Welcome to the world of NaturalCC, a remarkable toolkit that bridges the realm of programming and natural language through advanced machine learning techniques. This guide will walk you through the steps needed to set up and utilize NaturalCC for various software engineering tasks such as code generation, completion, summarization, and more.

Vision of NaturalCC

NaturalCC is designed to empower researchers and developers to train custom models for software engineering tasks. Whether you’re generating code, summarizing it, or detecting clones, NaturalCC equips you with an extensive toolkit to harness the power of AI in software development.

Key Features

• Modular and Extensible Framework: Built on Fairseq’s robust registry mechanism for easy adaptation.
• Datasets and Preprocessing Tools: Access to clean, preprocessed benchmarks with feature extraction scripts.
• Support for Large Code Models: Incorporates state-of-the-art models like Code Llama and CodeGen.
• Benchmarking and Evaluation: Evaluate models against well-known benchmarks using popular metrics.
• Optimized for Efficiency: Leverage multi-GPU support and mixed precision computations for faster training.
• Enhanced Logging: Detailed logging features for effective debugging and performance optimization.

Installation Guide

Follow the steps below to set up NaturalCC on your system:

1. Creating a Conda Environment (Optional):

   conda create -n naturalcc python=3.6
   conda activate naturalcc

2. Building NaturalCC from Source:

   git clone https://github.com/CGCL-codes/naturalcc
   cd naturalcc
   pip install -r requirements.txt
   cd src
   pip install --editable .

3. Installing Additional Dependencies:

   conda install conda-forge::libsndfile
   pip install -q -U git+https://github.com/huggingface/transformers.git
   pip install -q -U git+https://github.com/huggingface/accelerate.git

4. Getting HuggingFace Token: For certain large code models, you must log in to HuggingFace:

   huggingface-cli login

Quick Start Examples

Let’s explore a couple of examples to get you started with NaturalCC.

Example 1: Code Generation

1. Download the model checkpoint of your choice, for example, Codellama-7B.
2. Create a JSON file with your test cases.
3. Run the code generation scripts:

python
print("Initializing GenerationTask")
task = GenerationTask(task_name='codellama_7b_code', device='cuda:0')
print("Loading model weights [{}]".format(ckpt_path))
task.from_pretrained(ckpt_path)
print("Processing dataset [{}]".format(dataset_path))
task.load_dataset(dataset_path)
task.run(output_path=output_path, batch_size=1, max_length=50)
print("Output file: [{}]".format(output_path))

Example 2: Code Summarization

1. Download and process your dataset following the README from relevant instructions.
2. Register your self-defined models in the ncc/models and ncc/modules directories.
3. Train and perform inference:

CUDA_VISIBLE_DEVICES=0,1,2,3 nohup python -m run.summarization.transformer.train -f config/python_wan/python --log-file runsummarizationtransformerconfigpython_wanpython.log 21
CUDA_VISIBLE_DEVICES=0 python -m run.summarization.transformer.eval -f config/python_wan/python -o runsummarizationtransformerconfigpython_wanpython.txt

Troubleshooting and Support

While setting up NaturalCC, you may encounter some challenges. Here are a few troubleshooting ideas:

• If you face issues with dependencies, ensure that all required versions specified in the requirements are installed correctly.
• For CUDA-related errors, ensure that your NVIDIA drivers and library are configured correctly and are compatible with the version of PyTorch you are using.
• Check the Issues page on the GitHub repository for solutions to common problems.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.