cwx-aiml-app-template 0.1.5

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CWX AIML App Template

A standardized project template generator for AIML (Artificial Intelligence and Machine Learning) applications. This tool helps teams maintain consistency across projects by providing a well-structured template with best practices built in.

Installation

pip install cwx-aiml-app-template

Basic Usage

Create a new AIML project:

aimlapp init <project_name>

Project Structure

The generated project follows this structure:

my_project_name/
├── api/                    # API-related code
│   ├── __init__.py
│   ├── endpoints.py        # API endpoint definitions
│   └── serializers.py      # Data serialization logic
│
├── data/                   # Data storage and processing
│   ├── external/           # Data from third-party sources
│   ├── interim/           # Intermediate processed data
│   ├── processed/         # Final, processed data
│   └── raw/               # Original, immutable data
│
├── evaluation/            # Model evaluation scripts
│   ├── __init__.py
│   └── metrics.py         # Evaluation metrics
│
├── examples/              # Example scripts and notebooks
│   ├── __init__.py
│   └── sample_usage.py
│
├── notebooks/            # Jupyter notebooks
│   └── exploratory.ipynb
│
├── src/                  # Source code
│   ├── config/          # Configuration files
│   │   ├── __init__.py
│   │   └── settings.py
│   ├── models/         # Model implementations
│   │   ├── __init__.py
│   │   └── base.py
│   └── utils/         # Utility functions
│       ├── __init__.py
│       └── helpers.py
│
├── tests/              # Test files
│   ├── __init__.py
│   ├── conftest.py    # pytest fixtures
│   └── test_models.py
│
├── Dockerfile         # Container definition
├── requirements.txt   # Project dependencies
├── README.md         # Project documentation
└── version.txt       # Version information

Directory Details

• /api

endpoints.py: Define your API endpoints here

serializers.py: Implement data serialization/deserialization

• /data

/external: Data from third-party sources

/interim: Intermediate data that has been transformed

/processed: Final, canonical data sets for modeling

/raw: Original, immutable data dumps

• /evaluation

Contains scripts for model evaluation:

metrics.py: Implementation of evaluation metrics

Custom evaluation scripts can be added here

• /examples

Example implementations and usage:

sample_usage.py: Basic usage examples

Add more example scripts as needed

• /notebooks

Jupyter notebooks for:

Data exploration

Model experimentation

Result visualization

Analysis presentation

• /src

Main source code:

/config: Configuration management

/models: Model implementations

/utils: Utility functions and helpers

• /tests

Test suite: Unit tests

Integration tests

Test fixtures and configurations

Advanced Usage

Custom Templates

You can customize the generated structure:

from cwx_aiml_app_template.generator import create_project_structure
from cwx_aiml_app_template.templates.structure import DIRECTORY_STRUCTURE

# Customize the structure
custom_structure = DIRECTORY_STRUCTURE.copy()
custom_structure["custom_dir"] = {
    "__init__.py": "",
    "custom_module.py": "# Your custom code here\n"
    }

# Create project with custom structure
create_project_structure("my_project", structure=custom_structure)

Docker Support

The generated project includes a Dockerfile:

Dockerfile: Container definition

# Build the Docker image
docker build -t my-aiml-project .

# Run the container
docker run -it my-aiml-project

Best Practices

Code Organization

1. Keep raw data immutable
2. Document data transformations
3. Use configuration files for parameters
4. Write tests for critical functionality
5. Keep notebooks organized and documented

Development Workflow

1. Start with exploratory notebooks
2. Move stable code to source files
3. Write tests for new functionality
4. Document API changes
5. Update requirements.txt as needed

Data Management

1. Use version control for code, not data
2. Document data sources and transformations
3. Keep sensitive data out of version control
4. Use data validation in pipelines
5. Maintain data processing scripts

Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Write or update tests
5. Submit a pull request

Troubleshooting

Common Issues

1. Import Errors

# Wrong
from src.models import MyModel
# Correct
from my_project_name.src.models import MyModel

2. Path Issues

# Use pathlib for path handling
from pathlib import Path
data_dir = Path("data/raw")

3. Environment Setup

# Create virtual environment
python -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows
pip install -r requirements.txt

FAQ

Q: Can I use this for non-ML projects?

A: Yes, though the structure is optimized for ML workflows.

Q: How do I update the template?

A: Run pip install --upgrade cwx-aiml-app-template

Q: Can I add custom directories?

A: Yes, see the Custom Templates section above.

License

This project is licensed under the MIT License - see the LICENSE file for details.