Principle:Googleapis Python genai Tuning Job Launch
| Knowledge Sources | |
|---|---|
| Domains | Fine_Tuning, Model_Training |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
The operation of submitting a fine-tuning job that initiates server-side model training on a specified dataset.
Description
Tuning Job Launch submits a fine-tuning request to the service, specifying the base model, training dataset, and configuration. The service validates the inputs, allocates compute resources, and begins training asynchronously. A tuning job object is returned immediately with a resource name for monitoring. The job runs server-side and transitions through states (CREATING, ACTIVE, SUCCEEDED, FAILED, CANCELLED). Upon completion, the tuned model becomes available for inference at a dedicated endpoint.
Usage
Launch a tuning job after preparing the dataset and configuration. The call is non-blocking: it returns a TuningJob object immediately while training proceeds in the background. Use the returned job name to monitor progress via tunings.get. Choose a base model compatible with fine-tuning (e.g., gemini-1.5-flash-002).
Theoretical Basis
Job launch follows an Asynchronous Task Submission pattern:
# Async job pattern (pseudo-code)
job = service.submit_job(
base_model="model-id",
dataset=training_data,
config=hyperparameters
)
# Returns immediately with job reference
# Training runs server-side asynchronously
while job.state != "SUCCEEDED":
job = service.get_job(job.name)
wait()
# Tuned model now available
result = model.generate(tuned_endpoint, "query")