Combine Conda and Poetry - Migration from Conda and Pip

To integrate Conda and Poetry in your Python development workflow, you’ll leverage Conda for environment management and handling non-Python dependencies, while Poetry will manage Python-specific dependencies and packaging. This approach combines the strengths of both tools, ensuring a robust and reproducible development environment.

Below is a comprehensive guide on how to transition your existing environment.yml and environment_dev.yml files to use Conda and Poetry together.

1. Overview of the Integration

  • Conda:

    • Role: Manage environments, Python versions, and non-Python dependencies.

    • Advantages:

      • Handles complex dependencies, including system-level libraries.

      • Facilitates reproducible environments across different machines.

  • Poetry:

    • Role: Manage Python dependencies, handle packaging, and versioning.

    • Advantages:

      • Simplifies dependency resolution with pyproject.toml and poetry.lock.

      • Provides tools for building and publishing Python packages.

2. Setting Up Conda Environment with Poetry

2.1. Create a Base environment.yml

Your Conda environment.yml will handle the Python environment, install Poetry, and include any non-Python dependencies. Here’s how you can structure it:

name: iotemplateapp
channels:
  - conda-forge
dependencies:
  - python=3.12
  - pip
  - pyyaml
  - tomli
  - tomli-w
  - virtualenv
  - poetry
  # Add any additional non-Python dependencies here

Notes:

  • Poetry Installation: Installing Poetry via Conda ensures it’s available in your environment. Alternatively, you can install it via pip if preferred.

  • Non-Python Dependencies: If you have system-level dependencies (e.g., gcc, ffmpeg), list them under dependencies before the pip section.

2.2. Create the Conda Environment

Run the following command to create the environment:

conda env create -f environment.yml

Activate the environment:

conda activate iotemplateapp

3. Configuring Poetry

3.1. Initialize Poetry in Your Project

Navigate to your project directory and initialize a new Poetry project:

cd path/to/your/project
poetry init

Follow the prompts to set up your pyproject.toml. This file will manage your Python dependencies.

3.2. Configure Poetry to Use Conda’s Python Interpreter

Ensure Poetry uses the Python interpreter from your active Conda environment:

poetry env use $(which python)

This links Poetry’s environment to the Conda-managed Python interpreter, ensuring consistency.

3.3. Define Dependencies in pyproject.toml

Your pyproject.toml should specify both production and development dependencies. Here’s an example based on your provided environment_dev.yml:

[tool.poetry]
name = "iotemplateapp"
version = "0.1.0"
description = "Your project description"
authors = ["Your Name <you@example.com>"]

[tool.poetry.dependencies]
python = "^3.12"
pyyaml = "^6.0"
tomli = "^2.0"
tomli-w = "^0.7"
# Private Git Repository Dependency
io-common = { git = "https://github.com/io-aero/io-common.git", branch = "main", extras = [], develop = false }

[tool.poetry.dev-dependencies]
bandit = "^1.7"
black = "^23.3"
coverage = "^7.2"
coveralls = "^3.3"
docformatter = "^1.7"
furo = "^2023.4.28"
mypy = "^1.4"
myst-parser = "^0.19"
ordered-set = "^4.1"
pylint = "^2.17"
pytest = "^7.4"
pytest-cov = "^4.0"
pytest-deadfixtures = "^1.5"
pytest-helpers-namespace = "^1.1"
pytest-random-order = "^1.0"
rinohtype = "^0.9"
ruff = "^0.0.291"
sphinx = "^7.2"
sphinx-autoapi = "^1.11.1"
types-pyyaml = "^6.0.12"
types-toml = "^0.10.13"
vulture = "^2.5"

[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"

Handling Private Git Dependencies:

To include a private Git repository (e.g., io-common), you need to handle authentication securely. Here are two approaches:

  1. Using Environment Variables in pyproject.toml:

    Unfortunately, Poetry doesn’t natively support environment variable interpolation in pyproject.toml. Instead, you can use Poetry’s dependency groups or manage authentication via Git credentials.

  2. Using Git Credential Helpers:

    Configure Git to use credential helpers that can securely store and provide your credentials when accessing private repositories.

    git config --global credential.helper store

    Then, clone the repository manually once to store the credentials:

    git clone https://github.com/io-aero/io-common.git

    After this, Poetry should be able to access the repository without embedding credentials in the URL.

Alternatively, Use Poetry’s source Configuration:

You can define custom sources with authentication in Poetry’s configuration.

poetry config http-basic.io-aero IoAeroMachineUser $PYPI_PAT

Then, reference the package without embedding credentials:

io-common = { git = "https://github.com/io-aero/io-common.git", branch = "main" }

This approach keeps your credentials out of pyproject.toml.

