拒绝使用Google AiPlatform ModelServiceClient的权限

Question

为了让顶点AI管道正常工作，我正在遵循一个指南：

https://codelabs.developers.google.com/vertex-pipelines-intro#5

我实现了以下自定义组件：

from google.cloud import aiplatform as aip
from google.oauth2 import service_account

project = "project-id"
region = "us-central1"
display_name = "lookalike_model_pipeline_1646929843"

model_name = f"projects/{project}/locations/{region}/models/{display_name}"
api_endpoint = "us-central1-aiplatform.googleapis.com" #europe-west2
model_resource_path = model_name
client_options = {"api_endpoint": api_endpoint}

# Initialize client that will be used to create and send requests.
client = aip.gapic.ModelServiceClient(credentials=service_account.Credentials.from_service_account_file('..\\service_accounts\\aiplatform_sa.json'), 
client_options=client_options)
#get model evaluation
response = client.list_model_evaluations(parent=model_name)

我得到了以下错误：

(<class 'google.api_core.exceptions.PermissionDenied'>, PermissionDenied("Permission 'aiplatform.modelEvaluations.list' denied on resource '//aiplatform.googleapis.com/projects/project-id/locations/us-central1/models/lookalike_model_pipeline_1646929843' (or it may not exist)."), <traceback object at 0x000002414D06B9C0>)

该模型确实存在，并已完成训练。我已经在aiplatform服务帐户中给自己赋予了管理权限。在指南中，他们不使用服务帐户，而是只使用client_options。client_option有错误的类型，因为它应该是一个dict(str，str)：可选的‘’ClientOptions‘。但这不会导致错误。

我的主要问题是:我怎样才能避开这个许可问题？

我的子问题是：

1. 如何在URL中使用我的model_name变量来访问模型？
2. 如何创建可选的‘’ClientOptions‘对象作为client_option传递
3. 还有另一种方法，我可以从list_model_evaluations的模型，是在VertexAI，培训使用自动？

谢谢

Answer 1

我尝试使用您的代码，但它也不适用于我，并得到了一个不同的错误。正如@DazWilkin所提到的，建议使用云客户机。

我用了aiplatform_v1，效果很好。我注意到的一件事是，您应该始终为client_options定义一个值，以便它将指向正确的端点。检查"aiplatform.googleapis.com"的代码，如果我没有弄错的话，端点默认为ModelServiceClient，它没有预先设置位置。端点应该预先加上一个位置。

请参阅下面的代码。我使用了AutoML模型，它返回它们的模型评估。

from google.cloud import aiplatform_v1 as aiplatform
from typing import Optional

def get_model_eval(
        project_id: str,
        model_id: str,
        client_options: dict,
        location: str = 'us-central1',
        ):

    client_model = aiplatform.services.model_service.ModelServiceClient(client_options=client_options)

    model_name = f'projects/{project_id}/locations/{location}/models/{model_id}'
    list_eval_request = aiplatform.types.ListModelEvaluationsRequest(parent=model_name)
    list_eval = client_model.list_model_evaluations(request=list_eval_request)
    print(list_eval)



api_endpoint = 'us-central1-aiplatform.googleapis.com'
client_options = {"api_endpoint": api_endpoint} # api_endpoint is required for client_options
project_id = 'project-id'
location = 'us-central1'
model_id = '99999999999' # aiplatform_v1 uses the model_id

get_model_eval(
        client_options = client_options,
        project_id = project_id,
        location = location,
        model_id = model_id,
        )

这是我的AutoML文本分类的输出片段：

​

​

Answer 2

尽管我在评论中提到，虽然我熟悉GCP，但我对AI\ML不太熟悉。下列各点应能发挥作用。我没有一个模型可以用来测试它。

BILLING=[[YOUR-BILLING]]

export PROJECT=[[YOUR-PROJECT]]
export LOCATION="us-central1"
export MODEL=[[YOUR-MODEL]]

ACCOUNT="tester"

gcloud projects create ${PROJECT}

gcloud beta billing projects link ${PROJECT} \
--billing-account=${BILLING}

# Unsure whether ML is needed
for SERVICE in "aiplatform" "ml"
do
  gcloud services enable ${SERVICE}.googleapis.com \
  --project=${PROJECT}
done

gcloud iam service-accounts create ${ACCOUNT} \
--project=${PROJECT}

EMAIL=${ACCOUNT}@${PROJECT}.iam.gserviceaccount.com

gcloud projects add-iam-policy-binding ${PROJECT} \
--role=roles/aiplatform.admin \
--member=serviceAccount:${EMAIL}

gcloud iam service-accounts keys create ${PWD}/${ACCOUNT}.json \
--iam-account=${EMAIL} \
--project=${PROJECT}

export GOOGLE_APPLICATION_CREDENTIALS=${PWD}/${ACCOUNT}.json

python3 -m venv venv
source venv/bin/activate
python3 -m pip install google-cloud-aiplatform
python3 main.py

main.py

import os

from google.cloud import aiplatform

project = os.getenv("PROJECT")
location = os.getenv("LOCATION")
model = os.getenv("MODEL")

aiplatform.init(
    project=project,
    location=location,
    experiment="test",
)

parent = f"projects/{project}/locations/{location}/models/{model}"
model = aiplatform.Model(parent)