Native Python Module

Native Python installations are available through the Spack package manager with support for multiple versions, virtual environments, and modern package managers.

Python Quick Start 

Note

For the value of ${SPACK_ROOT}, Please refer to Spack Instances for the installation path.

# Modify this path accordingly
export SPACK_ROOT="/path/to/spack"

# Activate Spack environment
source "${SPACK_ROOT}/dist/bin/setup-env.sh" -y

# Check available Python versions
module avail python

# Load Python
module load python/3.12

# Verify installation
python --version
pip --version

Note

Module names may include a 7-digit hash suffix (e.g., python/3.12.9-abc1234). You do NOT need to include this hash when loading - the version alone (e.g., 3.12) is sufficient.

Creating Virtual Environments 

Python virtual environments allow you to create isolated environments for different projects.

Using venv (Standard Library)

# Create a virtual environment
python -m venv myenv

# Activate the environment
source myenv/bin/activate

# Install packages
pip install numpy pandas matplotlib

# Deactivate when done
deactivate

Using uv (Fast Alternative)

# Create virtual environment with uv
uv venv myenv

# Activate the environment
source myenv/bin/activate

# Install packages (much faster than pip)
uv pip install numpy pandas matplotlib

# Deactivate when done
deactivate

Using PDM (Modern Package Manager)

# Initialize a new project
pdm init

# Add dependencies
pdm add numpy pandas matplotlib

# Install dependencies
pdm install

# Run commands in the project environment
pdm run python script.py

Using Poetry (Dependency Management)

# Initialize a new project
poetry init

# Add dependencies
poetry add numpy pandas matplotlib

# Install dependencies
poetry install

# Run commands in the project environment
poetry run python script.py

# Activate poetry shell
poetry shell

Package Management 

Using pip 

# Install a package
pip install package_name

# Install specific version
pip install package_name==1.2.3

# Install from requirements file
pip install -r requirements.txt

# Upgrade a package
pip install --upgrade package_name

# List installed packages
pip list

# Generate requirements file
pip freeze > requirements.txt

Using uv (Faster Alternative)

# Install packages (10-100x faster than pip)
uv pip install numpy pandas matplotlib

# Install from requirements file
uv pip install -r requirements.txt

# Compile requirements (lock dependencies)
uv pip compile requirements.in -o requirements.txt

Building C Extensions with Cython 

All Python versions include Cython for building C extensions.

# Compile a Cython file
cython mymodule.pyx

# Build with setup.py
python setup.py build_ext --inplace

SLURM Integration 

Example batch script using Python virtual environment:

#!/bin/bash

## Your SBATCH settings here
#SBATCH ...

# Activate Spack environment
source /path/to/spack/dist/bin/setup-env.sh -y

# Load Python
module load python/3.12

# Activate virtual environment
source myenv/bin/activate

# Run your Python script
python my_script.py

Example using uv:

#!/bin/bash

## Your SBATCH settings here
#SBATCH ...

# Activate Spack environment
source /path/to/spack/dist/bin/setup-env.sh -y

# Load Python
module load python/3.12

# Use uv to run in isolated environment
uv run python my_script.py

Best Practices 

Virtual Environments

Always use virtual environments for projects
Keep one environment per project
Use requirements.txt or pyproject.toml for reproducibility
Activate the correct environment before installing packages

Package Management

Consider using uv for faster package installation
Pin package versions in production
Use pdm or poetry for dependency management
Regularly update packages for security

Performance

Use uv for significantly faster package operations
Consider pdm for better dependency resolution
Use Cython for performance-critical code

Support and Resources 

Python Documentation

Modern Package Managers

Development Tools