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Work with Virtual Environments

Angreal provides virtual environment management using UV, a fast Python package installer and resolver.

The virtual environment integration provides:

  • Create and manage virtual environments
  • Install packages and requirements
  • Ensure specific Python versions
  • Automatic environment activation for tasks
  • Programmatic activation within Python processes
from angreal.integrations.venv import VirtualEnv
# Create a new environment
venv = VirtualEnv("myproject-env")
# Install packages
venv.install(["django", "requests"])
# Install from requirements file
venv.install("requirements.txt")
from angreal.integrations.venv import VirtualEnv
# Create and activate a virtual environment
venv = VirtualEnv("data-env", now=True)
venv.install(["numpy", "pandas"])
# Activate the environment
venv.activate()
# Now you can import the installed packages
import numpy as np
import pandas as pd
# Remember to deactivate when done
venv.deactivate()
from angreal.integrations.venv import VirtualEnv
# Automatic activation/deactivation with context manager
with VirtualEnv("analysis-env", now=True) as venv:
venv.install("scikit-learn")
# Environment is activated here
from sklearn import datasets
iris = datasets.load_iris()
# Environment is automatically deactivated here
from angreal.integrations.venv import venv_required
import angreal
@angreal.command(name="analyze", about="Run data analysis")
@venv_required(".venv", requirements=["pandas", "numpy"])
def analyze_data():
"""This function runs in an isolated environment with packages available."""
import pandas as pd
import numpy as np
# Your code here - the virtual environment is activated
from angreal.integrations.venv import VirtualEnv
# Create environment with specific Python version
venv = VirtualEnv("py312-env", python="3.12")
# List available Python versions
pythons = VirtualEnv.discover_available_pythons()
for version, path in pythons:
print(f"{version}: {path}")