Vertex AI TensorBoard custom training with prebuilt container: Notebook
In this tutorial, you learn how to create a custom training job using prebuilt
containers, and monitor your training process on Vertex AI TensorBoard
in near real time.
This tutorial uses the following Cloud de Confiance by S3NS ML services and resources:
- Gemini Enterprise Agent Platform training
- Vertex AI TensorBoard
The steps performed include:
- Set up a service account and Cloud Storage buckets.
- Write your customized training code.
- Package and upload your training code to Cloud Storage.
- Create and launch your custom training job with Vertex AI TensorBoard enabled for near
real time monitoring.
Relevant content
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2026-10-13 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2026-10-13 UTC."],[],[]]