在 Gemini Enterprise Agent Platform Pipelines 上，使用自訂資料微調圖片分類模型

本教學課程說明如何使用 Gemini Enterprise Agent Platform Pipelines 執行端對端機器學習工作流程，包括下列工作：

  • 匯入及轉換資料。
  • 使用轉換後的資料，微調 TFHub 的圖片分類模型。
  • 將訓練好的模型匯入 Gemini Enterprise Agent Platform Model Registry。
  • 選用：使用 Vertex AI Inference 部署模型，以供線上提供使用。

事前準備

  1. 請確認您已完成「設定專案」中的步驟 1 至 3。

  2. 建立獨立的 Python 環境，並安裝 Agent Platform SDK for Python。

  3. 安裝 Kubeflow Pipelines SDK：

    python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet

執行機器學習模型訓練管道

程式碼範例會執行下列作業：

將下列程式碼範例複製到開發環境並運作執行。

圖片分類

# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
from kfp import components
from kfp.v2 import dsl

# %% Loading components
upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url('https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/component.yaml')
deploy_model_to_endpoint_op = components.load_component_from_url('https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml')
transcode_imagedataset_tfrecord_from_csv_op = components.load_component_from_url('https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/community-content/pipeline_components/image_ml_model_training/transcode_tfrecord_image_dataset_from_csv/component.yaml')
load_image_classification_model_from_tfhub_op = components.load_component_from_url('https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/b5b65198a6c2ffe8c0fa2aa70127e3325752df68/community-content/pipeline_components/image_ml_model_training/load_image_classification_model/component.yaml')
preprocess_image_data_op = components.load_component_from_url('https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/community-content/pipeline_components/image_ml_model_training/preprocess_image_data/component.yaml')
train_tensorflow_image_classification_model_op = components.load_component_from_url('https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/community-content/pipeline_components/image_ml_model_training/train_image_classification_model/component.yaml')


# %% Pipeline definition
def image_classification_pipeline():
    class_names = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
    csv_image_data_path = 'gs://cloud-samples-data/ai-platform/flowers/flowers.csv'
    deploy_model = False

    image_data = dsl.importer(
        artifact_uri=csv_image_data_path, artifact_class=dsl.Dataset).output

    image_tfrecord_data = transcode_imagedataset_tfrecord_from_csv_op(
        csv_image_data_path=image_data,
        class_names=class_names
    ).outputs['tfrecord_image_data_path']

    loaded_model_outputs = load_image_classification_model_from_tfhub_op(
        class_names=class_names,
    ).outputs

    preprocessed_data = preprocess_image_data_op(
        image_tfrecord_data,
        height_width_path=loaded_model_outputs['image_size_path'],
    ).outputs

    trained_model = (train_tensorflow_image_classification_model_op(
        preprocessed_training_data_path = preprocessed_data['preprocessed_training_data_path'],
        preprocessed_validation_data_path = preprocessed_data['preprocessed_validation_data_path'],
        model_path=loaded_model_outputs['loaded_model_path']).
                   set_cpu_limit('96').
                   set_memory_limit('128G').
                   add_node_selector_constraint('cloud.google.com/gke-accelerator', 'NVIDIA_TESLA_A100').
                   set_gpu_limit('8').
                   outputs['trained_model_path'])

    vertex_model_name = upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op(
        model=trained_model,
    ).outputs['model_name']

    # Deploying the model might incur additional costs over time
    if deploy_model:
        vertex_endpoint_name = deploy_model_to_endpoint_op(
            model_name=vertex_model_name,
        ).outputs['endpoint_name']

pipeline_func = image_classification_pipeline

# %% Pipeline submission
if __name__ == '__main__':
    from google.cloud import aiplatform
    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()

請注意下列提供的程式碼範例相關事項：

  • Kubeflow 管道定義為 Python 函式。
  • 管道的工作流程步驟是使用 Kubeflow 管道元件建立。將一個元件的輸出內容做為另一個元件的輸入內容，即可將管道的工作流程定義為圖表。舉例來說，preprocess_image_data_op 元件工作依附於 transcode_imagedataset_tfrecord_from_csv_op 元件工作的 tfrecord_image_data_path 輸出內容。
  • 您可以使用 Agent Platform SDK for Python，在 Gemini Enterprise Agent Platform Pipelines 上建立管道執行作業。

監控管道

在 Google Cloud 控制台的「Agent Platform」部分，前往「Pipelines」頁面，然後開啟「Runs」分頁。

前往管道執行作業

後續步驟