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Lesson 12 of 24

GitLab CI/CD & Alternative Platforms

Platform Comparison & Migration

5 min read

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CI/CD Platform Comparison for ML

Choosing the right CI/CD platform for ML workloads depends on your team's needs, existing infrastructure, and specific ML requirements.

Feature GitHub Actions GitLab CI/CD Jenkins
Hosting Cloud (SaaS) Cloud + Self-hosted Self-hosted
Configuration YAML workflows YAML pipeline Groovy/YAML
GPU Support Self-hosted runners SaaS + Self-hosted Self-hosted
Model Registry External (MLflow) Built-in External
Container Registry GitHub Packages Built-in External
Free Tier 2000 min/month 400 min/month Unlimited (self-hosted)
ML Ecosystem CML, DVC integration Native ML features Plugin-based
Learning Curve Low Medium High

When to Choose Each Platform

Choose GitHub Actions when:

  • Your code is already on GitHub
  • You want minimal infrastructure management
  • You need extensive marketplace integrations
  • Your team is familiar with GitHub ecosystem

Choose GitLab CI/CD when:

  • You need an all-in-one DevOps platform
  • Built-in model registry is valuable
  • You require self-hosted with full features
  • Enterprise compliance is critical

Choose Jenkins when:

  • You have existing Jenkins infrastructure
  • You need maximum customization
  • Complex, legacy pipeline requirements exist
  • On-premises deployment is mandatory

Feature Comparison Matrix

# GitHub Actions ML Pipeline
name: ML Pipeline
on: [push]
jobs:
  train:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: pip install -r requirements.txt
      - run: python train.py
      - uses: actions/upload-artifact@v4
        with:
          name: model
          path: models/

# GitLab CI/CD ML Pipeline
stages:
  - train

train:
  stage: train
  image: python:3.11
  script:
    - pip install -r requirements.txt
    - python train.py
  artifacts:
    paths:
      - models/

// Jenkins ML Pipeline (Jenkinsfile)
pipeline {
    agent any
    stages {
        stage('Train') {
            steps {
                sh 'pip install -r requirements.txt'
                sh 'python train.py'
            }
        }
    }
    post {
        success {
            archiveArtifacts artifacts: 'models/**'
        }
    }
}

Migration Strategies

GitHub Actions to GitLab CI/CD

Key mapping for migration:

GitHub Actions GitLab CI/CD
on: push (implicit, runs on push)
jobs: stages: + job definitions
runs-on: ubuntu-latest image: ubuntu:latest
uses: actions/checkout@v4 (automatic checkout)
uses: actions/upload-artifact@v4 artifacts: paths:
needs: [job1] needs: [job1]
if: github.ref == 'refs/heads/main' rules: - if: $CI_COMMIT_BRANCH == "main"
secrets.MY_SECRET $MY_SECRET (CI/CD variables)

Migration example:

# Original GitHub Actions
name: Train Model
on:
  push:
    branches: [main]
    paths:
      - 'data/**'

jobs:
  train:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: pip install -r requirements.txt
      - run: python train.py
        env:
          API_KEY: ${{ secrets.API_KEY }}
      - uses: actions/upload-artifact@v4
        with:
          name: model
          path: models/

# Migrated to GitLab CI/CD
stages:
  - train

train:
  stage: train
  image: python:3.11
  rules:
    - if: $CI_COMMIT_BRANCH == "main"
      changes:
        - data/**
  script:
    - pip install -r requirements.txt
    - python train.py
  variables:
    API_KEY: $API_KEY  # Set in GitLab CI/CD Variables
  artifacts:
    paths:
      - models/

GitLab CI/CD to GitHub Actions

# Original GitLab CI/CD
stages:
  - validate
  - train
  - deploy

validate-data:
  stage: validate
  image: python:3.11
  script:
    - python validate.py
  artifacts:
    reports:
      dotenv: validation.env

train-model:
  stage: train
  image: python:3.11
  script:
    - python train.py
  needs:
    - validate-data
  artifacts:
    paths:
      - models/

deploy:
  stage: deploy
  script:
    - python deploy.py
  rules:
    - if: $CI_COMMIT_BRANCH == "main"
      when: manual
  environment:
    name: production

