Données d'images Hello : configurer votre projet et votre environnement
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Si vous prévoyez d'utiliser le SDK Vertex AI pour Python, assurez-vous que le compte de service initialisant le client dispose du rôle Agent de service Vertex AI (roles/aiplatform.serviceAgent).
Vous allez configurer votre projet Google Cloud pour utiliser Vertex AI. Créez ensuite un bucket Cloud Storage et copiez les fichiers image à utiliser pour entraîner un modèle de classification d'images AutoML.
Ouvrez Cloud Shell.
Cloud Shell est un environnement shell interactif pour Google Cloud qui vous permet de gérer vos projets et vos ressources depuis un navigateur Web.
Dans Cloud Shell, définissez le projet actuel sur votre ID de projet Google Cloud et stockez-le dans la variable d'interface système projectid :
gcloud config set project PROJECT_ID &&
projectid=PROJECT_ID &&
echo $projectid
Remplacez PROJECT_ID par l'ID de votre projet. Vous trouverez l'ID de votre projet dans la console Google Cloud. Pour en savoir plus, consultez la section Trouver votre ID de projet.
Enable the IAM, Compute Engine, Notebooks, Cloud Storage, and Vertex AI APIs:
Replace USER_IDENTIFIER with the identifier for your user
account.
For example, user:myemail@example.com.
Replace ROLE with each individual role.
Le rôle IAM d'utilisateur Vertex AI (roles/aiplatform.user) permet d'utiliser toutes les ressources dans Vertex AI. Le rôle Administrateur de l'espace de stockage (roles/storage.admin) vous permet de stocker l'ensemble de données d'entraînement du document dans Cloud Storage.
Étapes suivantes
Suivez la page suivante de ce tutoriel pour utiliser la console Google Cloud afin de créer un ensemble de données de classification d'images et d'importer des images hébergées dans un bucket Cloud Storage public.
Sauf indication contraire, le contenu de cette page est régi par une licence Creative Commons Attribution 4.0, et les échantillons de code sont régis par une licence Apache 2.0. Pour en savoir plus, consultez les Règles du site Google Developers. Java est une marque déposée d'Oracle et/ou de ses sociétés affiliées.
Dernière mise à jour le 2025/05/06 (UTC).
[[["Facile à comprendre","easyToUnderstand","thumb-up"],["J'ai pu résoudre mon problème","solvedMyProblem","thumb-up"],["Autre","otherUp","thumb-up"]],[["Difficile à comprendre","hardToUnderstand","thumb-down"],["Informations ou exemple de code incorrects","incorrectInformationOrSampleCode","thumb-down"],["Il n'y a pas l'information/les exemples dont j'ai besoin","missingTheInformationSamplesINeed","thumb-down"],["Problème de traduction","translationIssue","thumb-down"],["Autre","otherDown","thumb-down"]],["Dernière mise à jour le 2025/05/06 (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."]]
