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'

Commencez par notebooks/how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-projects-remote/train-projects-remote.ipynb et l’appel défaillant à mlflow.projects.run dans la cellule 15. Suivez la gestion de l’objet Project par le backend Azure ML et la recherche de son environnement conda. Le travail est terminé lorsque l’expérience distante s’exécute sans le conda_env_path AttributeError.

Rédigé par le modèle d'indexation à partir du texte de l'issue.