Datos de imagen de Hello: configura tu proyecto y entorno
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Si planeas usar el SDK de Vertex AI para Python, asegúrate de que la cuenta de servicio que inicializa el cliente tenga el rol
Vertex AI Service Agent
(roles/aiplatform.serviceAgent) de IAM.
Configura tu proyecto de Google Cloud para usar Vertex AI. Luego, crea un
bucket de Cloud Storage y copia los archivos de imagen que usarás en el entrenamiento de un
modelo de clasificación de imágenes de AutoML.
En este instructivo, se incluyen las siguientes páginas:
Abre Cloud Shell.
Cloud Shell es un entorno de shell interactivo
para Google Cloud que te permite administrar proyectos y recursos desde
el navegador web.
En Cloud Shell, establece el proyecto actual como el ID del proyecto de Google Cloud
y guárdalo en la variable de
shell projectid:
gcloud config set project PROJECT_ID &&
projectid=PROJECT_ID &&
echo $projectid
Reemplaza PROJECT_ID por el ID del proyecto. Puedes
ubicar el ID del proyecto en la consola de Google Cloud. Para obtener más información, consulta
Encuentra el ID del proyecto.
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.
El rol de usuario de Vertex AI (roles/aiplatform.user) IAM
proporciona acceso para usar todos los recursos en Vertex AI. La función
Administrador de almacenamiento
(roles/storage.admin) almacena el conjunto de datos de entrenamiento del documento
en Cloud Storage.
¿Qué sigue?
Sigue la página siguiente de este instructivo a fin de usar la consola de Google Cloud para crear un conjunto de datos de clasificación de imágenes y, también, importar imágenes alojadas en un bucket público de Cloud Storage.
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Información o código de muestra incorrectos","incorrectInformationOrSampleCode","thumb-down"],["Faltan la información o los ejemplos que necesito","missingTheInformationSamplesINeed","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 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."]]
