Using ML Flow>train projects remote throwing error for conda env path

When executing the train-projects-remote notebook in VS Code it throws and error when you try to run the experiment.
**AttributeError: 'Project' object has no attribute 'conda_env_path'**

Output exceeds the [size limit](command:workbench.action.openSettings?[). Open the full output data [in a text editor](command:workbench.action.openLargeOutput?f9df7c58-8ba9-4518-ae74-8429b68af288)
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AttributeError Traceback (most recent call last)
/home/brcampb/projects/ml/notebooks/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb Cell 15 in ()
----> [1](vscode-notebook-cell://wsl%2Bubuntu-22.04/home/brcampb/projects/ml/notebooks/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb#X20sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0) remote_mlflow_run = mlflow.projects.run(uri=".",
[2](vscode-notebook-cell://wsl%2Bubuntu-22.04/home/brcampb/projects/ml/notebooks/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb#X20sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1) parameters={"alpha":0.3},
[3](vscode-notebook-cell://wsl%2Bubuntu-22.04/home/brcampb/projects/ml/notebooks/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb#X20sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2) backend = "azureml",
[4](vscode-notebook-cell://wsl%2Bubuntu-22.04/home/brcampb/projects/ml/notebooks/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb#X20sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3) backend_config = backend_config,
[5](vscode-notebook-cell://wsl%2Bubuntu-22.04/home/brcampb/projects/ml/notebooks/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb#X20sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4) synchronous=True)

File ~/miniconda3/envs/azureml/lib/python3.8/site-packages/mlflow/projects/__init__.py:331, in run(uri, entry_point, version, parameters, docker_args, experiment_name, experiment_id, backend, backend_config, use_conda, storage_dir, synchronous, run_id, run_name, env_manager)
325 backend_config_dict[MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG] = run_id
327 experiment_id = _resolve_experiment_id(
328 experiment_name=experiment_name, experiment_id=experiment_id
329 )
--> 331 submitted_run_obj = _run(
332 uri=uri,
333 experiment_id=experiment_id,
334 entry_point=entry_point,
335 version=version,
336 parameters=parameters,
337 docker_args=docker_args,
338 backend_name=backend,
339 backend_config=backend_config_dict,
340 env_manager=env_manager,
341 storage_dir=storage_dir,
...
---> 96 if mlproject.conda_env_path:
97 _logger.info(_CONSOLE_MSG.format("Creating remote conda environment for project using MLproject"))
98 environment = Environment.from_conda_specification(name="environment", file_path=mlproject.conda_env_path)

AttributeError: 'Project' object has no attribute 'conda_env_path'

notebooks/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb と、セル 15 で失敗している mlflow.projects.run 呼び出しから始めてください。Azure ML バックエンドによる Project オブジェクトの処理と、その conda 環境の検索を追跡してください。リモート実験が conda_env_path AttributeError なしで実行されれば完了です。

索引モデルが issue の本文から書いたものです。