# Migrated to GitHub Actions
name: ML Pipeline
on:
  push:
    branches: [main]

jobs:
  validate-data:
    runs-on: ubuntu-latest
    outputs:
      status: ${{ steps.validate.outputs.status }}
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - id: validate
        run: python validate.py

  train-model:
    runs-on: ubuntu-latest
    needs: validate-data
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: python train.py
      - uses: actions/upload-artifact@v4
        with:
          name: model
          path: models/

  deploy:
    runs-on: ubuntu-latest
    needs: train-model
    if: github.ref == 'refs/heads/main'
    environment: production
    steps:
      - uses: actions/checkout@v4
      - run: python deploy.py

Decision Framework

Use this framework to choose your platform:

START
  │
  ├─ Code on GitHub? ──Yes──> Team size < 10? ──Yes──> GitHub Actions
  │         │                      │
  │         No                     No
  │         │                      │
  │         v                      v
  │    Self-hosted required? ──> GitLab Self-Managed
  │         │                    or GitHub Enterprise
  │         No
  │         │
  │         v
  │    Need integrated DevOps? ──Yes──> GitLab CI/CD
  │         │
  │         No
  │         │
  │         v
  │    Existing Jenkins? ──Yes──> Keep Jenkins + modernize
  │         │
  │         No
  │         │
  │         v
  └────> GitHub Actions (default choice)

Key Takeaways

Consideration Recommendation
Starting fresh GitHub Actions for simplicity
Enterprise needs GitLab CI/CD for integrated platform
Legacy systems Jenkins with pipeline modernization
Migration Map concepts carefully, test incrementally
ML-specific All platforms work; ecosystem matters more

المحتوى العربي

مقارنة منصات CI/CD لـ ML

اختيار منصة CI/CD المناسبة لأحمال عمل ML يعتمد على احتياجات فريقك والبنية التحتية الحالية ومتطلبات ML المحددة.

الميزة GitHub Actions GitLab CI/CD Jenkins
الاستضافة سحابية (SaaS) سحابية + ذاتية ذاتية الاستضافة
التكوين YAML workflows YAML pipeline Groovy/YAML
دعم GPU runners ذاتية SaaS + ذاتية ذاتية الاستضافة
سجل النماذج خارجي (MLflow) مدمج خارجي
سجل الحاويات GitHub Packages مدمج خارجي
الطبقة المجانية 2000 دقيقة/شهر 400 دقيقة/شهر غير محدود (ذاتي)
نظام ML البيئي تكامل CML، DVC ميزات ML أصلية قائم على الإضافات
منحنى التعلم منخفض متوسط عالي

متى تختار كل منصة

اختر GitHub Actions عندما:

  • الكود موجود على GitHub
  • تريد إدارة بنية تحتية أقل
  • تحتاج تكاملات marketplace واسعة
  • فريقك مألوف بنظام GitHub البيئي

اختر GitLab CI/CD عندما:

  • تحتاج منصة DevOps متكاملة
  • سجل النماذج المدمج قيّم
  • تحتاج استضافة ذاتية مع ميزات كاملة
  • الامتثال المؤسسي حرج

اختر Jenkins عندما:

  • لديك بنية Jenkins تحتية موجودة
  • تحتاج أقصى تخصيص
  • متطلبات pipeline معقدة وقديمة موجودة
  • النشر المحلي إلزامي

مصفوفة مقارنة الميزات

# GitHub Actions ML Pipeline
name: ML Pipeline
on: [push]
jobs:
  train:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: pip install -r requirements.txt
      - run: python train.py
      - uses: actions/upload-artifact@v4
        with:
          name: model
          path: models/

# GitLab CI/CD ML Pipeline
stages:
  - train

train:
  stage: train
  image: python:3.11
  script:
    - pip install -r requirements.txt
    - python train.py
  artifacts:
    paths:
      - models/

// Jenkins ML Pipeline (Jenkinsfile)
pipeline {
    agent any
    stages {
        stage('Train') {
            steps {
                sh 'pip install -r requirements.txt'
                sh 'python train.py'
            }
        }
    }
    post {
        success {
            archiveArtifacts artifacts: 'models/**'
        }
    }
}

