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Depois de criar e implantar apps, você pode
gerenciar esses apps usando o console ou a linha de comando do Google Cloud .
Gerenciar um aplicativo
Atualizar um app
Apps não implantados
Faça as modificações necessárias no app (como adicionar ou remover nós de componentes). A Vertex AI Vision vai armazenar as mudanças automaticamente.
Apps implantados
As mudanças nos apps implantados são armazenadas automaticamente no servidor da Vertex AI Vision, e o aplicativo implantado não é afetado. Para descartar as modificações, clique no botão Descartar mudanças. Para salvar as mudanças, selecione Atualizar o aplicativo. Para aplicar essas mudanças locais ao aplicativo,
cancele a implantação do aplicativo atual e faça a implantação de novo.
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 2025-09-04 UTC."],[],[],null,["# Manage applications\n\nAfter you [build](/vision-ai/docs/build-app) and [deploy](/vision-ai/docs/deploy-app) apps, you can\nmanage these apps using the Google Cloud console or command line.\n\nManage an application\n---------------------\n\nUpdate an app\n-------------\n\n#### Undeployed apps\n\nMake any modifications to the app (such as adding or removing component nodes)\n; Vertex AI Vision will automatically store the changes.\n\n#### Deployed apps\n\nChanges to deployed apps are automatically stored to the Vertex AI Vision server\nand the deployed application is not affected. To discard modifications, click\nthe **Discard changes** button. To save changes, select\n**Update the application**. To apply these local changes to the application,\nundeploy the current application, then redeploy it.\n\nList apps\n---------\n\n### REST\n\nTo list all apps in a project, send a GET request by using the\n[projects.locations.applications.list](/vision-ai/docs/reference/rest/v1/projects.locations.applications/list)\nmethod.\n\n\nBefore using any of the request data,\nmake the following replacements:\n\n- \u003cvar translate=\"no\"\u003ePROJECT\u003c/var\u003e: Your Google Cloud [project ID or\n project number](/resource-manager/docs/creating-managing-projects#identifying_projects).\n- \u003cvar translate=\"no\"\u003eLOCATION_ID\u003c/var\u003e: The [region](/about/locations) where you are using Vertex AI Vision. For example: `us-central1`, `europe-west4`. See [available regions](/vision-ai/docs/warehouse-supported-regions).\n\n\nHTTP method and URL:\n\n```\nGET https://visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/applications\n```\n\nTo send your request, choose one of these options: \n\n#### curl\n\n| **Note:** The following command assumes that you have logged in to the `gcloud` CLI with your user account by running [`gcloud init`](/sdk/gcloud/reference/init) or [`gcloud auth login`](/sdk/gcloud/reference/auth/login) , or by using [Cloud Shell](/shell/docs), which automatically logs you into the `gcloud` CLI . You can check the currently active account by running [`gcloud auth list`](/sdk/gcloud/reference/auth/list).\n\n\nExecute the following command:\n\n```\ncurl -X GET \\\n -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n \"https://visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/applications\"\n```\n\n#### PowerShell\n\n| **Note:** The following command assumes that you have logged in to the `gcloud` CLI with your user account by running [`gcloud init`](/sdk/gcloud/reference/init) or [`gcloud auth login`](/sdk/gcloud/reference/auth/login) . You can check the currently active account by running [`gcloud auth list`](/sdk/gcloud/reference/auth/list).\n\n\nExecute the following command:\n\n```\n$cred = gcloud auth print-access-token\n$headers = @{ \"Authorization\" = \"Bearer $cred\" }\n\nInvoke-WebRequest `\n -Method GET `\n -Headers $headers `\n -Uri \"https://visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/applications\" | Select-Object -Expand Content\n```\n\nYou should receive a JSON response similar to the following:\n\n```\n{\n \"applications\": [\n {\n \"name\": \"projects/PROJECT_NUMBER/locations/LOCATION_ID/applications/test-application-1\",\n \"createTime\": \"2022-03-01T20:04:12.558371402Z\",\n \"updateTime\": \"2022-03-01T20:07:21.589713094Z\",\n \"displayName\": \"Test Application 1\",\n \"runtimeInfo\": {\n \"deployTime\": \"2022-03-01T20:07:21.460654Z\"\n }\n \"state\": \"DEPLOYED\"\n },\n {\n \"name\": \"projects/PROJECT_NUMBER/locations/LOCATION_ID/applications/test-application-2\",\n \"createTime\": \"2022-03-01T20:04:12.558371402Z\",\n \"updateTime\": \"2022-03-01T20:07:21.589713094Z\",\n \"displayName\": \"Test Application 2\",\n \"runtimeInfo\": {\n \"deployTime\": \"2022-03-01T20:07:21.460654Z\"\n }\n \"state\": \"DEPLOYED\"\n },\n ]\n}\n```\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\nWhat's next\n-----------\n\n- Learn how to read app input data from an ingestion stream or analyzed model output data in [Read stream data](/vision-ai/docs/read-stream)."]]