Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Setelah membangun dan men-deploy aplikasi, Anda dapat
mengelola instance aplikasi ini menggunakan konsol Google Cloud atau command line.
Melihat instance dan output aplikasi yang di-deploy
Anda dapat melihat instance dan output aplikasi menggunakan konsol Google Cloud atau
command line. Kemudian, Anda dapat menggunakan informasi ini untuk
membaca aliran output model
dan mendapatkan aset.
UI Web
Lihat instance dan output aplikasi di konsol Google Cloud .
Pilih nama aplikasi yang ingin Anda lihat. Tindakan ini akan mengarahkan Anda ke halaman detail aplikasi.
Halaman detail aplikasi menampilkan tabel dengan resource aplikasi.
Tabel ini mencantumkan semua instance aplikasi yang sedang berjalan. Setiap aliran
input ke aplikasi memiliki instance-nya sendiri. Setiap instance memiliki
resource input dan outputnya sendiri yang tercantum di bawahnya.
Untuk memeriksa resource aset stream atau gudang di tabel instance,
klik ID input atau output, atau pilih jalur.
Jika mengklik resource stream, Anda akan dialihkan ke halaman detail stream, tempat Anda dapat memeriksa informasi mendetail tentang stream tersebut.
Untuk mempelajari cara membaca aliran output model menggunakan command line, lihat Membuat dan mengelola Aliran.
Mengklik resource aset gudang akan mengarahkan Anda ke halaman detail aset Vision Warehouse.
Metode deployment dan penghapusan deployment berlaku untuk aplikasi dengan kurang dari 20 instance. Jika aplikasi Anda memiliki lebih dari 20 instance, Anda harus membuat dan menghapus instance secara bertahap dengan API. Alur kerja yang direkomendasikan adalah sebagai
berikut:
Buat aplikasi Anda.
Tambahkan 1-20 instance eksperimental.
Deploy aplikasi Anda.
Pastikan aplikasi Anda berfungsi seperti yang diharapkan.
Gunakan metode createApplicationInstances untuk menambahkan lebih banyak input secara bertahap ke aplikasi yang di-deploy.
Izinkan aplikasi berjalan.
Gunakan metode deleteApplicationInstances untuk
menghapus input secara bertahap dari
aplikasi yang di-deploy.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-09-04 UTC."],[],[],null,["# Manage application instances\n\nAfter you [build](/vision-ai/docs/build-app) and [deploy](/vision-ai/docs/deploy-app) apps, you can\nmanage these app instances using the Google Cloud console or command line.\n\nView deployed app instances and output\n--------------------------------------\n\nYou can view app instances and output using the Google Cloud console or\ncommand line. You can then use this information to\n[read model output streams](/vision-ai/docs/read-stream)\nand [get assets](/vision-ai/docs/manage-assets-api#get-asset). \n\n### Web UI\n\nView an app's instances and output in the Google Cloud console.\n\n1. Open the **Applications** tab of the Vertex AI Vision dashboard.\n\n [Go to the Applications tab](https://console.cloud.google.com/ai/vision-ai/applications)\n2. Select the name of the app you want to view. This takes you to the\n application details page.\n\n The application details page displays a table with application resources.\n This table lists all the running instances of the application. Each input\n stream to the application has its own instance. Each instance has\n its own input and output resources listed under it.\n\n3. To inspect the stream or warehouse asset resources in the instance table,\n click on the input or output ID, or select the path.\n\n - If you click on **stream** resource, you are redirected to the\n stream details page, where you can inspect the detailed information of\n that stream.\n\n To learn how to read a model's output stream using the command line,\n see [Create and manage Streams](/vision-ai/docs/read-stream).\n\n - Clicking on the **warehouse asset** resource takes you to the\n Vision Warehouse asset details page.\n\n To get a Vision Warehouse asset, see [Manage resources\n using the Vision Warehouse API](/vision-ai/docs/manage-assets-api#get-asset).\n\n### REST\n\nTo list app instances, send a GET request by using the\n[projects.locations.applications.instances.list](/vision-ai/docs/reference/rest/v1/projects.locations.applications.instances/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- \u003cvar translate=\"no\"\u003eAPPLICATION_ID\u003c/var\u003e: The ID of your target application.\n\n\nHTTP method and URL:\n\n```\nGET https://visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/applications/APPLICATION_ID/instances\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 $(gcloud auth print-access-token)\" \\\n \"https://visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/applications/APPLICATION_ID/instances\"\n```\n\n#### PowerShell\n\n| **Note:** The following command assumes that you have logged in to the `gcloud` CLI with your user account by running [`gcloud init`](/sdk/gcloud/reference/init) or [`gcloud auth login`](/sdk/gcloud/reference/auth/login) . You can check the currently active account by running [`gcloud auth list`](/sdk/gcloud/reference/auth/list).