Prerequisito: devi sapere come sviluppare programmi utilizzando Ray
open source.
L'SDK Ray on Vertex AI per Python utilizzato qui è una versione dell'SDK Vertex AI per Python
che include la funzionalità di Ray
Client, il connettore Ray BigQuery, la gestione del cluster Ray su Vertex AI e le previsioni su Vertex AI.
Se utilizzi Ray su Vertex AI nella Google Cloud console, un
notebook Colab Enterprise
ti guida nella procedura di installazione dell'SDK Vertex AI per Python
dopo aver creato un cluster Ray.
Se utilizzi Ray su Vertex AI in Vertex AI Workbench o in un altro ambiente Python interattivo, installa l'SDK Vertex AI per 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]
Dopo aver installato l'SDK, riavvia il kernel prima di importare i pacchetti.
(Facoltativo) Se prevedi di leggere da BigQuery, crea un nuovo set di dati BigQuery o utilizza un set di dati esistente. Per farlo, consulta Crea un nuovo set di dati BigQuery.
(Facoltativo) Per ridurre il rischio di esfiltrazione di dati da
Vertex AI, attiva i Controlli di servizio VPC e specifica
una rete VPC quando crei un cluster. Per maggiori
informazioni, consulta Controlli di servizio VPC con
Vertex AI.
Se abiliti Controlli di servizio VPC, non puoi raggiungere le risorse
al di fuori del perimetro, ad esempio i file in un bucket Cloud Storage.
(Facoltativo) Per utilizzare un'immagine container personalizzata, ospitala su
Artifact Registry. Un'immagine personalizzata ti consente di aggiungere dipendenze Python non incluse nelle immagini container predefinite. Per creare immagini personalizzate, consulta la sezione Creazione del pacchetto del software nella documentazione di Docker.
(Facoltativo) Se specifichi una rete VPC durante la creazione di un cluster Ray su
Vertex AI, è consigliabile utilizzare una rete VPC in modalità automatica
nel tuo progetto. Le reti VPC in modalità personalizzata e più reti VPC nello stesso progetto non sono supportate e potrebbero causare errori durante la creazione del cluster.
Proteggi i tuoi cluster
Segui le best practice e le linee guida di Ray, tra cui l'esecuzione di codice attendibile su reti attendibili, per proteggere i tuoi carichi di lavoro Ray.
Il deployment di ray.io nelle tue istanze cloud rientra nel modello di
responsabilità condivisa.
[[["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 2025-09-02 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)"]]
