agent-platform-tuning-management

name	agent-platform-tuning-management
metadata	{"category":"AiAndMachineLearning"}
description	Manages GenAI tuning jobs in Agent Platform. Use this to list, get, or cancel ongoing model tuning jobs. Don't use for fine-tuning models (use `agent-platform-tuning`), deploying models to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-management`).

Agent Platform Tuning Management

This skill provides instructions on how to manage GenAI Tuning Jobs using the Agent Platform Python SDK. Use this skill when a user wants to check the status of their tuning runs, find an active tuning job, or cancel a job that is running too long.

Safety & Confirmation Tiers (CRITICAL)

Before executing any commands on behalf of the user, you MUST adhere to the following safety tiers based on the action requested:

1. Tier R: Read-only (list, get)
   • Rule: No confirmation needed. You may execute these commands immediately to gather information for the user.
2. Tier D: Destructive & Interruptive (cancel)
   • Rule: This requires explicit typed confirmation. You MUST output a text message to the user explaining that this will stop the tuning process and any progress will be lost, and asking them to type "I confirm" or "Yes, cancel it". You MUST ask for this confirmation IMMEDIATELY, before executing the cancel command.

Phase 0: Environment Setup

CRITICAL: Before running any of the Python snippets below, you MUST ensure the environment is correctly initialized by following these steps:

1. Virtual Environment: Create and activate a virtual environment:

   python3 -m venv ~/tuning_mgr_venv
   source ~/tuning_mgr_venv/bin/activate
   

2. Google Cloud Authentication: Authenticate with your Google Cloud account and configure active Application Default Credentials (ADC) for Agent Platform access:

   gcloud auth login
   gcloud auth application-default login
   

3. Install Dependencies: Install the required Agent Platform SDK:

   pip install google-cloud-aiplatform
   

4. Execution: Advise the user that every time they execute a Python snippet, they must ensure this virtual environment is activated first.

Workflow Decision Tree

1. Information Gathering: Do you have a Project ID and Region?

   • No -> You MUST ask the user for the missing Project ID and Region in plain text, or advise them to check their gcloud configuration. If neither location has this information, then ask the user to provide it. Do not attempt to search random regions on your own.
   • Yes -> Proceed to Step 2.

2. Task Type: What does the user want to do?

   • Find or List Jobs -> Use the Python SDK to list tuning jobs. (Tier R)
   • Check Status / Inspect a Specific Job -> Use the Python SDK to get tuning job details. (Tier R)
   • Cancel a Job -> Ask for confirmation, then use the Python SDK to cancel the tuning job. (Tier D)

Using the Python SDK

[!NOTE] Resource Verification & Missing Projects/Jobs: If the execution of the Python snippet fails with an error (such as 403 Permission Denied, 404 Not Found, INVALID_ARGUMENT, or indicating a dummy/missing project or job ID), you MUST inform the user that the project or tuning job does not exist or cannot be accessed. You MUST prompt the user to provide a valid Project ID or Job ID, and stop tool execution immediately to wait for their response. Do NOT retry or loop, do NOT assume the resource is valid, and do NOT execute further scripts before receiving valid details from the user.

1. Listing Tuning Jobs (Tier R)

If the user asks "What tuning jobs do I have running?" or wants to find a specific job ID:

from google.cloud import aiplatform_v1

project_id = "YOUR_PROJECT_ID"
region = "YOUR_REGION"
parent = f"projects/{project_id}/locations/{region}"

client = aiplatform_v1.GenAiTuningServiceClient(
    client_options={"api_endpoint": f"{region}-aiplatform.googleapis.com"}
)

jobs = client.list_tuning_jobs(parent=parent)
for job in jobs:
    print(f"Name: {job.name}")
    print(f"Base Model: {job.base_model}")
    print(f"State: {job.state}")

2. Getting Details for a Specific Job (Tier R)

If the user provides a Tuning Job ID and asks for its status:

from google.cloud import aiplatform_v1

project_id = "YOUR_PROJECT_ID"
region = "YOUR_REGION"
job_id = "YOUR_JOB_ID"  # 19-digit ID
name = f"projects/{project_id}/locations/{region}/tuningJobs/{job_id}"

client = aiplatform_v1.GenAiTuningServiceClient(
    client_options={"api_endpoint": f"{region}-aiplatform.googleapis.com"}
)

job = client.get_tuning_job(name=name)
print(f"Name: {job.name}")
print(f"Base Model: {job.base_model}")
print(f"State: {job.state}")
print(f"Tuning Model: {job.tuned_model_display_name}")

3. Canceling a Job (Tier D)

If the user explicitly requests to stop, abort, or cancel a running tuning job:

Safety Check: Action requires explicit typed confirmation before proceeding. You MUST ask the user for confirmation before generating or providing this script, even if they provided the job ID, unless they explicitly use confirming language like "Yes, I confirm, cancel tuning job 123456".

[!IMPORTANT] NEVER pre-emptively provide or execute any cancellation code before receiving the user's response in a new turn. You must never speculate or assume that confirmation will be given. Asking for confirmation and providing the code in a single parallel turn is a severe safety violation.

from google.cloud import aiplatform_v1

project_id = "YOUR_PROJECT_ID"
region = "YOUR_REGION"
job_id = "YOUR_JOB_ID"  # 19-digit ID
name = f"projects/{project_id}/locations/{region}/tuningJobs/{job_id}"

client = aiplatform_v1.GenAiTuningServiceClient(
    client_options={"api_endpoint": f"{region}-aiplatform.googleapis.com"}
)

client.cancel_tuning_job(name=name)
print(f"Successfully requested cancellation for {name}")