通过在本地计算机上使用azureml Python SDK，我能够配置Hyperdrive扫描作业并提交到Azure Machine Learning Studio中的计算集群。下面是我的代码示例，基于this tutorial from Microsoft Learn：

#import required libraries
from azure.ai.ml import MLClient, command
from azure.ai.ml.sweep import Choice
from azure.identity import DefaultAzureCredential
# from azure.ai.ml.entities import Environment
#connect to the workspace
ml_client = MLClient.from_config(DefaultAzureCredential())
def build_sweep_job(experiment_name, jobname):
    # Create your base command job
    env = '...'
    compute = '...'
    params = [1, 2]
    n_jobs = len(models)
    max_trials = n_jobs
    cmd = 'python train.py --param ${{inputs.params}}'
    inputs = {
        'params': params[0],
    }
    command_job = command(code='codepath', command=cmd, environment=env, inputs=inputs, compute=compute, experiment_name=experiment_name)
    # Override your inputs with parameter expressions
    command_job_for_sweep = command_job(models=Choice(values=models))
    sweep_job = command_job_for_sweep.sweep(
        compute=compute,
        sampling_algorithm='grid',
        primary_metric='Best value',
        goal='Minimize',
    )
    # Specify your experiment details
    sweep_job.display_name = jobname
    sweep_job.experiment_name = experiment_name
    sweep_job.description = 'Run a hyperparameter sweep job.'
    sweep_job.set_limits(max_concurrent_trials=n_jobs, max_total_trials=max_trials)
    sweep_job.early_termination = None
    
    return sweep_job
sweep_job = build_sweep_job(experiment_name='my_experiment', jobname='todays_job')
returned_sweep_job = ml_client.create_or_update(sweep_job)
print(returned_sweep_job.services["Studio"].endpoint)

代码正确地创建并运行我的作业。最后，ML Studio Web界面显示它已完成：

然而，在我的Python Notebook中，在VS Code中运行，即使在完成一个小时后，状态也显示为“Running”：

我该怎么解决呢？我想知道，没有离开我的本地VS代码，我的工作已经成功结束。