Automated C/C++ to Rust Translation Pipeline

Overview

This project provides a reusable, automated pipeline to translate any C/C++ GitHub repository into Rust. It leverages Ollama to run LLMs locally, ensuring data privacy and efficient resource utilization. The pipeline is fully containerized using Docker.

Prerequisites

• Docker
• Docker Compose
• Git

Setup Instructions

1. Clone the Repository

git clone https://github.com/nrjchnd/translation-pipeline.git cd translation-pipeline

source scripts/setup_env.sh bash scripts/start.sh

Open your web browser and navigate to http://localhost:8000. Fill in the source and destination repository details to start the translation process.

Watch the logs: docker-compose logs -f

Access the db if required: docker exec -it $(docker-compose ps -q db) psql -U user -d translation

The translated Rust code will be pushed to the destination repository you specified. Review the generated README.md in the destination repository for compilation instructions

bash scripts/stop.sh

Components UI Service Located in the ui/ directory. Collects user input for repository details. Worker Service Located in the worker/ directory. Handles cloning, indexing, translation, compilation, and pushing code. Interacts with Ollama for code translation. Database Service Uses PostgreSQL. Stores repository info, file indexing, and translation status. Ollama Service Located in the models/ollama/ directory. Runs LLMs locally to perform code translation. Exposes API on port 11434. Version Control Service Optional GitLab instance provided in vcs/gitlab/. Can be replaced with any Git server. Notes Ensure that the LLMs used comply with their licensing agreements. Handle all credentials securely. The pipeline is designed to be extensible and scalable. Troubleshooting Services not starting: Ensure Docker and Docker Compose are installed and running. Database connection errors: Verify that the DATABASE_URL environment variable is correctly set. Translation errors: Check the logs for the worker service to identify issues.

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Additional Considerations**

• Entrypoint Script Adjustments:

  • The entrypoint.sh script in the Ollama service ensures the Ollama server starts when the container runs.

• Security and Privacy:

  • Running LLMs locally ensures that the source code does not leave the local environment.
  • This is particularly important for proprietary or sensitive codebases.

Testing the Updated Pipeline**

• Model Availability:

  • Before running the pipeline, ensure that the required LLMs are available locally in the Ollama service.

• Sample Repository:

  • Test the pipeline with a small, open-source C/C++ repository to validate functionality.
  • Check that files are correctly translated, compiled, and pushed to the destination repository.

• Error Handling:

  • Monitor logs for any errors during translation or compilation.
  • Adjust the prompts or handling in translate.py as necessary to improve translation quality.

Next Steps:

1. Run the Pipeline:

   • Start the services using the updated start.sh script.
   • Use the UI to initiate a translation job.

2. Monitor and Adjust:

   • Monitor the translation process.
   • If needed, adjust the translation prompts or model parameters to improve results.

3. Scale and Optimize:

   • Consider scaling the worker service if translating large repositories.
   • Optimize resource allocation in Docker to ensure efficient performance.