jepa 0.1.11

Joint-Embedding Predictive Architecture for Self-Supervised Learning

JEPA Framework

A powerful self-supervised learning framework for Joint-Embedding Predictive Architecture (JEPA)

Installation • Quick Start • Documentation • Examples • Contributing

🚀 Overview

JEPA (Joint-Embedding Predictive Architecture) is a cutting-edge self-supervised learning framework that learns rich representations by predicting parts of the input from other parts. This implementation provides a flexible, production-ready framework for training JEPA models across multiple modalities.

Key Features

🔧 Modular Design

• Flexible encoder-predictor architecture
• Support for any PyTorch model as encoder/predictor
• Easy to extend and customize for your specific needs

🌍 Multi-Modal Support

• Computer Vision: Images, videos, medical imaging
• Natural Language Processing: Text, documents, code
• Time Series: Sequential data, forecasting, anomaly detection
• Audio: Speech, music, environmental sounds
• Multimodal: Vision-language, audio-visual learning

⚡ High Performance

• Mixed precision training (FP16/BF16)
• Native DistributedDataParallel (DDP) support
• Memory-efficient implementations
• Optimized for both research and production

📊 Comprehensive Logging

• Weights & Biases integration
• TensorBoard support
• Console logging with rich formatting
• Multi-backend logging system

🎛️ Production Ready

• CLI interface for easy deployment
• Flexible YAML configuration system
• Comprehensive testing suite
• Docker support and containerization
• Type hints throughout

🏗️ Architecture

JEPA follows a simple yet powerful architecture:

graph LR
    A[Input Data] --> B[Context/Target Split]
    B --> C[Encoder]
    C --> D[Joint Embedding Space]
    D --> E[Predictor]
    E --> F[Target Prediction]
    F --> G[Loss Computation]

The model learns by:

1. Splitting input into context and target regions
2. Encoding both context and target separately
3. Predicting target embeddings from context embeddings
4. Learning representations that capture meaningful relationships

📦 Installation

From PyPI (Recommended)

pip install jepa

From Source

git clone https://github.com/dipsivenkatesh/jepa.git
cd jepa
pip install -e .

Development Installation

git clone https://github.com/dipsivenkatesh/jepa.git
cd jepa
pip install -e ".[dev,docs]"

Docker

docker pull dipsivenkatesh/jepa:latest
docker run -it dipsivenkatesh/jepa:latest

🚀 Quick Start

Python API

import torch
from torch.utils.data import DataLoader, TensorDataset

from jepa.models import JEPA
from jepa.models.encoder import Encoder
from jepa.models.predictor import Predictor
from jepa.trainer import create_trainer

# Toy dataset of (state_t, state_t1) pairs
dataset = TensorDataset(torch.randn(256, 16, 128), torch.randn(256, 16, 128))
train_loader = DataLoader(dataset, batch_size=8, shuffle=True)

# Build model components
encoder = Encoder(hidden_dim=128)
predictor = Predictor(hidden_dim=128)
model = JEPA(encoder=encoder, predictor=predictor)

# Trainer with sensible defaults
trainer = create_trainer(model, learning_rate=3e-4, device="auto")

# Train for a couple of epochs
trainer.train(train_loader, num_epochs=2)

# Optional: stream metrics to Weights & Biases
trainer_ddp = create_trainer(
    model,
    learning_rate=3e-4,
    device="auto",
    logger="wandb",
    logger_project="jepa-experiments",
    logger_run_name="quickstart-run",
)

# Persist weights for downstream inference
model.save_pretrained("artifacts/jepa-small")

# Reload using the same model class
reloaded = JEPA.from_pretrained("artifacts/jepa-small", encoder=encoder, predictor=predictor)

Distributed Training (DDP)

Launch multi-GPU jobs with PyTorch's launcher:

torchrun --nproc_per_node=4 scripts/train.py --config config/default_config.yaml

Inside your training script, enable DDP when you create the trainer:

trainer = create_trainer(
    model,
    distributed=True,
    world_size=int(os.environ["WORLD_SIZE"]),
    local_rank=int(os.environ.get("LOCAL_RANK", 0)),
)

The trainer wraps the model in DistributedDataParallel, synchronizes losses, and restricts logging/checkpointing to rank zero automatically.

Action-Conditioned Variant

Use JEPAAction when actions influence the next state. Provide a state encoder, an action encoder, and a predictor that consumes the concatenated [z_t, a_t] embedding.

from jepa import JEPAAction
import torch.nn as nn

state_dim = 512
action_dim = 64

# Example encoders (replace with your own)
state_encoder = nn.Sequential(
    nn.Flatten(),
    nn.Linear(784, state_dim),
)
action_encoder = nn.Sequential(
    nn.Linear(10, 128), nn.ReLU(),
    nn.Linear(128, action_dim),
)

# Predictor takes [state_dim + action_dim] → state_dim
predictor = nn.Sequential(
    nn.Linear(state_dim + action_dim, 512), nn.ReLU(),
    nn.Linear(512, state_dim),
)

model = JEPAAction(state_encoder, action_encoder, predictor)

Command Line Interface

# Train a model
jepa-train --config config/default_config.yaml

# Train with custom parameters
jepa-train --config config/vision_config.yaml \
           --batch-size 64 \
           --learning-rate 0.001 \
           --num-epochs 100

# Evaluate a trained model
jepa-evaluate --config config/default_config.yaml \
              --checkpoint checkpoints/best_model.pth