\n\n\nExecute the following command:\n\n```\n$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/APPLICATION_ID/instances\" | Select-Object -Expand Content\n```\n\nYou should receive a JSON response similar to the following:\n\n```\n{\n \"instances\": [\n {\n \"name\": \"projects/PROJECT_NUMBER/locations/LOCATION_ID/applications/APPLICATION_ID/instances/INSTANCE_ID\",\n \"createTime\": \"2022-03-01T20:05:45.863836157Z\",\n \"inputResources\": [\n {\n \"inputResource\": \"input-stream\",\n \"consumerNode\": \"builtin-input-stream\"\n }\n ],\n \"outputResources\": [\n {\n \"outputResource\": \"sample-resource-1\",\n \"producerNode\": \"builtin-occupancy-count\",\n \"isTemporary\": true\n },\n {\n \"outputResource\": \"sample-resource-2\",\n \"producerNode\": \"builtin-input-stream\"\n },\n {\n \"outputResource\": \"sample-resource-3\",\n \"producerNode\": \"builtin-input-stream\",\n \"isTemporary\": true\n },\n {\n \"outputResource\": \"sample-resource-4\",\n \"producerNode\": \"builtin-input-stream\",\n \"isTemporary\": true\n }\n ]\n }\n ]\n}\n```\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\nDelete an app instance\n----------------------\n\n### REST\n\nTo delete application instances, send a POST request by using the\n[projects.locations.applications.deleteApplicationInstances](/vision-ai/docs/reference/rest/v1/projects.locations.applications/deleteApplicationInstances)\nmethod.\n\n\nBefore using any of the request data,\nmake the following replacements:\n\n- \u003cvar translate=\"no\"\u003ePROJECT_NUMBER\u003c/var\u003e: Your Google Cloud [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- \u003cvar translate=\"no\"\u003eAPPLICATION_ID\u003c/var\u003e: The ID of your target application.\n\n\nHTTP method and URL:\n\n```\nPOST https://visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/applications/APPLICATION_ID:deleteApplicationInstances\n```\n\n\nRequest JSON body:\n\n```\n{\n \"instanceIds\": [\n \"INSTANCE_ID1\",\n \"INSTANCE_ID2\",\n [...]\n ]\n}\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\nSave the request body in a file named `request.json`,\nand execute the following command:\n\n```\ncurl -X POST \\\n -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n -H \"Content-Type: application/json; charset=utf-8\" \\\n -d @request.json \\\n \"https://visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/applications/APPLICATION_ID:deleteApplicationInstances\"\n```\n\n#### PowerShell\n\n| **Note:** The following command assumes that you have logged in to the `gcloud` CLI with your user account by running [`gcloud init`](/sdk/gcloud/reference/init) or [`gcloud auth login`](/sdk/gcloud/reference/auth/login) . You can check the currently active account by running [`gcloud auth list`](/sdk/gcloud/reference/auth/list).\n\n\nSave the request body in a file named `request.json`,\nand execute the following command:\n\n```\n$cred = gcloud auth print-access-token\n$headers = @{ \"Authorization\" = \"Bearer $cred\" }\n\nInvoke-WebRequest `\n -Method POST `\n -Headers $headers `\n -ContentType: \"application/json; charset=utf-8\" `\n -InFile request.json `\n -Uri \"https://visionai.googleapis.com/v1/projects/PROJECT_NUMBER/locations/LOCATION_ID/applications/APPLICATION_ID:deleteApplicationInstances\" | Select-Object -Expand Content\n```\n\nYou should receive a JSON response similar to the following:\n\n```\n{\n \"name\": \"projects/PROJECT_NUMBER/locations/LOCATION_ID/operations/OPERATION_ID\",\n \"metadata\": {\n \"@type\": \"type.googleapis.com/google.cloud.visionai.v1.OperationMetadata\",\n \"createTime\": \"[...]\",\n \"Target\": \"projects/PROJECT_NUMBER/locations/LOCATION_ID/applications/APPLICATION_ID\"\n \"Verb\": \"update\"\n \"apiVersion\": \"v1\"\n },\n \"done\": false\n}\n```\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\nManage large scale apps\n-----------------------\n\nThe deploy and undeploy methods are valid for applications with less than\n20 instances. If your app has more than 20 instances you must create and\nremove instances incrementally with the API. The recommended workflow is as\nfollows:\n\n1. Create your app.\n2. Add 1-20 experimental instances.\n3. Deploy your application.\n4. Verify your app works as expected.\n5. Use the [`createApplicationInstances`](/vision-ai/docs/reference/rest/v1/projects.locations.applications/createApplicationInstances) method to incrementally add more inputs to the deployed application.\n6. Allow app to run.\n7. Use the [`deleteApplicationInstances`](/vision-ai/docs/reference/rest/v1/projects.locations.applications/deleteApplicationInstances) method to incrementally remove inputs from deployed applications.\n8. Undeploy the application.\n\n| **Caution:** For a single application there can only be *one* update request running at a time. You are responsible for queueing these types of requests, and performing the necessary retries if there is a failure."]]