استراتيجيات الهجرة

من GitHub Actions إلى GitLab CI/CD

تعيين المفاتيح للهجرة:

GitHub Actions GitLab CI/CD
on: push (ضمني، يعمل عند push)
jobs: stages: + تعريفات المهام
runs-on: ubuntu-latest image: ubuntu:latest
uses: actions/checkout@v4 (checkout تلقائي)
uses: actions/upload-artifact@v4 artifacts: paths:
needs: [job1] needs: [job1]
if: github.ref == 'refs/heads/main' rules: - if: $CI_COMMIT_BRANCH == "main"
secrets.MY_SECRET $MY_SECRET (متغيرات CI/CD)

مثال الهجرة:

# GitHub Actions الأصلي
name: Train Model
on:
  push:
    branches: [main]
    paths:
      - 'data/**'

jobs:
  train:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: pip install -r requirements.txt
      - run: python train.py
        env:
          API_KEY: ${{ secrets.API_KEY }}
      - uses: actions/upload-artifact@v4
        with:
          name: model
          path: models/

# المهاجر إلى GitLab CI/CD
stages:
  - train

train:
  stage: train
  image: python:3.11
  rules:
    - if: $CI_COMMIT_BRANCH == "main"
      changes:
        - data/**
  script:
    - pip install -r requirements.txt
    - python train.py
  variables:
    API_KEY: $API_KEY  # معيّن في GitLab CI/CD Variables
  artifacts:
    paths:
      - models/

من GitLab CI/CD إلى GitHub Actions

# GitLab CI/CD الأصلي
stages:
  - validate
  - train
  - deploy

validate-data:
  stage: validate
  image: python:3.11
  script:
    - python validate.py
  artifacts:
    reports:
      dotenv: validation.env

train-model:
  stage: train
  image: python:3.11
  script:
    - python train.py
  needs:
    - validate-data
  artifacts:
    paths:
      - models/

deploy:
  stage: deploy
  script:
    - python deploy.py
  rules:
    - if: $CI_COMMIT_BRANCH == "main"
      when: manual
  environment:
    name: production

# المهاجر إلى GitHub Actions
name: ML Pipeline
on:
  push:
    branches: [main]

jobs:
  validate-data:
    runs-on: ubuntu-latest
    outputs:
      status: ${{ steps.validate.outputs.status }}
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - id: validate
        run: python validate.py

  train-model:
    runs-on: ubuntu-latest
    needs: validate-data
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: python train.py
      - uses: actions/upload-artifact@v4
        with:
          name: model
          path: models/

  deploy:
    runs-on: ubuntu-latest
    needs: train-model
    if: github.ref == 'refs/heads/main'
    environment: production
    steps:
      - uses: actions/checkout@v4
      - run: python deploy.py

إطار القرار

استخدم هذا الإطار لاختيار منصتك:

ابدأ
  │
  ├─ الكود على GitHub؟ ──نعم──> حجم الفريق < 10؟ ──نعم──> GitHub Actions
  │         │                      │
  │         لا                     لا
  │         │                      │
  │         v                      v
  │    استضافة ذاتية مطلوبة؟ ──> GitLab Self-Managed
  │         │                    أو GitHub Enterprise
  │         لا
  │         │
  │         v
  │    تحتاج DevOps متكامل؟ ──نعم──> GitLab CI/CD
  │         │
  │         لا
  │         │
  │         v
  │    Jenkins موجود؟ ──نعم──> أبقِ Jenkins + حدّث
  │         │
  │         لا
  │         │
  │         v
  └────> GitHub Actions (الخيار الافتراضي)

النقاط الرئيسية

الاعتبار التوصية
بداية جديدة GitHub Actions للبساطة
احتياجات مؤسسية GitLab CI/CD لمنصة متكاملة
أنظمة قديمة Jenkins مع تحديث pipeline
الهجرة عيّن المفاهيم بعناية، اختبر تدريجياً
خاص بـ ML جميع المنصات تعمل؛ النظام البيئي أهم

Quiz

Module 3: GitLab CI/CD & Alternative Platforms

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