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Project Overview for Freelancer: Creating a GPT-NeoX Code Completion Notebook

Objective:

Develop a comprehensive Colab notebook that sets up a GPT-NeoX environment for training a Python code completion model from scratch. The notebook should be designed for experimentation with different training algorithms and model architectures, suitable for running on a T4 GPU (such as those available on Google Colab). Additionally, the project emphasizes collaboration via GitHub and experiment tracking using MLFlow.

Key Requirements:

  1. Training from Scratch:

    • The model should be trained from scratch, not fine-tuned from pre-trained models.
    • Focus on exploring various training algorithms and their impact on model performance.

  2. Python Code Completion Task:

    • The model will be trained to perform code completion on Python code.
    • Utilize a suitable Python code dataset for training and evaluation.

  3. Resource Constraints:

    • Ensure the notebook is optimized to run on a single T4 GPU (~16GB VRAM).
    • Implement strategies to handle memory limitations and training efficiency.

  4. Collaboration via GitHub:

    • Use GitHub as the central repository for code sharing and version control.
    • Organize the repository for clarity and ease of collaboration.

  5. Experiment Tracking with MLFlow:

    • Integrate MLFlow for logging, tracking experiments, and visualizing results.
    • Facilitate collaboration by sharing MLFlow dashboards and reports.

Notebook Structure and Content:

The notebook should be structured to guide users through the entire process, with clear explanations and modular code sections. Below is an outline of the expected content:

1. Introduction:

  • Objective Statement:
    • Briefly describe the goal of training a GPT-NeoX model for Python code completion from scratch.
    • Emphasize the focus on exploring training algorithms.

3. Data Preparation:

  • Dataset Selection:

    • Use a suitable Python code dataset, such as CodeSearchNet (Python subset) or a custom-curated dataset.
    • Ensure the dataset complies with licensing requirements.

  • Data Downloading and Processing:

    • Provide code to download and preprocess the dataset.
    • Include steps to clean and format the data for training.

  • Tokenization:

    • Train a custom tokenizer suitable for Python code.
    • Use a character-level tokenizer or a subword tokenizer trained on the code dataset.

4. Model Configuration:

  • Define a Custom GPT-NeoX Configuration:

    • Create a YAML configuration file tailored for training on a T4 GPU.
    • Adjust model parameters to balance performance and resource constraints.

  • Explanation of Configuration Choices:

    • Provide rationale for the chosen hyperparameters.

5. Training Loop with MLFlow Integration:

  • Configure MLFlow for Experiment Tracking:

    • Integrate MLFlow into the training script to log metrics, hyperparameters, and artifacts.

  • Implement the Training Loop:

    • Use GPT-NeoX's training scripts with modifications to include MLFlow logging.
    • Ensure the training loop captures necessary metrics for analysis.

6. Experimentation with Training Algorithms:

  • Modular Code for Easy Modification:

    • Structure code to allow easy swapping of training algorithms and hyperparameters.

  • Instructions for Running Different Experiments:

    • Explain how to modify configuration files or command-line arguments to test different setups.
    • Encourage users to document their experiments using MLFlow.

7. Evaluation and Benchmarking:

  • Define Evaluation Metrics:

    • Use appropriate metrics for code completion, such as perplexity, token accuracy, or BLEU scores.

  • Implement Evaluation Scripts:

    • Include code to evaluate the model on a public benchmark
    • Log evaluation metrics to MLFlow for comparison.

  • Visualization of Results:

    • Show how to visualize training progress and evaluation results using MLFlow dashboards.

8. Collaborative Features:

  • GitHub Integration:

    • Ensure the notebook and associated files are structured and commented for collaborative development.
    • Include a README.md file with instructions on setting up the environment and running experiments.
    • Use Git branches or pull requests for collaborative code reviews.

  • MLFlow Collaboration:

    • Utilize MLFlow's team features to share experiment results and dashboards.

Collaboration and Project Management:

  • GitHub Repository Setup:

    • Create a well-organized GitHub repository with the following structure:

      /root
├── notebooks
│   └── code_completion_notebook.ipynb
├── configs
│   └── code_completion.yaml
├── data
│   └── (Include instructions for data download)
├── tokenizer
│   └── (Tokenizer files)
├── README.md
├── .gitignore
└── requirements.txt

  • Version Control Practices:

    • Use meaningful commit messages.
    • Encourage the use of branches for developing new features or experiments.

  • Issue Tracking and Discussion:

    • Utilize GitHub Issues to track bugs, feature requests, and tasks.
    • Use GitHub Discussions or Pull Request comments for team communication.

MLFlow Integration:

  • Project Setup:

    • Create a MLFlow project dedicated to this code completion model.

  • Team Collaboration:

    • Invite team members to the MLFlow project for shared access to experiment logs and dashboards.

  • Experiment Tracking:

    • Log hyperparameters, training metrics, model checkpoints, and evaluation results.
    • Use MLFlow's comparison features to analyze different training runs.

Expectations from the Freelancer:

  • Deliverables:

    • A fully functional Colab notebook (code_completion_notebook.ipynb) meeting the requirements above.
    • Necessary configuration files and scripts stored in the GitHub repository.
    • Clear instructions and comments within the notebook to guide users through each step.

  • Code Quality:

    • Write clean, well-documented code following best practices.
    • Ensure reproducibility by setting random seeds and documenting library versions.

  • Collaboration:

    • Use GitHub for all code-related activities.
    • Set up the MLFlow project and ensure proper logging throughout the training process.

  • Communication:

    • Provide regular updates on progress.
    • Be open to feedback and ready to make revisions based on team input.

Additional Considerations:

  • Licensing and Data Privacy:

    • Ensure compliance with data licensing.
    • Avoid including any proprietary or sensitive information in the repository.

  • Testing and Validation:

    • Validate that the notebook runs smoothly end-to-end on a T4 GPU in Colab.
    • Test different configurations to ensure flexibility and robustness.

  • User Experience:

    • Aim for a seamless experience for users running the notebook.
    • Include checkpoints and instructions for resuming training if the Colab session disconnects.

Conclusion:

The goal is to create a collaborative and experimental environment where team members can explore different training algorithms for GPT-NeoX models focused on Python code completion. By leveraging GitHub for code sharing and MLFlow for experiment tracking, the project aims to foster a collaborative learning experience. The notebook should serve as both a practical tool for experimentation and an educational resource for the team.