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feat(generativeai): Create genai_sdk_supervised_example.py #13350

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78 changes: 78 additions & 0 deletions generative_ai/model_tuning/genai_sdk_supervised_example.py
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# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os

from google.genai import types

PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")


def genai_sdk_gemini_tuning_basic() -> types.TuningJob:
# [START genaisdk_gemini_tuning_basic]
import time

from google import genai
from google.genai import types

# TODO(developer): Update and un-comment below lines
# PROJECT_ID = "your-project-id"
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medium

The PROJECT_ID should be explicitly set if the environment variable is not found, or the program should exit with an error message. Consider using a default value or raising an exception if the environment variable is not set. This will prevent unexpected behavior if the environment variable is missing.

  PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
  if not PROJECT_ID:
    raise ValueError("GOOGLE_CLOUD_PROJECT environment variable must be set.")

client = genai.Client(
vertexai=True,
project=PROJECT_ID,
location="us-central1",
)

tuning_job = client.tunings.tune(
base_model="gemini-2.0-flash-lite-001",
train_dataset="gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl",
config=types.CreateTuningJobConfig(
tuned_model_display_name="Example tuning job",
# Set to True to disable tuning intermediate checkpoints.
# export_last_checkpoint_only=True,
),
)

running_states = set([
"JOB_STATE_PENDING",
"JOB_STATE_RUNNING",
])

while tuning_job.state in running_states:
print(tuning_job.state)
tuning_job = client.tunings.get(name=tuning_job.name)
time.sleep(60)
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medium

Consider adding a try-except block to handle potential exceptions during the API call. This will make the example more robust and prevent it from crashing if the API call fails.

    try:
      tuning_job = client.tunings.get(name=tuning_job.name)
    except Exception as e:
      print(f"Error getting tuning job: {e}")
      break # Exit the loop if there's an error


print(tuning_job.tuned_model.model)
print(tuning_job.tuned_model.endpoint)
print(tuning_job.experiment)
# Example response:
# projects/123456789012/locations/us-central1/models/1234567890@1
# projects/123456789012/locations/us-central1/endpoints/123456789012345
# projects/123456789012/locations/us-central1/metadataStores/default/contexts/tuning-experiment-2025010112345678

if tuning_job.tuned_model.checkpoints:
for i in range(len(tuning_job.tuned_model.checkpoints)):
checkpoint = tuning_job.tuned_model.checkpoints[i]
print(f"Checkpoint {i + 1}: ", checkpoint)
# Example response:
# Checkpoint 1: checkpoint_id='1' epoch=1 step=10 endpoint='projects/123456789012/locations/us-central1/endpoints/123456789000000'
# Checkpoint 2: checkpoint_id='2' epoch=2 step=20 endpoint='projects/123456789012/locations/us-central1/endpoints/123456789012345'

# [END genaisdk_gemini_tuning_basic]
return tuning_job


if __name__ == "__main__":
genai_sdk_gemini_tuning_basic()