# google-ai-edge/litert-torch

> Support PyTorch model conversion with LiteRT.

## Fast Facts & Telemetry
- **Canonical URL**: https://githubrepo.cloud/repo/google-ai-edge/litert-torch
- **GitHub Source**: https://github.com/google-ai-edge/litert-torch
- **Global Rank**: #6,154 on GitHubRepo
- **Stars**: 1,102
- **Forks**: 190
- **Open Issues**: 206
- **Primary Language**: Jupyter Notebook
- **Software License**: Apache-2.0
- **Last Commit / Push**: Oct 1, 2026

## Project Overview
litert-torch is an open-source project written in Jupyter Notebook: Support PyTorch model conversion with LiteRT.

Engineered for speed, consistency, and developer ease, it solves common hurdles in Jupyter Notebook. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.

## Key Features
- Optimized execution pipeline written in Jupyter Notebook for predictable speed.
- Zero-friction configuration with comprehensive sensible defaults out of the box.
- Cross-platform runtime support across Linux, macOS, and Windows environments.
- Strong typing and modular architecture designed for easy extension and maintainability.
- Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
- Active community maintenance with regular dependency updates and security patches.

## Installation & Quick Start
### Linux
```bash
git clone https://github.com/google-ai-edge/litert-torch.git
cd litert-torch
```

### macOS
```bash
git clone https://github.com/google-ai-edge/litert-torch.git
cd litert-torch
```

### Windows
```bash
git clone https://github.com/google-ai-edge/litert-torch.git
cd litert-torch
```

## System Requirements & Architecture
- **Supported Platforms**: macOS, Linux, Windows
- **Runtime Dependencies**: Jupyter Notebook environment and standard tooling
- **Architecture**: x86_64, ARM64 (Apple Silicon & Graviton)

## How It Works
litert-torch coordinates its core functionality through a modular Jupyter Notebook pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.

## Production Use Cases
### Production System Integration
Embed litert-torch into Jupyter Notebook backend services to handle core application logic.

### CI/CD Automated Pipelines
Run automated validation, builds, and integration suites during deployments.

### Developer Tooling & Workflows
Accelerate developer onboarding with pre-configured project utilities.

### Open Source Extension
Fork and customize internal modules under the repository's open Apache-2.0 license.

## Target Audience
Developers and engineering teams building with Jupyter Notebook, seeking reliable, tested, and actively maintained tooling for production workloads.

## Strengths
- Active community backing with 1,102 GitHub stars and verified adoption.
- Permissive open-source distribution under the Apache-2.0 license.
- Built in Jupyter Notebook for high execution speed and developer familiarity.
- Cross-platform compatibility across modern Linux, macOS, and Windows environments.
- Clean modular design allowing flexible configuration and pipeline integration.

## Considerations
- Requires familiarity with Jupyter Notebook and modern CLI workflows.
- Ecosystem extensions may require manual configuration depending on environment constraints.
- Active development roadmap means breaking API changes may occur across major versions.

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