Conda and Poetry for Machine Learning Projects

Combining Conda and Poetry can be an effective strategy for machine learning projects that require both robust environment management and sophisticated Python package handling. This approach leverages the strengths of each tool while mitigating their individual limitations.

Understanding the Tools

Conda: Environment and Package Manager

• Manages environments containing any software (not just Python)
• Excels at installing complex dependencies like CUDA toolkits and scientific computing packages
• Provides pre-compiled binaries, reducing compilation issues
• Supports multiple channels (conda-forge, pytorch, etc.)

Poetry: Python Package Manager

• Modern dependency management with deterministic resolution
• Superior handling of Python-only dependencies
• Lock files for reproducible environments
• Simplified publishing and packaging workflow

Why Combine Both?

The hybrid approach makes sense when:

1. You need non-Python dependencies (CUDA, system libraries)
2. You want Poetry's superior dependency resolution for Python packages
3. You require environment reproducibility across different systems
4. GPU-accelerated packages are essential for your workflow

Implementation Guide

Environment Configuration

Create a environment.yml file for Conda:

name: ml-project
channels:
  - pytorch
  - conda-forge
  - defaults
dependencies:
  - python=3.9
  - cudatoolkit=11.8
  - cudnn
  - mamba
  - pip

Create a pyproject.toml file for Poetry:

[tool.poetry]
name = "ml-project"
version = "0.1.0"
description = "Machine learning project with Conda and Poetry"
authors = ["Your Name <your.email@example.com>"]

[tool.poetry.dependencies]
python = "^3.9"
torch = "^2.0.0"
transformers = "^4.30.0"
pandas = "^1.5.0"

[tool.poetry.dev-dependencies]
pytest = "^7.0.0"
black = "^22.0.0"

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

Setup Workflow

1. Create Conda environment:

conda env create -f environment.yml
conda activate ml-project

2. Initialize Poetry within Conda environment:

poetry install

3. Install additional packages:

# Use poetry for Python packages
poetry add scikit-learn

# Use conda for system dependencies
conda install -c conda-forge opencv

WARNING

Avoid using poetry shell or poetry run when working within a Conda environment, as this can create conflicts between the two environment managers.

Best Practices

1. Clear Separation of Responsibilities

• Use Conda for: Python interpreter, system dependencies, CUDA toolkits
• Use Poetry for: Python package management, dependency resolution

2. Version Pinning

Pin critical versions in both configuration files to ensure reproducibility:

# environment.yml
dependencies:
  - python=3.9.12
  - cudatoolkit=11.3.1
  - cudnn=8.2.1

# pyproject.toml
[tool.poetry.dependencies]
torch = "1.13.1"
torchvision = "0.14.1"

3. Lock Files for Reproducibility

Generate and commit lock files from both tools:

# Conda (using conda-lock)
conda-lock -f environment.yml -p linux-64

# Poetry
poetry lock

4. CI/CD Integration

Example GitHub Actions workflow:

name: Build and Test

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v3
    - name: Set up Conda
      uses: conda-incubator/setup-miniconda@v2
      with:
        environment-file: environment.yml
        activate-environment: ml-project
    - name: Install Python dependencies
      run: poetry install
    - name: Run tests
      run: poetry run pytest

Common Challenges and Solutions

Package Conflicts

When both Conda and Poetry try to manage the same package:

# Explicitly tell Poetry to ignore Conda-managed packages
poetry add --lock tensorflow=2.8.0

CUDA Version Compatibility

Ensure CUDA versions match between Conda-installed tools and PyPI packages:

# environment.yml
dependencies:
  - cudatoolkit=11.3
  - cudnn=8.2

# pyproject.toml
[tool.poetry.dependencies]
torch = {version = "^1.12.0", source = "pytorch"}

Performance Optimization

Use Mamba for faster dependency resolution:

# Replace conda with mamba for faster environment solving
mamba env create -f environment.yml

Alternatives to Consider

Pixi (Emerging Solution)

For newer projects, consider Pixi, which combines Conda and Poetry functionality:

# Install pixi
curl -fsSL https://pixi.sh/install.sh | bash

# Create environment
pixi init my-project
pixi add python pytorch cudatoolkit
pixi add --pypy transformers pandas

Conclusion

The Conda + Poetry combination provides a robust solution for machine learning projects that require both system-level dependencies and sophisticated Python package management. While it introduces some complexity, the benefits of reproducibility, GPU support, and clean dependency management make it worthwhile for production ML workflows.

TIP

For most machine learning projects, start with Conda for environment setup and critical system dependencies, then use Poetry for Python package management to leverage its superior dependency resolution and lockfile capabilities.

Remember to:

1. Maintain clear separation between Conda and Poetry responsibilities
2. Pin versions for critical dependencies
3. Use lock files from both tools for reproducibility
4. Document the setup process for team members
5. Consider newer tools like Pixi for greenfield projects