python_env_setup Block

The python_env_setup block is used to create and configure Python virtual environments that can be used by python_custom_shell blocks and other Python-based operations.

Overview

The python_env_setup block manages Python virtual environments, ensuring proper isolation and dependency management for custom Python scripts. It creates isolated environments where Python packages can be installed without affecting the system Python installation.

Important: This block is used only once to create the virtual environment - it doesn't need to run every time during flow execution. Virtual environment names should be unique within the Flow AI project.

Configuration Options

Operation Types

Choose the type of environment setup:

• Setup VENV: Create a new virtual environment
• Install Packages: Install additional packages to an existing environment
• Update Environment: Update packages in an existing environment
• Clean Environment: Remove and recreate an environment

Virtual Environment Configuration

When using "Setup VENV" operation:

• Name: Descriptive name for the environment setup operation
• Choose VENV name: The name of the virtual environment to create
• Choose PIP Requirements file to upload: Upload a requirements.txt file
• Download File requirements.txt: Download the default requirements file

Environment Management

• VENV Name: Unique identifier for the virtual environment (must be unique within the Flow AI project)
• Python Version: Specify the Python version to use
• Dependencies: Manage package dependencies through requirements.txt
• Isolation: Ensure complete isolation from system Python
• One-Time Setup: Environment is created once and can be reused across all flows in the project

How It Works

The python_env_setup block:

1. Creates Environment: Sets up a new virtual environment with the specified name
2. Installs Dependencies: Installs packages from the requirements.txt file
3. Configures Paths: Sets up proper Python and pip paths
4. Validates Setup: Ensures the environment is properly configured
5. Reports Status: Provides feedback on the setup process

Environment Creation Flow

Trigger → Create VENV → Install Dependencies → Validate Setup → Report Status

Important Notes:

• This block is used only once to create the virtual environment
• It doesn't need to run every time during flow execution
• Virtual environment names should be unique within the Flow AI project
• Created environments can be used across all flows in the same project

Use Cases

Project Setup

Set up environments for different projects:

trigger → python_env_setup (project A) → python_custom_shell (project A code)

Dependency Management

Manage package dependencies:

requirements.txt → python_env_setup → validated environment → ready for use

Environment Isolation

Create isolated environments for different tasks:

data processing env → python_env_setup → ml processing env → python_env_setup

Multi-Version Support

Support different Python versions:

Python 3.8 env → python_env_setup → Python 3.9 env → python_env_setup

Common Patterns

Basic Environment Setup

// Configuration
Operation: Setup VENV
Name: "Excel Processing Environment"
VENV Name: "read_excel_v2"
Requirements File: requirements.txt

// requirements.txt content
pandas>=1.3.0
openpyxl>=3.0.0
numpy>=1.21.0

Machine Learning Environment

// Configuration
Operation: Setup VENV
Name: "ML Processing Environment"
VENV Name: "ml_processing"
Requirements File: ml_requirements.txt

// ml_requirements.txt content
scikit-learn>=1.0.0
tensorflow>=2.8.0
pandas>=1.3.0
numpy>=1.21.0
matplotlib>=3.5.0

Data Analysis Environment

// Configuration
Operation: Setup VENV
Name: "Data Analysis Environment"
VENV Name: "data_analysis"
Requirements File: analysis_requirements.txt

// analysis_requirements.txt content
pandas>=1.3.0
numpy>=1.21.0
matplotlib>=3.5.0
seaborn>=0.11.0
jupyter>=1.0.0

Advanced Features

Environment Validation

The block validates the created environment:

• Python Availability: Ensures Python is accessible
• Package Installation: Verifies all packages are installed correctly
• Path Configuration: Checks that paths are properly set
• Dependency Resolution: Resolves package conflicts

Error Handling

Comprehensive error handling:

• Creation Errors: Handles virtual environment creation failures
• Installation Errors: Manages package installation issues
• Permission Errors: Handles file system permission problems
• Dependency Conflicts: Resolves package version conflicts

Environment Reuse

Efficient environment management:

• One-Time Creation: Environment is created once and reused across flows
• Project-Wide Access: All flows in the same project can use the created environment
• Unique Naming: Virtual environment names must be unique within the Flow AI project
• Persistent Storage: Environments persist and don't need to be recreated for each flow execution

Configuration Examples

Standard Data Processing Environment

// Configuration
Operation: Setup VENV
Name: "Standard Data Processing"
VENV Name: "data_processing"
Requirements File: standard_requirements.txt

// Standard requirements
pandas>=1.3.0
numpy>=1.21.0
requests>=2.25.0
python-dateutil>=2.8.0

Custom Package Environment

// Configuration
Operation: Setup VENV
Name: "Custom Packages Environment"
VENV Name: "custom_packages"
Requirements File: custom_requirements.txt

// Custom requirements
pandas>=1.3.0
openpyxl>=3.0.0
pillow>=8.3.0
beautifulsoup4>=4.10.0

Tips

• One-Time Setup: Remember this block is used only once to create the environment
• Unique Names: Ensure virtual environment names are unique within the Flow AI project
• Use Descriptive Names: Choose clear, descriptive names for environments
• Version Pinning: Pin package versions in requirements.txt for reproducibility
• Minimal Dependencies: Only include necessary packages to reduce setup time
• Project-Wide Usage: Created environments can be shared across all flows in the same project

Common Issues

Environment Creation Failed

Error: Failed to create virtual environment

Solution: Check available disk space and permissions. Ensure Python is properly installed.

Package Installation Failed

Error: Failed to install package 'pandas'

Solution: Check package name spelling and version compatibility. Ensure internet connectivity.

Environment Not Found

Error: Virtual environment 'read_excel_v2' not found

Solution: Ensure the environment was created successfully using this python_env_setup block before using it in python_custom_shell. Remember that virtual environment names must be unique within the Flow AI project.

Permission Denied

Error: Permission denied when creating environment

Solution: Check file system permissions and ensure the application has write access to the storage directory.

Environment Lifecycle

Creation Phase

1. Validate Requirements: Check requirements.txt format and package availability
2. Create Directory: Set up environment directory structure
3. Install Python: Set up Python interpreter in the environment
4. Install Packages: Install all required packages
5. Validate Setup: Test the environment configuration

Usage Phase

1. Environment Activation: Activate the environment for script execution
2. Package Access: Make installed packages available to scripts
3. Path Management: Ensure proper Python and package paths
4. Execution Support: Support script execution in the environment

Maintenance Phase

1. Package Updates: Update packages as needed
2. Environment Cleanup: Remove unused environments
3. Dependency Resolution: Resolve package conflicts
4. Performance Optimization: Optimize environment performance

Related Blocks

• python_custom_shell - For executing Python scripts in the created environment
• function - For simple JavaScript-based operations
• debug - For monitoring environment setup status