Home Backend Development Python Tutorial GCP publish python package in production

GCP publish python package in production

Nov 20, 2024 pm 12:29 PM

GCP publish python package in production

GCP: Publish Python Package in Production

This guide explains how to use Google Artifact Registry to manage shared Python code as a package. This approach eliminates code duplication between your Cloud Functions and server.


Step 1: Structure Your Shared Code

Create a new Python package for your shared logic (e.g., common_logic).

common_logic/
├── setup.py
├── common_logic/
│   ├── __init__.py
Copy after login
Copy after login

Step 2: Create setup.py

Define your package configuration in a setup.py file:

common_logic/
├── setup.py
├── common_logic/
│   ├── __init__.py
Copy after login
Copy after login

Step 3: Set Up Google Artifact Registry

  1. Enable the Artifact Registry API:
from setuptools import setup, find_packages

setup(
    name="common_logic",
    version="0.1.0",
    packages=find_packages(),
    install_requires=[
        "pandas>=1.3.0",
    ],
    author="Your Name",
    author_email="your.email@example.com",
    description="Common logic for app",
)
Copy after login
  1. Create a Python repository:
   gcloud services enable artifactregistry.googleapis.com
Copy after login

Step 4: Configure Authentication

  1. Create a service account:
   gcloud artifacts repositories create python-packages \
       --repository-format=python \
       --location=us-central1 \
       --description="Python packages repository"
Copy after login
  1. Grant necessary permissions:
   gcloud iam service-accounts create artifact-publisher \
       --description="Service account for publishing to Artifact Registry"
Copy after login
  1. Create and download a key:
   gcloud artifacts repositories add-iam-policy-binding python-packages \
       --location=us-central1 \
       --member="serviceAccount:artifact-publisher@${PROJECT_ID}.iam.gserviceaccount.com" \
       --role="roles/artifactregistry.writer"
Copy after login

Step 5: Build and Upload Package

  1. Install build tools:
   gcloud iam service-accounts keys create key.json \
       --iam-account=artifact-publisher@${PROJECT_ID}.iam.gserviceaccount.com
Copy after login
  1. Build the package:
   pip install build twine
Copy after login
  1. Configure twine for Artifact Registry:
   python -m build
Copy after login
  1. Upload the package:
   cat > ~/.pypirc << EOL
   [distutils]
   index-servers = common-logic-repo
   [common-logic-repo]
   repository: https://us-central1-python.pkg.dev/${PROJECT_ID}/python-packages/
   username: _json_key_base64
   password: $(base64 -w0 key.json)
   EOL
Copy after login

Step 6: Use the Package

In Cloud Functions

  1. Create a requirements.txt file:
   twine upload --repository common-logic-repo dist/*
Copy after login
  1. Use the package in your Cloud Function:
   --index-url https://pypi.org/simple
   --extra-index-url https://oauth2accesstoken:${ARTIFACT_REGISTRY_TOKEN}@us-central1-python.pkg.dev/${PROJECT_ID}/python-packages/simple/
   common-logic==0.1.0
Copy after login
Copy after login

In Server Code

  1. Add to your server's requirements.txt:
   from common_logic import ...

   def cloud_function(request):
       # Your cloud function code using the imported functions
       pass
Copy after login
  1. Use it in your server code:
   --index-url https://pypi.org/simple
   --extra-index-url https://oauth2accesstoken:${ARTIFACT_REGISTRY_TOKEN}@us-central1-python.pkg.dev/${PROJECT_ID}/python-packages/simple/
   common-logic==0.1.0
Copy after login
Copy after login

Step 7: CI/CD Integration

  1. Add the service account key as a secret in your GitHub repository.
  2. Update your Cloud Build configuration:
   from common_logic import ...
   # Your server code using the imported functions
Copy after login

Step 8: Version Management

  1. Update the version in setup.py.
  2. Build and upload the new version.
  3. Update requirements.txt in both Cloud Functions and server code.
  4. Deploy both components.

Best Practices

  • Use semantic versioning for your package.
  • Pin specific versions in requirements.txt.
  • Test new versions thoroughly before deploying.
  • Keep a changelog of version changes.
  • Use environment variables for PROJECT_ID and LOCATION.
  • Include comprehensive documentation in your package.

Common Issues and Solutions

Authentication Errors

  • Verify service account permissions.
  • Ensure key.json is properly encoded.
  • Check .pypirc configuration.

Package Not Found

  • Verify repository URL format.
  • Check if the package was successfully uploaded.
  • Ensure requirements.txt uses the correct URL format.

Version Conflicts

  • Pin specific versions of dependencies.
  • Use virtual environments for testing.
  • Document dependency requirements clearly.

The above is the detailed content of GCP publish python package in production. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Hot Topics

Java Tutorial
1664
14
PHP Tutorial
1268
29
C# Tutorial
1243
24
Python vs. C  : Applications and Use Cases Compared Python vs. C : Applications and Use Cases Compared Apr 12, 2025 am 12:01 AM

Python is suitable for data science, web development and automation tasks, while C is suitable for system programming, game development and embedded systems. Python is known for its simplicity and powerful ecosystem, while C is known for its high performance and underlying control capabilities.

Python: Games, GUIs, and More Python: Games, GUIs, and More Apr 13, 2025 am 12:14 AM

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

The 2-Hour Python Plan: A Realistic Approach The 2-Hour Python Plan: A Realistic Approach Apr 11, 2025 am 12:04 AM

You can learn basic programming concepts and skills of Python within 2 hours. 1. Learn variables and data types, 2. Master control flow (conditional statements and loops), 3. Understand the definition and use of functions, 4. Quickly get started with Python programming through simple examples and code snippets.

Python vs. C  : Learning Curves and Ease of Use Python vs. C : Learning Curves and Ease of Use Apr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

How Much Python Can You Learn in 2 Hours? How Much Python Can You Learn in 2 Hours? Apr 09, 2025 pm 04:33 PM

You can learn the basics of Python within two hours. 1. Learn variables and data types, 2. Master control structures such as if statements and loops, 3. Understand the definition and use of functions. These will help you start writing simple Python programs.

Python and Time: Making the Most of Your Study Time Python and Time: Making the Most of Your Study Time Apr 14, 2025 am 12:02 AM

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python: Exploring Its Primary Applications Python: Exploring Its Primary Applications Apr 10, 2025 am 09:41 AM

Python is widely used in the fields of web development, data science, machine learning, automation and scripting. 1) In web development, Django and Flask frameworks simplify the development process. 2) In the fields of data science and machine learning, NumPy, Pandas, Scikit-learn and TensorFlow libraries provide strong support. 3) In terms of automation and scripting, Python is suitable for tasks such as automated testing and system management.

Python: Automation, Scripting, and Task Management Python: Automation, Scripting, and Task Management Apr 16, 2025 am 12:14 AM

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

See all articles