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python dependency management

· 4 min read
Dipjyoti Metia
Dipjyoti Metia
Chapter Lead - Testing

Python package management can be tricky, especially when working with machine learning and AI projects that often have complex dependencies. In this guide, we'll explore how to use pipx and poetry together to create a robust development environment for your generative AI projects.

What are pipx and poetry?​

pipx is a tool that lets you install and run Python applications in isolated environments. Think of it as npm install -g for Python, but with better isolation. Poetry, on the other hand, is a dependency management and packaging tool that makes it easy to manage project dependencies and build packages.

Setting Up Your Environment​

1. Installing pipx​

First, let's install pipx. It's recommended to use pip to install pipx globally:

python -m pip install --user pipx
python -m pipx ensurepath

2. Installing poetry using pipx​

Now that we have pipx, we can use it to install poetry in an isolated environment:

pipx install poetry

Creating a New GenAI Project​

1. Project Initialization​

Let's create a new project:

poetry new genai-project
cd genai-project

This creates a basic project structure:

genai-project/
├── pyproject.toml
├── README.md
├── genai_project/
│   └── __init__.py
└── tests/
    └── __init__.py

2. Configuring poetry​

Let's modify the pyproject.toml file for our GenAI project:

pyproject.toml
[tool.poetry]
name = "genai-project"
version = "0.1.0"
description = "A generative AI project using modern Python tools"
authors = ["Your Name <your.email@example.com>"]

[tool.poetry.dependencies]
python = "^3.9"
torch = "^2.0.0"
transformers = "^4.30.0"
datasets = "^2.12.0"
accelerate = "^0.20.0"

[tool.poetry.group.dev.dependencies]
pytest = "^7.3.1"
black = "^23.3.0"
isort = "^5.12.0"
flake8 = "^6.0.0"

[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

3. Installing Dependencies​

Install the project dependencies:

poetry install

Working with Virtual Environments​

1. Activating the Environment​

Poetry automatically creates and manages virtual environments. To activate it:

poetry shell

2. Running Scripts​

You can run Python scripts in your project using:

poetry run python your_script.py

Best Practices for GenAI Projects​

1. Managing GPU Dependencies​

For GPU support, you might need to install PyTorch with CUDA. Modify your pyproject.toml:

pyproject.toml
[tool.poetry.dependencies]
torch = { version = "^2.0.0", source = "pytorch" }

[[tool.poetry.source]]
name = "pytorch"
url = "https://download.pytorch.org/whl/cu117"
priority = "explicit"

2. Dependency Groups​

Organize dependencies into groups for better management:

pyproject.toml
[tool.poetry.group.training]
optional = true
dependencies = {accelerate = "^0.20.0", wandb = "^0.15.0"}

[tool.poetry.group.inference]
optional = true
dependencies = {onnxruntime-gpu = "^1.15.0"}

Install specific groups:

poetry install --with training

3. Version Control​

Add these entries to your .gitignore:

.venv/
dist/
__pycache__/
*.pyc
.pytest_cache/

Common Workflows​

1. Adding New Dependencies​

poetry add transformers datasets

2. Updating Dependencies​

poetry update

3. Exporting Requirements​

For environments that don't use poetry:

poetry export -f requirements.txt --output requirements.txt

Troubleshooting​

1. GPU Dependencies​

If you encounter GPU-related issues:

  • Ensure CUDA is properly installed
  • Match PyTorch version with your CUDA version
  • Use nvidia-smi to verify GPU availability

2. Memory Issues​

For large models:

  • Use poetry config virtualenvs.in-project true to create the virtual environment in your project directory
  • Consider using poetry run python -m pytest instead of pytest directly

Conclusion​

Using pipx and poetry together provides a robust foundation for GenAI projects. The isolation provided by pipx ensures that poetry itself doesn't interfere with other Python tools, while poetry's dependency management makes it easy to handle complex AI library requirements.

Remember to:

  • Always use poetry for dependency management
  • Keep your pyproject.toml updated
  • Commit both pyproject.toml and poetry.lock to version control
  • Use dependency groups to organize optional dependencies

This setup will help you maintain a clean, reproducible environment for your GenAI projects, making it easier to collaborate and deploy your models.