import os import sys import shutil import tempfile from huggingface_hub import create_repo, HfApi def print_banner(): banner = """ ====================================================================== HUGGING FACE HUB DEPLOYMENT WIZARD | PROFILE: ARAVINDHAN11 ====================================================================== """ print(banner) def write_docker_assets(directory_path, space_title): """ Writes a Dockerfile and README.md with spaces metadata for Hugging Face Spaces. """ dockerfile_content = """FROM python:3.12-slim # Install essential system dependencies RUN apt-get update && apt-get install -y --no-install-recommends \\ build-essential \\ && rm -rf /var/lib/apt/lists/* WORKDIR /app # Install Python packages RUN pip install --no-cache-dir torch transformers datasets rich huggingface_hub # Copy all project files COPY . . # Expose port (HF Spaces defaults to 7860) EXPOSE 7860 ENV PORT=7860 ENV HOME=/tmp # Run command center server CMD ["python", "server.py"] """ readme_content = f"""--- title: {space_title} emoji: šŸ§  colorFrom: indigo colorTo: purple sdk: docker pinned: false --- # {space_title} An advanced and intelligent distributed transformer training and inference framework. ## Key Features - **Hugging Face Hub Connection**: Convert, load, and push models. - **Playground**: High-performance streaming chat. - **Fine-Tuning**: Live loss chart. - **Parallelism**: 3D Parallelism status. Developed by **Aravindhan11**. """ with open(os.path.join(directory_path, "Dockerfile"), "w", encoding="utf-8") as f: f.write(dockerfile_content) with open(os.path.join(directory_path, "README.md"), "w", encoding="utf-8") as f: f.write(readme_content) def deploy_space(token, space_name): """ Deploys the entire framework dashboard as a Docker Space on Hugging Face. """ repo_id = f"Aravindhan11/{space_name}" print(f"\nInitializing Hugging Face Space repository: {repo_id}...") try: # Create Hugging Face Space repository create_repo( repo_id=repo_id, token=token, repo_type="space", space_sdk="docker", private=False, exist_ok=True ) print("Space repository initialized successfully.") # Create a temporary directory to bundle files, avoiding copying large .git folders print("Bundling project assets for container building...") current_dir = os.path.dirname(os.path.abspath(__file__)) with tempfile.TemporaryDirectory() as temp_dir: # Copy source files for item in os.listdir(current_dir): s = os.path.join(current_dir, item) d = os.path.join(temp_dir, item) # Exclude large cache directories, virtual environments, and checkpoints if item in [".git", ".venv", "__pycache__", "exported_checkpoints", "node_modules", ".gemini"]: continue if os.path.isdir(s): shutil.copytree(s, d) else: shutil.copy(s, d) # Write Docker assets into bundle write_docker_assets(temp_dir, "Distributed LLaMA Framework") print("Uploading bundled files to Hugging Face Spaces (this might take a minute)...") api = HfApi() api.upload_folder( folder_path=temp_dir, repo_id=repo_id, repo_type="space", token=token, commit_message="Deploy Intelligent Distributed LLaMA Framework" ) space_url = f"https://huggingface.co/spaces/{repo_id}" print(f"\nšŸŽ‰ SUCCESS! Your Intelligent Framework is now building and hosting on Hugging Face Spaces!") print(f"šŸ‘‰ Access your public dashboard here: {space_url}") print("Hugging Face will automatically build the Docker image and deploy it. Enjoy!") except Exception as e: print(f"\nāŒ Error during deployment: {e}") def deploy_model(token, repo_name): """ Converts and deploys a small LLaMA model configuration and pre-trained weights to Hugging Face Hub. """ repo_id = f"Aravindhan11/{repo_name}" print(f"\nPreparing to upload pre-trained custom model weights to Model Hub: {repo_id}...") try: from hf_converter import HFWeightConverter # Download and map a small SmolLM model locally to convert it base_model_name = "HuggingFaceTB/SmolLM-135M-Instruct" print(f"Downloading pre-trained base model ({base_model_name}) to convert keys...") model, config, tokenizer = HFWeightConverter.load_and_convert_hf(base_model_name) # Save custom checkpoints locally local_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "exported_checkpoints", repo_name) print(f"Reversing weight names and writing standard config files to {local_dir}...") HFWeightConverter.save_custom_to_hf_format( custom_model=model, hf_config=config, tokenizer=tokenizer, save_directory=local_dir ) # Push folder to HF repo_url = HFWeightConverter.push_to_huggingface( local_dir=local_dir, repo_id=repo_id, token=token ) print(f"\nšŸŽ‰ SUCCESS! Your custom LLaMA model has been converted and hosted on Hugging Face Model Hub!") print(f"šŸ‘‰ View your model repository here: {repo_url}") except Exception as e: print(f"\nāŒ Error during model deployment: {e}") def main(): print_banner() # 1. Ask for credentials (use env var HF_TOKEN if set) token = os.getenv("HF_TOKEN") if not token: token = input("Enter your Hugging Face User Write Token (hf_...): ").strip() if not token: print("Error: Hugging Face Token is required.") sys.exit(1) # 2. Ask for deployment mode (use env var DEPLOY_MODE if set) print("\nWhat would you like to host on Hugging Face?") print(" [1] Entire Intelligent Web Dashboard (as an interactive Hugging Face Space)") print(" [2] Custom LLaMA Model Config & Weights (as a Hugging Face Model Repository)") choice = os.getenv("DEPLOY_MODE") if not choice: choice = input("\nSelect option (1 or 2): ").strip() if choice == "1": space_name = os.getenv("SPACE_NAME") if not space_name: space_name = input("Enter Space Name (default: Distributed-Transformer-Framework): ").strip() if not space_name: space_name = "Distributed-Transformer-Framework" deploy_space(token, space_name) elif choice == "2": repo_name = os.getenv("REPO_NAME") if not repo_name: repo_name = input("Enter Model Repository Name (default: Distributed-Llama-Model): ").strip() if not repo_name: repo_name = "Distributed-Llama-Model" deploy_model(token, repo_name) else: print("Invalid option selected. Exiting.") if __name__ == "__main__": main()