3.4. Install Dependencies with Poetry

After configuring pyproject.toml, install the dependencies:

poetry install

This command will create a poetry.lock file, ensuring reproducible installs across environments.

4. Handling Development vs. Production Environments

4.1. Production Environment

For production, you typically exclude development dependencies to keep the environment lean.

Steps:

  1. Create a Separate Conda Environment for Production:

    # environment_prod.yml
name: iotemplateapp
channels:
  - conda-forge
dependencies:
  - python=3.12
  - pyyaml
  - tomli
  - tomli-w
  - virtualenv
  - poetry
  # Add any additional non-Python dependencies here

  2. Initialize and Install with Poetry Without Dev Dependencies:

    conda env create -f environment_prod.yml
conda activate iotemplateapp
poetry install --no-dev

4.2. Development Environment

For development, include both production and development dependencies.

Steps:

  1. Use the Existing environment.yml:

    # environment_dev.yml
name: iotemplateapp
channels:
  - conda-forge
dependencies:
  - python=3.12
  - pip
  - pyyaml
  - tomli
  - tomli-w
  - virtualenv
  - poetry
  # Add any additional non-Python dependencies here

  2. Initialize and Install with Poetry Including Dev Dependencies:

    conda env create -f environment_dev.yml
conda activate iotemplateapp
poetry install

Note: Ensure that your pyproject.toml differentiates between production and development dependencies using [tool.poetry.dependencies] and [tool.poetry.dev-dependencies].

5. Example Workflow

5.1. Production Setup

  1. Create environment_prod.yml:

    name: iotemplateapp
channels:
  - conda-forge
dependencies:
  - python=3.12
  - pyyaml
  - tomli
  - tomli-w
  - virtualenv
  - poetry
  # Add any additional non-Python dependencies here

  2. Create and Activate the Environment:

    conda env create -f environment_prod.yml
conda activate iotemplateapp

  3. Configure Poetry and Install Dependencies:

    poetry env use $(which python)
poetry install --no-dev

5.2. Development Setup

  1. Create environment_dev.yml:

    name: iotemplateapp
channels:
  - conda-forge
dependencies:
  - python=3.12
  - pip
  - pyyaml
  - tomli
  - tomli-w
  - virtualenv
  - poetry
  # Add any additional non-Python dependencies here

  2. Create and Activate the Environment:

    conda env create -f environment_dev.yml
conda activate iotemplateapp

  3. Configure Poetry and Install Dependencies:

    poetry env use $(which python)
poetry install

6. Best Practices and Recommendations

6.1. Avoid Mixing Package Managers for Python Packages

  • Do Not Install Python Packages with Both Conda and Poetry/Pip: This can lead to dependency conflicts and unpredictable behavior.

  • Use Conda for Non-Python Dependencies Only: Let Poetry handle all Python-specific packages.

6.2. Securely Manage Private Repositories

  • Use Git Credential Helpers: To avoid embedding sensitive information in configuration files.

  • Leverage Poetry’s Authentication Mechanisms: Configure credentials using Poetry’s settings to keep them secure.

6.3. Automate Environment Setup

Create scripts or Makefiles to streamline the setup process, reducing manual errors and ensuring consistency across team members.

Example setup.sh:

#!/bin/bash

# Create Conda environment
conda env create -f environment_dev.yml

# Activate environment
conda activate iotemplateapp

# Configure Poetry to use Conda's Python
poetry env use $(which python)

# Install dependencies
poetry install

Make the script executable:

chmod +x setup.sh

Run the setup:

./setup.sh

6.4. Regularly Update Dependencies

  • Poetry: Use poetry update to keep Python dependencies up-to-date.

  • Conda: Update Conda packages as needed, ensuring compatibility with Poetry-managed dependencies.

6.5. Document the Workflow

Maintain clear documentation for your team on how to set up and work within the Conda and Poetry integrated environment. Include instructions for environment creation, activating environments, and managing dependencies.

7. Final Thoughts

Combining Conda and Poetry offers a powerful and flexible environment for Python development, especially in complex projects requiring both Python and non-Python dependencies. By following the structured approach outlined above, you can achieve:

  • Reproducible Environments: Ensuring consistency across development, testing, and production.

  • Efficient Dependency Management: Leveraging Poetry’s robust handling of Python packages.

  • Scalability and Flexibility: Easily managing both Python and system-level dependencies without conflicts.

Remember to regularly maintain and update your dependencies, secure your private repositories, and streamline your workflow to maximize the benefits of this integrated setup.