Prerequisite: You must know how to develop programs using open source
Ray.
The Ray on Vertex AI SDK for Python used here is a version of the Vertex AI SDK for Python
that includes the functionality of the Ray
Client,
Ray BigQuery connector, Ray
cluster management on Vertex AI, and predictions on Vertex AI.
If you use Ray on Vertex AI in the Google Cloud console, a
Colab Enterprise
notebook guides you through the Vertex AI SDK for Python installation
process after you create a Ray cluster.
If you use Ray on Vertex AI in the Vertex AI Workbench or other interactive Python environment, install the Vertex AI SDK for 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]
After you install the SDK, restart the kernel before you import packages.
Optional: If you plan to read from BigQuery, create a
new BigQuery dataset or use an existing
dataset. To do this, see create a new BigQuery dataset.
(Optional) To mitigate the risk of data exfiltration from
Vertex AI, enable VPC Service Controls and specify
a VPC network when you create a cluster. For more
information, see VPC Service Controls with
Vertex AI.
If you enable VPC Service Controls, you can't reach resources
outside the perimeter, such as files in a Cloud Storage bucket.
(Optional) To use a custom container image, host it on
Artifact Registry. 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.
(Optional) If you specify a VPC network when creating a Ray cluster on
Vertex AI, it's highly recommended that you use an auto mode VPC network
in your project. Custom mode VPC networks and multiple VPC networks in the
same project aren't supported and may cause cluster creation to fail.
Secure your clusters
Follow Ray best practices and guidelines, including
running trusted code on trusted networks, to secure your Ray workloads.
Deployment of ray.io in your cloud instances falls under the model of
shared responsibility.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-25 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)"]]
