Introduzione ai dati di immagine: configura il progetto e l'ambiente
Mantieni tutto organizzato con le raccolte
Salva e classifica i contenuti in base alle tue preferenze.
Se prevedi di utilizzare l'SDK Vertex AI per Python, assicurati che il account di servizio
che inizializza il client disponga del ruolo IAM
agente di servizio Vertex AI
(roles/aiplatform.serviceAgent).
Configurerai il tuo progetto Google Cloud per utilizzare Vertex AI. Poi crea un bucket Cloud Storage e copia i file immagine da utilizzare per l'addestramento di un modello di classificazione delle immagini AutoML.
In Cloud Shell, imposta il progetto corrente sul tuo ID progetto Google Cloude archivialo nella variabile di shell projectid:
gcloud config set project PROJECT_ID &&
projectid=PROJECT_ID &&
echo $projectid
Sostituisci PROJECT_ID con l'ID progetto. Puoi
trovare l'ID progetto nella Google Cloud console. Per ulteriori informazioni, vedi
Trovare l'ID progetto.
Enable the IAM, Compute Engine, Notebooks, Cloud Storage, and Vertex AI APIs.
In the Principal column, find all rows that identify you or a group that
you're included in. To learn which groups you're included in, contact your
administrator.
For all rows that specify or include you, check the Role column to see whether
the list of roles includes the required roles.
Nel campo Nuove entità, inserisci il tuo identificatore utente.
In genere si tratta dell'indirizzo email di un Account Google.
Nell'elenco Seleziona un ruolo, seleziona un ruolo.
Per concedere altri ruoli, fai clic su addAggiungi
un altro ruolo e aggiungi ogni ruolo aggiuntivo.
Fai clic su Salva.
Il ruolo IAM Utente Vertex AI (roles/aiplatform.user)
fornisce l'accesso per utilizzare tutte le risorse in Vertex AI. Il ruolo
Amministratore storage
(roles/storage.admin) in cui memorizzi il set di dati di addestramento
del documento in Cloud Storage.
Passaggi successivi
Segui la pagina successiva di questo tutorial per utilizzare la
consoleGoogle Cloud per creare un set di dati di classificazione delle immagini e
importare le immagini ospitate in un bucket Cloud Storage pubblico.
[[["Facile da capire","easyToUnderstand","thumb-up"],["Il problema è stato risolto","solvedMyProblem","thumb-up"],["Altra","otherUp","thumb-up"]],[["Difficile da capire","hardToUnderstand","thumb-down"],["Informazioni o codice di esempio errati","incorrectInformationOrSampleCode","thumb-down"],["Mancano le informazioni o gli esempi di cui ho bisogno","missingTheInformationSamplesINeed","thumb-down"],["Problema di traduzione","translationIssue","thumb-down"],["Altra","otherDown","thumb-down"]],["Ultimo aggiornamento 2025-09-04 UTC."],[],[],null,["# Hello image data: Set up your project and environment\n\nIf you plan to use the Vertex AI SDK for Python, make sure that the service account\ninitializing the client has the\n[Vertex AI Service Agent](/vertex-ai/docs/general/access-control#aiplatform.serviceAgent)\n(`roles/aiplatform.serviceAgent`) IAM role.\n\nYou'll set up your Google Cloud project to use Vertex AI. Then create a\nCloud Storage bucket and copy image files to use for training an AutoML\nimage classification model.\n\nThis tutorial has several pages:\n\n1. Set up your project and environment.\n\n2. [Create an image classification dataset, and\n import images.](/vertex-ai/docs/tutorials/image-classification-automl/dataset)\n\n3. [Train an AutoML image classification\n model.](/vertex-ai/docs/tutorials/image-classification-automl/training)\n\n4. [Evaluate and analyze model performance.](/vertex-ai/docs/tutorials/image-classification-automl/error-analysis)\n\n5. [Deploy a model to an endpoint, and send a\n prediction.](/vertex-ai/docs/tutorials/image-classification-automl/deploy-predict)\n\n6. [Clean up your project.](/vertex-ai/docs/tutorials/image-classification-automl/cleanup)\n\nEach page assumes that you have already performed the instructions from the\nprevious pages of the tutorial.\n\nBefore you begin\n----------------\n\nComplete the following steps before using Vertex AI functionality.\n\n1. In the Google Cloud console, go to the project selector page.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n2. Select or create a Google Cloud project.\n\n | **Note**: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.\n3.\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n4. Open [Cloud Shell](/shell/docs/launching-cloud-shell-editor). Cloud Shell is an interactive shell environment for Google Cloud that lets you manage your projects and resources from your web browser.\n[Go to Cloud Shell](https://ssh.cloud.google.com/cloudshell/editor)\n5. In the Cloud Shell, set the current project to your Google Cloud project ID and store it in the `projectid` shell variable: \n\n ```\n gcloud config set project PROJECT_ID &&\n projectid=PROJECT_ID &&\n echo $projectid\n ```\n Replace \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e with your project ID. You can locate your project ID in the Google Cloud console. For more information, see [Find your project ID](/vertex-ai/docs/tutorials/tabular-bq-prediction/prerequisites#find-project-id).\n6.\n\n\n Enable the IAM, Compute Engine, Notebooks, Cloud Storage, and Vertex AI APIs.\n\n\n [Enable the APIs](https://console.cloud.google.com/flows/enableapi?apiid=iam.googleapis.com, compute.googleapis.com,notebooks.googleapis.com storage.googleapis.com aiplatform.googleapis.com)\n7.\n\n Make sure that you have the following role or roles on the project:\n\n roles/aiplatform.user, roles/storage.admin\n\n #### Check for the roles\n\n 1.\n In the Google Cloud console, go to the **IAM** page.\n\n [Go to IAM](https://console.cloud.google.com/projectselector/iam-admin/iam?supportedpurview=project)\n 2. Select the project.\n 3.\n In the **Principal** column, find all rows that identify you or a group that\n you're included in. To learn which groups you're included in, contact your\n administrator.\n\n 4. For all rows that specify or include you, check the **Role** column to see whether the list of roles includes the required roles.\n\n #### Grant the roles\n\n 1.\n In the Google Cloud console, go to the **IAM** page.\n\n [Go to IAM](https://console.cloud.google.com/projectselector/iam-admin/iam?supportedpurview=project)\n 2. Select the project.\n 3. Click person_add **Grant access**.\n 4.\n In the **New principals** field, enter your user identifier.\n\n This is typically the email address for a Google Account.\n\n 5. In the **Select a role** list, select a role.\n 6. To grant additional roles, click add **Add\n another role** and add each additional role.\n 7. Click **Save**.\nThe Vertex AI User (`roles/aiplatform.user`) IAM role provides access to use all resources in Vertex AI. The [Storage Admin](/storage/docs/access-control/iam-roles) (`roles/storage.admin`) role you store the document's training dataset in Cloud Storage.\n\nWhat's next\n-----------\n\nFollow the [next page of this tutorial](/vertex-ai/docs/tutorials/image-classification-automl/dataset) to use the\nGoogle Cloud console to create an image classification dataset and\nimport images hosted in a public Cloud Storage bucket."]]
