How to Intermediate · 3 min read

How to use Vertex AI for fine-tuning

Quick answer

Use Google Cloud's Vertex AI Python client library to create a fine-tuning job for Gemini models by specifying your training dataset and model parameters. The process involves uploading your dataset to Google Cloud Storage, configuring a CustomJob or FineTuneJob with the Gemini base model, and monitoring the training job via the Vertex AI API.

PREREQUISITES

Python 3.8+
Google Cloud account with Vertex AI enabled
Google Cloud SDK installed and configured
Service account with Vertex AI permissions
pip install google-cloud-aiplatform

Setup

Install the google-cloud-aiplatform Python package and set up authentication with a service account key that has Vertex AI permissions. Ensure your Google Cloud project and Vertex AI API are enabled.

bash

pip install google-cloud-aiplatform

Step by step

This example demonstrates how to fine-tune a Gemini model on Vertex AI using Python. It assumes you have your training data uploaded to a Google Cloud Storage bucket.

python

from google.cloud import aiplatform
import os

# Set environment variables
PROJECT_ID = os.environ['GOOGLE_CLOUD_PROJECT']
LOCATION = 'us-central1'
BUCKET_NAME = 'your-gcs-bucket'
TRAINING_DATA_URI = f'gs://{BUCKET_NAME}/training-data.jsonl'

# Initialize Vertex AI client
client_options = {'api_endpoint': f'{LOCATION}-aiplatform.googleapis.com'}
aiplatform.init(project=PROJECT_ID, location=LOCATION)

# Define fine-tuning job parameters
fine_tune_job = aiplatform.CustomJob(
    display_name='gemini-fine-tune-job',
    worker_pool_specs=[{
        'machine_spec': {'machine_type': 'n1-standard-4'},
        'replica_count': 1,
        'python_package_spec': {
            'executor_image_uri': 'gcr.io/cloud-aiplatform/training/tf-cpu.2-6:latest',
            'package_uris': [],  # Your training package if any
            'python_module': 'trainer.task',  # Your training module
            'args': [
                f'--training_data_uri={TRAINING_DATA_URI}',
                '--model_name=gemini-1.5-pro',
                '--output_dir=gs://' + BUCKET_NAME + '/fine-tune-output'
            ]
        }
    }]
)

# Run the fine-tuning job
fine_tune_job.run(sync=True)

print('Fine-tuning job completed.')

output

Fine-tuning job completed.

Common variations

Use aiplatform.FineTuneJob if available for direct Gemini fine-tuning support.
Adjust machine_type for larger models or faster training.
Use asynchronous job execution with sync=False to monitor progress separately.
Switch regions by changing the LOCATION variable.

Troubleshooting

If you see permission errors, verify your service account has Vertex AI Admin and Storage Object Viewer roles.
For quota errors, check your Google Cloud project quotas for Vertex AI and increase if needed.
If training fails, check logs in the Google Cloud Console under Vertex AI > Training jobs.

✅

Key Takeaways

Use the official Google Cloud Python SDK google-cloud-aiplatform for Vertex AI fine-tuning.
Upload your training data to Google Cloud Storage before starting fine-tuning jobs.
Configure your fine-tuning job with appropriate machine types and model names for Gemini.
Monitor training jobs via the Google Cloud Console or programmatically with the SDK.
Ensure your service account has all necessary permissions to avoid common errors.

Verified 2026-04 · gemini-1.5-pro

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