Tutorials

This Smart Compiler uses AI models and traditional compiler techniques to enhance the performance scalability of C programs and Python programs. By profiling, and finding approaches for optimizations.

Sofwate Requirements

Install the project

For dependency management and installation, this project uses uv. See Astral Documentation for installing the uv package manager.

Project dependencies

Packages

After installing uv run: uv sync for syncing project dependencies.

Ollama

To deploy a LLM using ollama first we need to install Ollama by following its Official Documentation.

Once Ollama is installed deploy the Ollama server (if it was not deployed by the installation).

Quick Ollama deploy

1. Serve the Ollama server: ollama serve (if it is not already deployed).
2. Create LLM model using the SmartCompiler Modelfile: ollama create llama3.1-smart-compiler -f ollama-smart-compiler-Modelfile.
3. Run the created LLM: ollama run llama3.1-smart-compiler:latest.
4. If it opens a chat after running the LLM, just type /bye to close that chat.

Setting up Environment variables

Set up the environment variables in a .env file. An example of how this file looks like.

# .env
OLLAMA_MODEL=llama3.1-smart-compiler:latest
OLLAMA_HOST=http://localhost:11434
MCP_SERVER_SCRIPT_PATH=<project_folder>/src/server_main.py
MCP_SERVER_TRANSPORT=stdio
MCP_SERVER_HOST=0.0.0.0
MCP_SERVER_PORT=8000
MCP_SERVER_TRANSPORT=stdio
MCP_SERVER_OLLAMA_MODEL=llama3.1:latest
LOG_LEVEL=INFO # OR DEBUG

Then type : export $(cat .env | xargs)