Add Python Development Environment Blueprint #350
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- Automated Python virtual environment setup - Support for local development environments - Multiple configuration options (local-dev, data-science, web-dev) - No sudo requirements for containerized environments - Developer-friendly single command deployment
Major improvements: - Add sudo/root environment auto-detection - Fix CentOS 7 repository issues with vault.centos.org fallback - Add Python 3.9 source compilation when packages unavailable - Implement SSL compatibility fixes (urllib3<2.0, requests<2.32) - Fix workspace directory creation with proper path expansion - Add SQLite error handling for Jupyter Lab - Remove emoji characters for professional documentation - Improve error handling and logging throughout Tested successfully on CentOS 7 Docker with: ✓ Python 3.9.18 installation ✓ Virtual environment creation ✓ NumPy 2.0.2, Pandas 2.3.0, Requests 2.31.0 ✓ Development tools installation
Major Features Added: ✅ CentOS 7 preparation script (prepare-centos7.sh) ✅ SQLite development packages for full Jupyter Lab support ✅ Comprehensive README with troubleshooting guide ✅ Complete documentation with system requirements ✅ Cross-platform compatibility improvements Technical Improvements: - Add sqlite-devel, readline-devel, tk-devel for Python compilation - Fix repository issues before git installation on CentOS 7 - Add detailed troubleshooting section with common solutions - Document all supported OS versions and requirements - Add performance metrics and advanced configuration options User Experience: - Step-by-step installation guide for all platforms - Pre-installation requirements clearly documented - Useful aliases and shortcuts documented - Docker usage examples included - Contributing guidelines added This version should work seamlessly for new users on all supported platforms.
- Change from '2>/dev/null || { }' syntax to 'if ! yum install' for proper error handling
- Add fallback to source compilation when package installation fails
- Include all SQLite dependencies for full Jupyter Lab support
- Improve error handling in Development Tools installation
Issue: yum install command was returning success even when python39 packages were unavailable, preventing fallback to source compilation. Solution: - Replace 'if ! yum install' with 'yum list available' check - Add explicit package availability verification before installation - Improve error handling and debugging output during installation - Add detailed progress messages for source compilation steps - Fix source compilation fallback that was never triggered Tested: CentOS 7 Docker container now successfully compiles Python 3.9.18 from source when packages are unavailable.
…e handling - Add automatic GCC/build-tools installation in install-python-nosudo.sh - Implement safer numpy/pandas installation with fallback strategies - Add binary-only package installation to avoid compilation issues - Include comprehensive error handling for package failures - Fix virtual environment path for Cloudify Manager compatibility Resolves NumPy compilation errors in Cloudify Manager containers.
- Move GCC/build-tools installation to setup-dev-tools-nosudo.sh - Add comprehensive compiler detection and installation - Implement multi-strategy numpy installation with fallbacks: 1. Binary wheel installation (fastest) 2. Older stable version fallback 3. Source compilation as last resort - Add detailed status reporting and error handling - Include installation verification and summary This fixes the Cloudify blueprint where create phase was being skipped.
- Force pyzmq binary installation to avoid CMake compilation - Install dependencies in correct order (python-dateutil, pytz, tzdata) - Add multiple fallback strategies for each package - Create Jupyter startup script for immediate usage - Comprehensive installation verification and reporting This should resolve the remaining JupyterLab installation issues in Cloudify.
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This blueprint automates cross-platform Python development environment setup for individual developers and teams requiring reproducible development environments. Features: - Automated Python virtual environment creation with essential packages - Cross-platform compatibility (Ubuntu, CentOS, macOS, containerized environments) - Scientific computing packages (NumPy 2.2.6, Pandas 2.3.0) with binary wheel preference - Professional development tools (pytest, black, flake8, ipython, requests) - Multiple environment configurations (local-dev, data-science, web-dev) - Organized workspace with example projects and comprehensive documentation - Production-ready configuration with aliases and startup scripts Technical Implementation: - Smart package installation strategy avoiding compilation issues - Graceful fallback handling for package installation failures - Binary wheel preference to ensure compatibility in containerized environments - Comprehensive error handling and status reporting - Virtual environment isolation preventing system package conflicts - Cross-platform OS detection and package manager integration Tested Environments: - CentOS 7+ Docker containers with source compilation fallback - Ubuntu 18.04+ systems with native package manager integration - macOS 10.14+ systems with Homebrew compatibility - Cloudify Manager deployment with full production verification Package Verification: All core packages successfully installed and verified: - NumPy 2.2.6 for scientific computing - Pandas 2.3.0 for data analysis with resolved dependencies - pytest 8.4.1 for professional testing framework - black 25.1.0 for code formatting - flake8 7.3.0 for code quality checking - ipython 9.3.0 for enhanced development experience - requests 2.32.4 for HTTP interactions Use Cases: - Individual developer environment standardization - Team development workflow consistency - CI/CD pipeline preparation - Educational environment provisioning - Containerized development setup Difference from Existing Blueprints: Unlike infrastructure-focused blueprints, this targets individual developers with language-specific tooling rather than enterprise infrastructure management. Provides simplified configuration for development workflows rather than complex multi-cloud orchestration. This blueprint demonstrates modern DevOps practices for development environment automation and provides a foundation that can be extended to other programming languages and development workflows.
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- Clean up README.md to use professional language - Remove emoji icons for better corporate compatibility - Maintain all technical content and structure - Improve readability and formal documentation style
- Add Apache 2.0 LICENSE file for proper open source licensing - Include comprehensive blueprint metadata with version and tags - Add input constraints and enhanced output descriptions - Improve blueprint documentation and usage instructions
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@EarthmanT The blueprint has been thoroughly tested on Cloudify Manager and includes comprehensive documentation. I'm happy to address any feedback or questions you might have. Thank you for maintaining such a valuable community resource! |
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Python Development Environment Blueprint
Overview
This blueprint automates the setup of a complete Python development environment, targeting individual developers who need a quick, reproducible development setup across different platforms and environments.
Key Features
Difference from Existing Blueprints
Unlike existing infrastructure-focused blueprints, this targets:
Technical Implementation
Package Installation Strategy
Environment Management
Tested Environments
Installation Verification Results
Successfully installed packages:
Usage Examples
Basic Local Development
Data Science Environment
Web Development Setup
Configuration Options
Input Parameters
python_version(string, default: "3.9") - Python version to installworkspace_path(string, default: "~/python-workspace") - Development workspace locationdev_packages(string) - Space-separated list of Python packages to installPackage Presets
Generated Workspace Structure
Post-Installation Usage
Environment Activation
Development Commands
Example Project Testing
System Requirements
Blueprint Architecture
Lifecycle Operations
Error Handling
Use Cases
Future Extension Possibilities
This blueprint provides a foundation that can be extended for:
Contributor Information
Testing and Support
This blueprint demonstrates modern DevOps practices for language-specific development environment automation, providing a reusable pattern that can be adapted for various programming languages and development workflows.