fraction	Data sampling ratio	0.5	0.1 - 1.0
select_iter	Reselection frequency	25	Any integer
optimizer	Optimization algorithm	"sgd"	"sgd", "adam"
lr	Learning rate	0.1	0.001 - 0.1
warm_start	Use full data initially	False	True/False
decomp	Decomposition backend	"numpy"	"numpy", "torch"

Performance Benefits

• Speed: 30-50% faster training time
• Memory: Reduced memory usage through smart sampling
• Accuracy: Maintains or improves model performance
• Efficiency: Lower carbon footprint and energy consumption

Package Structure

graft-pytorch/
├── graft/
│   ├── __init__.py          # Main package exports
│   ├── trainer.py           # Training orchestration
│   ├── genindices.py        # Sample selection algorithms
│   ├── decompositions.py    # Feature decomposition
│   ├── models/              # Supported architectures
│   │   ├── resnet.py        # ResNet implementations  
│   │   ├── efficientnet.py  # EfficientNet models
│   │   └── BERT_model.py    # BERT for classification
│   └── utils/               # Utility functions
│       ├── loader.py        # Dataset loaders
│       └── model_mapper.py  # Model selection
├── tests/                   # Comprehensive test suite
├── examples/                # Usage examples
└── OIDC_SETUP.md           # Deployment configuration

Contributing

We welcome contributions! Please see our contribution guidelines for details.

Development Setup

# Clone the repository
git clone https://github.com/ashishjv1/GRAFT.git
cd GRAFT

# Install in development mode
pip install -e .[dev]

# Run tests
pytest tests/ -v

# Run linting
flake8 graft/ tests/

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use GRAFT in your research, please cite our paper:

@misc{jha2025graftgradientawarefastmaxvol,
  title         = {GRAFT: Gradient-Aware Fast MaxVol Technique for Dynamic Data Sampling},
  author        = {Ashish Jha and Anh Huy Phan and Razan Dibo and Valentin Leplat},
  year          = {2025},
  eprint        = {2508.13653},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  url           = {https://arxiv.org/abs/2508.13653}
}

Acknowledgments

• Built using PyTorch
• Inspired by MaxVol techniques for data sampling
• Special thanks to the open-source community

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PyPI Package: graft-pytorch
Paper: arXiv:2508.13653
Issues: GitHub Issues
Contact: Ashish Jha