# Generate a configuration template
jepa-train --generate-config my_config.yaml

# Get help
jepa-train --help

Configuration

JEPA uses YAML configuration files for easy experiment management:

# config/my_experiment.yaml
model:
  encoder_type: "transformer"
  encoder_dim: 768
  predictor_type: "mlp"
  predictor_hidden_dim: 2048

training:
  batch_size: 32
  learning_rate: 0.0001
  num_epochs: 100
  warmup_epochs: 10

data:
  train_data_path: "data/train"
  val_data_path: "data/val"
  sequence_length: 16

logging:
  wandb:
    enabled: true
    project: "jepa-experiments"
  tensorboard:
    enabled: true
    log_dir: "./tb_logs"

🎯 Use Cases

Computer Vision

• Image Classification: Pre-train backbones for downstream tasks
• Object Detection: Learn robust visual representations
• Medical Imaging: Analyze medical scans and imagery
• Satellite Imagery: Process large-scale geographic data

Natural Language Processing

• Language Models: Pre-train transformer architectures
• Document Understanding: Learn document-level representations
• Code Analysis: Understand code structure and semantics
• Cross-lingual Learning: Build multilingual representations

Time Series Analysis

• Forecasting: Pre-train models for prediction tasks
• Anomaly Detection: Learn normal patterns in sequential data
• Financial Modeling: Analyze market trends and patterns
• IoT Sensors: Process sensor data streams

Multimodal Learning

• Vision-Language: Combine images and text understanding
• Audio-Visual: Learn from synchronized audio and video
• Cross-Modal Retrieval: Search across different modalities
• Embodied AI: Integrate multiple sensor modalities

📚 Examples

Vision Example

from jepa import JEPADataset, JEPATrainer, load_config
import torch.nn as nn

# Custom vision encoder
class VisionEncoder(nn.Module):
    def __init__(self, input_dim=3, hidden_dim=512):
        super().__init__()
        self.conv_layers = nn.Sequential(
            nn.Conv2d(input_dim, 64, 7, 2, 3),
            nn.ReLU(),
            nn.Conv2d(64, 128, 3, 2, 1),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d((1, 1)),
            nn.Flatten(),
            nn.Linear(128, hidden_dim)
        )
    
    def forward(self, x):
        return self.conv_layers(x)

# Load config and customize
config = load_config("config/vision_config.yaml")
trainer = JEPATrainer(config=config, custom_encoder=VisionEncoder)
trainer.train()

NLP Example

from transformers import AutoModel
from jepa import JEPA, JEPATrainer

# Use pre-trained transformer as encoder
class TransformerEncoder(nn.Module):
    def __init__(self, model_name="bert-base-uncased"):
        super().__init__()
        self.transformer = AutoModel.from_pretrained(model_name)
    
    def forward(self, input_ids, attention_mask=None):
        outputs = self.transformer(input_ids, attention_mask=attention_mask)
        return outputs.last_hidden_state.mean(dim=1)  # Pool over sequence

config = load_config("config/nlp_config.yaml")
trainer = JEPATrainer(config=config, custom_encoder=TransformerEncoder)
trainer.train()

Time Series Example

from jepa import create_dataset, JEPATrainer

# Create time series dataset
dataset = create_dataset(
    data_path="data/timeseries.csv",
    sequence_length=50,
    prediction_length=10,
    features=['sensor1', 'sensor2', 'sensor3']
)

config = load_config("config/timeseries_config.yaml")
trainer = JEPATrainer(config=config, train_dataset=dataset)
trainer.train()

📖 Documentation

• Full Documentation - Complete API reference and guides
• Installation Guide - Detailed installation instructions
• Configuration Guide - How to configure your experiments
• Training Guide - Training best practices
• API Reference - Complete API documentation

🔧 Development

Setting up Development Environment

git clone https://github.com/dipsivenkatesh/jepa.git
cd jepa

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

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

# Install pre-commit hooks
pre-commit install

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=jepa --cov-report=html

# Run specific test
pytest tests/test_model.py::test_jepa_forward

Code Quality

# Format code
black jepa/
isort jepa/

# Type checking
mypy jepa/

# Linting
flake8 jepa/

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Ways to Contribute

• 🐛 Bug Reports: Submit detailed bug reports
• ✨ Feature Requests: Suggest new features or improvements
• 📖 Documentation: Improve documentation and examples
• 🔧 Code: Submit pull requests with bug fixes or new features
• 🎯 Use Cases: Share your JEPA applications and results

Development Workflow

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes and add tests
4. Ensure all tests pass: pytest
5. Submit a pull request

📄 Citation

If you use JEPA in your research, please cite:

@software{jepa2025,
  title = {JEPA: Joint-Embedding Predictive Architecture Framework},
  author = {Venkatesh, Dilip},
  year = {2025},
  url = {https://github.com/dipsivenkatesh/jepa},
  version = {0.1.0}
}

📝 License

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

🙏 Acknowledgments

• Inspired by the original JEPA paper and Meta's research
• Built with PyTorch, Transformers, and other amazing open-source libraries
• Thanks to all contributors and users of the framework

📞 Support

• GitHub Issues: Report bugs or request features
• Documentation: Read the full documentation
• Discussions: Join community discussions

Steps to push latest version: rm -rf dist build .egg-info python -m build twine upload dist/

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Built with ❤️ by Dilip Venkatesh