Pré-requisito: você precisa saber como desenvolver programas usando o Ray de código aberto.
O Ray no SDK da Vertex AI para Python usado aqui é uma versão do SDK da Vertex AI para Python que inclui a funcionalidade do Ray Client, conector do Ray no BigQuery, Ray gerenciamento de clusters e previsões na Vertex AI.
Se você usar o Ray na Vertex AI no console Google Cloud , um
notebook do Colab Enterprise
vai orientar você pelo processo de instalação do SDK da Vertex AI para Python
depois que você criar um cluster do Ray.
Se você usa o Ray na Vertex AI no Vertex AI Workbench ou em outro ambiente Python interativo, instale o SDK da Vertex AI para Python:
# The latest image in the Ray cluster includes Ray 2.47
# The latest supported Python version is Python 3.11.
$ pip install google-cloud-aiplatform[ray]
Depois de instalar o SDK, reinicie o kernel antes de importar pacotes.
Opcional: se você pretende fazer leituras pelo BigQuery, crie um novo conjunto de dados do BigQuery ou use um que já existe. Para fazer isso, consulte criar um conjunto de dados do BigQuery.
(Opcional) Para reduzir o risco de exfiltração de dados da
Vertex AI, ative o VPC Service Controls e especifique
uma rede VPC ao criar um cluster. Para mais
informações, consulte VPC Service Controls com
a Vertex AI.
Se você ativar o VPC Service Controls, não será possível acessar recursos
fora do perímetro, como arquivos em um bucket do Cloud Storage.
(Opcional) Para usar uma imagem de contêiner personalizada, hospede-a no
Artifact Registry. Uma imagem personalizada permite adicionar dependências do Python que não estão incluídas nas imagens de contêiner pré-criadas. Para criar imagens personalizadas, consulte Como empacotar o software na documentação do Docker.
Opcional: se você especificar uma rede VPC ao criar um cluster Ray na
Vertex AI, é altamente recomendável usar uma rede VPC de modo automático
no projeto. Redes VPC de modo personalizado e várias redes VPC no mesmo projeto não são compatíveis e podem causar falha na criação do cluster.
[[["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 2025-08-19 UTC."],[],[],null,["# Set up for Ray on Vertex AI\n\n| To see an example of getting started with Ray on Vertex AI cluster management,\n| run the \"Ray on Vertex AI cluster management\" notebook in one of the following\n| environments:\n|\n| [Open in Colab](https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ray_on_vertex_ai/ray_cluster_management.ipynb)\n|\n|\n| \\|\n|\n| [Open in Colab Enterprise](https://console.cloud.google.com/vertex-ai/colab/import/https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fray_on_vertex_ai%2Fray_cluster_management.ipynb)\n|\n|\n| \\|\n|\n| [Open\n| in Vertex AI Workbench](https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Fray_on_vertex_ai%2Fray_cluster_management.ipynb)\n|\n|\n| \\|\n|\n| [View on GitHub](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ray_on_vertex_ai/ray_cluster_management.ipynb)\n\nBefore you begin with Ray on Vertex AI, follow these steps to set up your\nGoogle project and :\n\n1. Set up billing for your project, [install the\n gcloud CLI](/sdk/docs/install), and enable the Vertex AI API. To do this,\n follow the steps at [Set up a project and a development\n environment](/vertex-ai/docs/start/cloud-environment).\n\n [Enable the Vertex AI API](https://console.cloud.google.com/apis/enableflow?apiid=aiplatform.googleapis.com)\n2. Prerequisite: You must know how to develop programs using [open source\n Ray](https://docs.ray.io/en/latest/ray-overview/index.html).\n\n3. The Ray on Vertex AI SDK for Python used here is a version of the Vertex AI SDK for Python\n that includes the functionality of the [Ray\n Client](https://docs.ray.io/en/latest/cluster/running-applications/job-submission/ray-client.html),\n Ray BigQuery connector, Ray\n cluster management on Vertex AI, and predictions on Vertex AI.\n\n - If you use Ray on Vertex AI in the Google Cloud console, a\n Colab Enterprise\n notebook guides you through the Vertex AI SDK for Python installation\n process after you [create a Ray cluster](/vertex-ai/docs/open-source/ray-on-vertex-ai/create-cluster).\n\n - If you use Ray on Vertex AI in the Vertex AI Workbench or other interactive Python environment, install the Vertex AI SDK for Python:\n\n ```\n # The latest image in the Ray cluster includes Ray 2.47\n # The latest supported Python version is Python 3.11.\n $ pip install google-cloud-aiplatform[ray]\n ```\n\n After you install the SDK, restart the kernel before you import packages.\n | **Note:** If you use a Vertex AI Workbench notebook as the client environment and use the [Deep Learning VM](/deep-learning-vm/docs/introduction) as the machine image, Ray and the Vertex AI SDK for Python are pre-installed in the Python, TensorFlow Enterprise\n4. Optional: If you plan to read from BigQuery, create a\n new BigQuery dataset or use an existing\n dataset. To do this, see [create a new BigQuery dataset](/bigquery/docs/datasets).\n\n | **Note:** If you run code on your Ray cluster on Vertex AI that interacts with Google services like BigQuery, the [Vertex AI Custom Code Service\n | Agent](/vertex-ai/docs/general/access-control#service-agents) authenticates.\n5. (Optional) To mitigate the risk of data exfiltration from\n Vertex AI, enable VPC Service Controls and specify\n a VPC network when you create a cluster. For more\n information, see [VPC Service Controls with\n Vertex AI](/vertex-ai/docs/general/vpc-service-controls).\n\n If you enable VPC Service Controls, you can't reach resources\n outside the perimeter, such as files in a Cloud Storage bucket.\n | **Note:** The best setup for Ray on Vertex AI is one auto mode VPC network per project. If you use a custom mode VPC network or use multiple VPC networks to create clusters in the same project, you might encounter issues.\n6. (Optional) To use a custom container image, host it on\n [Artifact Registry](/artifact-registry/docs/overview). A custom image lets you add Python dependencies that aren't included with the prebuilt container images. To build custom images, see Packing your software in the [Docker documentation](https://docs.docker.com/build/building/packaging/).\n\n7. (Optional) If you specify a VPC network when creating a Ray cluster on\n Vertex AI, it's highly recommended that you use an auto mode VPC network\n in your project. Custom mode VPC networks and multiple VPC networks in the\n same project aren't supported and may cause cluster creation to fail.\n\nSecure your clusters\n--------------------\n\nFollow [Ray best practices and guidelines](https://docs.ray.io/en/latest/ray-security/index.html#best-practices), including\nrunning trusted code on trusted networks, to secure your Ray workloads.\nDeployment of ray.io in your cloud instances falls under the model of\n[shared responsibility](/vertex-ai/docs/shared-responsibility).\n\nFor more information about Google Cloud best practices, see the\n[GCP-2024-020 security bulletin](/support/bulletins#gcp-2024-020).\n\nSupported locations\n-------------------\n\nThe [Feature availability](/vertex-ai/docs/general/locations#available-regions) table lists the available locations for Ray on Vertex AI for Custom\nmodel training.\n\nWhat's next\n-----------\n\n- [Create a Ray cluster on Vertex AI](/vertex-ai/docs/open-source/ray-on-vertex-ai/create-cluster)"]]
