GitLab CI
从您的第一个 .gitlab-ci.yml 开始学习 GitLab CI/CD, 逐步掌握包含产物、环境和部署的多阶段流水线。
共 11 节,按顺序
- 120 分钟
什么是 CI/CD
Continuous integrationContinuous deliveryContinuous deploymentPipelinesStagesJobs+2 - 225 分钟
你的第一个流水线 (pipeline)
.gitlab-ci.yml locationMinimal pipelineSingle jobScript keywordPipeline UIJob logs - 330 分钟
任务 (jobs) 与阶段 (stages)
Default stagesStage keywordParallel jobs in same stageStages keywordNeeds for DAGJob ordering - 430 分钟
变量和密钥
Variables blockPredefined CI variablesCI_COMMIT_SHACI_PROJECT_DIRMasked variablesProtected variables+1 - 530 分钟
工件 (artifacts) 和缓存 (caches)
Artifacts pathsArtifacts expire_inDownloading artifactsCache keyCache pathsArtifacts vs caches - 630 分钟
规则 (rules) 和条件任务 (jobs)
Rules keywordIf conditionsCI_COMMIT_BRANCHCI_PIPELINE_SOURCEWhen manualChanges paths+1 - 735 分钟
Docker 任务 (jobs) 和服务 (services)
Image keywordServices keywordPostgres serviceRedis serviceDocker-in-dockerService networking - 830 分钟
环境与部署
Environment keywordEnvironment nameEnvironment urlDeployment trackingManual production deployDynamic environments+1 - 930 分钟
包含 (includes) 和模板 (templates)
Include localInclude projectInclude remoteInclude templateExtends keywordYAML anchors+1 - 1030 分钟
运行器 (runners)
Shared runnersGroup runnersProject runnersDocker executorShell executorKubernetes executor+2 - 1160 分钟
综合项目. 构建、测试、部署
Multi-stage pipelineBuild artifactPostgres service testDocker image build and pushManual production deployRules on main
用你的语言学习 GitLab CI
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Roles that use GitLab CI
See how hiring teams interview engineers on GitLab CI, in production-like environments.
CI/CD Engineer
Hire CI/CD engineers who keep your release train on time. Interview them in real GitHub Actions, GitLab CI and Jenkins workspaces with builds that fail the way they do in production.
See the interviewRelease/Build Engineer
Hire release and build engineers who make shipping boring. Real repos, real artifact stores, real signing flows. We record every step they take.
See the interviewDevOps Engineer
Hire DevOps engineers who actually ship. Run live or take-home interviews in production-like Linux environments with Docker, Kubernetes, Terraform and CI/CD. See how they debug, automate, and work alongside AI.
See the interview