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feat(generativeai): Create genai_sdk_supervised_checkpoints_set_default_example.py #13346

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feat(generativeai): Create genai_sdk_supervised_checkpoints_set_default_example.py #13346

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60 changes: 60 additions & 0 deletions generative_ai/model_tuning/genai_sdk_supervised_checkpoints_set_default_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_checkpoints_set_default_example() -> types.Model:
# [START genaisdk_gemini_tuning_checkpoints_set_default]
from google import genai

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

name = (
"projects/123456789012/locations/us-central1/tuningJobs/123456789012345"
)
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medium

Consider using environment variables for the name to make the example more configurable and avoid hardcoding project-specific information. This would allow users to easily adapt the example to their own projects without modifying the code directly.

  name = os.getenv("TUNING_JOB_NAME", "projects/123456789012/locations/us-central1/tuningJobs/123456789012345")

tuning_job = client.tunings.get(name=name)
tuned_model = client.models.get(model=tuning_job.tuned_model.model)

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high

It's good practice to include error handling. Wrap the API calls in try...except blocks to catch potential exceptions and provide informative error messages. This will make the example more robust and easier to debug.

  try:
    tuning_job = client.tunings.get(name=name)
    tuned_model = client.models.get(model=tuning_job.tuned_model.model)
  except Exception as e:
    print(f"Error getting tuning job or model: {e}")
    return None

print(f"Default checkpoint: {tuned_model.default_checkpoint_id}")
print(f"Tuned model endpoint: {tuning_job.tuned_model.endpoint}")
# Example response:
# Default checkpoint: 2
# projects/123456789012/locations/us-central1/endpoints/123456789012345

tuned_model = client.models.update(
model=tuned_model.name,
config=types.UpdateModelConfig(default_checkpoint_id="1"),
)
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high

Add error handling here as well, similar to the previous API calls. This will ensure that the example handles potential errors when updating the model.

  try:
    tuned_model = client.models.update(
        model=tuned_model.name,
        config=types.UpdateModelConfig(default_checkpoint_id="1"),
    )
  except Exception as e:
    print(f"Error updating model: {e}")
    return None


print(f"Default checkpoint: {tuned_model.default_checkpoint_id}")
print(f"Tuned model endpoint: {tuning_job.tuned_model.endpoint}")
# Example response:
# Default checkpoint: 1
# projects/123456789012/locations/us-central1/endpoints/123456789000000

# [END genaisdk_gemini_tuning_checkpoints_set_default]
return tuned_model


if __name__ == "__main__":
genai_sdk_gemini_tuning_checkpoints_set_default_example()
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medium

Consider adding a basic test case to supervised_tuning_examples_test.py to verify the functionality of this example. This will help ensure that the example works as expected and prevent regressions in the future.