我正在尝试在Azure机器学习工作室中使用Python SDK v2创建一个管道。已经被这个错误卡住了很多..很多..个小时了，所以现在我正在联系。

我一直遵循以下指南：https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-pipeline-python-sdk
我的设置非常相似，但是我将“data_prep”拆分为两个单独的步骤，并且我使用的是自定义ml模型。
如何定义管道：
`

# the dsl decorator tells the sdk that we are defining an Azure ML pipeline
from azure.ai.ml import dsl, Input, Output
import pathlib
import os

@dsl.pipeline(
    compute=cpu_compute_target,
    description="Car predict pipeline",
)
def car_predict_pipeline(
    pipeline_job_data_input,
    pipeline_job_registered_model_name,
):
    # using data_prep_function like a python call with its own inputs
    data_prep_job = data_prep_component(
        data=pipeline_job_data_input,
    )

    print('-----------------------------------------------')
    print(os.path.realpath(str(pipeline_job_data_input)))
    print(os.path.realpath(str(data_prep_job.outputs.prepared_data)))

    print('-----------------------------------------------')

    train_test_split_job = traintestsplit_component(
        prepared_data=data_prep_job.outputs.prepared_data
    )

    # using train_func like a python call with its own inputs
    train_job = train_component(
        train_data=train_test_split_job.outputs.train_data,  # note: using outputs from previous step
        test_data=train_test_split_job.outputs.test_data,  # note: using outputs from previous step
        registered_model_name=pipeline_job_registered_model_name,
    )

    # a pipeline returns a dictionary of outputs
    # keys will code for the pipeline output identifier
    return {
        # "pipeline_job_train_data": train_job.outputs.train_data,
        # "pipeline_job_test_data": train_job.outputs.test_data,
        "pipeline_job_model": train_job.outputs.model
    }

'我设法通过命令行成功地按顺序运行了每个组件，并生成了一个经过训练的模型。因此，组件和数据运行正常，但管道无法运行。
我可以提供额外的信息，但我不确定需要什么，我不想杂乱的职位。
我试过用谷歌搜索。我试过将教程中的管道与我自己的管道进行比较。我试过使用print语句来隔离问题。到目前为止没有任何效果。我所做的任何事情也没有改变错误，无论如何都是同样的错误。

编辑：

关于我的环境的一些其他信息：

from azure.ai.ml.entities import Environment

custom_env_name = "pipeline_test_environment_pricepredict_model"

pipeline_job_env = Environment(
    name=custom_env_name,
    description="Environment for testing out Jeppes model in pipeline building",
    conda_file=os.path.join(dependencies_dir, "conda.yml"),
    image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:latest",
    version="1.0",
)
pipeline_job_env = ml_client.environments.create_or_update(pipeline_job_env)

print(
    f"Environment with name {pipeline_job_env.name} is registered to workspace, the environment version is {pipeline_job_env.version}"
)

Build status of environment. It had already run successfully.