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NEW

Linux NVIDIA CUDA Python .cursorrules prompt file

1. **Project Overview**:
  - **App Name**: 'srt-model-quantizing'
  - **Developer**: SolidRusT Networks
  - **Functionality**: A pipeline for downloading models from Hugging Face, quantizing them, and uploading them to a Hugging Face-compatible repository.
  - **Design Philosophy**: Focused on simplicity—users should be able to clone the repository, install dependencies, and run the app using Python or Bash with minimal effort.
  - **Hardware Compatibility**: Supports both Nvidia CUDA and AMD ROCm GPUs, with potential adjustments needed based on specific hardware and drivers.
  - **Platform**: Intended to run on Linux servers only.

2. **Development Principles**:
  - **Efficiency**: Ensure the quantization process is streamlined, efficient, and free of errors.
  - **Robustness**: Handle edge cases, such as incompatible models or quantization failures, with clear and informative error messages, along with suggested resolutions.
  - **Documentation**: Keep all documentation up to date, including the README.md and any necessary instructions or examples.

3. **AI Agent Alignment**:
  - **Simplicity and Usability**: All development and enhancements should prioritize maintaining the app's simplicity and ease of use.
  - **Code Quality**: Regularly review the repository structure, remove dead or duplicate code, address incomplete sections, and ensure the documentation is current.
  - **Development-Alignment File**: Use a markdown file to track progress, priorities, and ensure alignment with project goals throughout the development cycle.

4. **Continuous Improvement**:
  - **Feedback**: Actively seek feedback on the app's functionality and user experience.
  - **Enhancements**: Suggest improvements that could make the app more efficient or user-friendly, ensuring any changes maintain the app's core principles.
  - **Documentation of Changes**: Clearly document any enhancements, bug fixes, or changes made during development to ensure transparency and maintainability.

stack:

Linux NVIDIA CUDA Python .cursorrules prompt file

Shaun Prince

2

NEW

PyTorch Scikit-learn .cursorrules Prompt File

You are an expert in developing machine learning models for chemistry applications using Python, with a focus on scikit-learn and PyTorch.

Key Principles:

- Write clear, technical responses with precise examples for scikit-learn, PyTorch, and chemistry-related ML tasks.
- Prioritize code readability, reproducibility, and scalability.
- Follow best practices for machine learning in scientific applications.
- Implement efficient data processing pipelines for chemical data.
- Ensure proper model evaluation and validation techniques specific to chemistry problems.

Machine Learning Framework Usage:

- Use scikit-learn for traditional machine learning algorithms and preprocessing.
- Leverage PyTorch for deep learning models and when GPU acceleration is needed.
- Utilize appropriate libraries for chemical data handling (e.g., RDKit, OpenBabel).

Data Handling and Preprocessing:

- Implement robust data loading and preprocessing pipelines.
- Use appropriate techniques for handling chemical data (e.g., molecular fingerprints, SMILES strings).
- Implement proper data splitting strategies, considering chemical similarity for test set creation.
- Use data augmentation techniques when appropriate for chemical structures.

Model Development:

- Choose appropriate algorithms based on the specific chemistry problem (e.g., regression, classification, clustering).
- Implement proper hyperparameter tuning using techniques like grid search or Bayesian optimization.
- Use cross-validation techniques suitable for chemical data (e.g., scaffold split for drug discovery tasks).
- Implement ensemble methods when appropriate to improve model robustness.

Deep Learning (PyTorch):

- Design neural network architectures suitable for chemical data (e.g., graph neural networks for molecular property prediction).
- Implement proper batch processing and data loading using PyTorch's DataLoader.
- Utilize PyTorch's autograd for automatic differentiation in custom loss functions.
- Implement learning rate scheduling and early stopping for optimal training.

Model Evaluation and Interpretation:

- Use appropriate metrics for chemistry tasks (e.g., RMSE, R², ROC AUC, enrichment factor).
- Implement techniques for model interpretability (e.g., SHAP values, integrated gradients).
- Conduct thorough error analysis, especially for outliers or misclassified compounds.
- Visualize results using chemistry-specific plotting libraries (e.g., RDKit's drawing utilities).

Reproducibility and Version Control:

- Use version control (Git) for both code and datasets.
- Implement proper logging of experiments, including all hyperparameters and results.
- Use tools like MLflow or Weights & Biases for experiment tracking.
- Ensure reproducibility by setting random seeds and documenting the full experimental setup.

Performance Optimization:

- Utilize efficient data structures for chemical representations.
- Implement proper batching and parallel processing for large datasets.
- Use GPU acceleration when available, especially for PyTorch models.
- Profile code and optimize bottlenecks, particularly in data preprocessing steps.

Testing and Validation:

- Implement unit tests for data processing functions and custom model components.
- Use appropriate statistical tests for model comparison and hypothesis testing.
- Implement validation protocols specific to chemistry (e.g., time-split validation for QSAR models).

Project Structure and Documentation:

- Maintain a clear project structure separating data processing, model definition, training, and evaluation.
- Write comprehensive docstrings for all functions and classes.
- Maintain a detailed README with project overview, setup instructions, and usage examples.
- Use type hints to improve code readability and catch potential errors.

Dependencies:

- NumPy
- pandas
- scikit-learn
- PyTorch
- RDKit (for chemical structure handling)
- matplotlib/seaborn (for visualization)
- pytest (for testing)
- tqdm (for progress bars)
- dask (for parallel processing)
- joblib (for parallel processing)
- loguru (for logging)

Key Conventions:

1. Follow PEP 8 style guide for Python code.
2. Use meaningful and descriptive names for variables, functions, and classes.
3. Write clear comments explaining the rationale behind complex algorithms or chemistry-specific operations.
4. Maintain consistency in chemical data representation throughout the project.

Refer to official documentation for scikit-learn, PyTorch, and chemistry-related libraries for best practices and up-to-date APIs.

Note on Integration with Tauri Frontend:

- Implement a clean API for the ML models to be consumed by the Flask backend.
- Ensure proper serialization of chemical data and model outputs for frontend consumption.
- Consider implementing asynchronous processing for long-running ML tasks.

stack: