1

问题

我正在尝试准备,然后从 Python 中的 Azure 函数向 Azure 机器学习提交一个新实验。因此,我为我的 Azure ML 工作区注册了一个新数据集,其中包含使用dataset.register(.... 但是,当我尝试使用以下代码行创建此数据集时

dataset = Dataset.Tabular.from_delimited_files(path = datastore_paths)

然后我得到一个Failure Exception: OSError: [Errno 30] Read-only file system ....

想法

  1. 我知道如果可能的话,我不应该从 Azure 函数中写入文件系统。但我实际上不想向本地文件系统写入任何内容。我只想创建数据集作为对我的 blob 存储的引用datastore_path,然后将其注册到我的 Azure 机器学习工作区。但似乎该方法from_delimited_files无论如何都试图写入文件系统(也许是一些缓存?)。
  2. 我也知道有一个临时文件夹,允许在其中写入临时文件。但是,我相信我无法真正控制这种方法在哪里写入数据。我已经尝试在使用函数调用之前将当前工作目录更改为这个临时文件夹os.chdir(tempfile.gettempdir()),但这没有帮助。

还有其他想法吗?我不认为我在做一些特别不寻常的事情......

细节

我正在使用 python 3.7 和 azureml-sdk 1.9.0,我可以毫无问题地在本地运行 python 脚本。我目前使用 Azure Functions 扩展版本 0.23.0(以及用于 CI/CD 的 Azure DevOps 管道)从 VSCode 进行部署。

这是我的完整堆栈跟踪:

Microsoft.Azure.WebJobs.Host.FunctionInvocationException: Exception while executing function: Functions.HttpTrigger_Train
 ---> Microsoft.Azure.WebJobs.Script.Workers.Rpc.RpcException: Result: Failure
Exception: OSError: [Errno 30] Read-only file system: '/home/site/wwwroot/.python_packages/lib/site-packages/dotnetcore2/bin/deps.lock'
Stack:   File "/azure-functions-host/workers/python/3.7/LINUX/X64/azure_functions_worker/dispatcher.py", line 345, in _handle__invocation_request
    self.__run_sync_func, invocation_id, fi.func, args)
  File "/usr/local/lib/python3.7/concurrent/futures/thread.py", line 57, in run
    result = self.fn(*self.args, **self.kwargs)
  File "/azure-functions-host/workers/python/3.7/LINUX/X64/azure_functions_worker/dispatcher.py", line 480, in __run_sync_func
    return func(**params)
  File "/home/site/wwwroot/HttpTrigger_Train/__init__.py", line 11, in main
    train()
  File "/home/site/wwwroot/shared_code/train.py", line 70, in train
    dataset = Dataset.Tabular.from_delimited_files(path = datastore_paths)
  File "/home/site/wwwroot/.python_packages/lib/site-packages/azureml/data/_loggerfactory.py", line 126, in wrapper
    return func(*args, **kwargs)
  File "/home/site/wwwroot/.python_packages/lib/site-packages/azureml/data/dataset_factory.py", line 308, in from_delimited_files
    quoting=support_multi_line)
  File "/home/site/wwwroot/.python_packages/lib/site-packages/azureml/dataprep/api/readers.py", line 100, in read_csv
    df = Dataflow._path_to_get_files_block(path, archive_options)
  File "/home/site/wwwroot/.python_packages/lib/site-packages/azureml/dataprep/api/dataflow.py", line 2387, in _path_to_get_files_block
    return datastore_to_dataflow(path)
  File "/home/site/wwwroot/.python_packages/lib/site-packages/azureml/dataprep/api/_datastore_helper.py", line 41, in datastore_to_dataflow
    datastore, datastore_value = get_datastore_value(source)
  File "/home/site/wwwroot/.python_packages/lib/site-packages/azureml/dataprep/api/_datastore_helper.py", line 83, in get_datastore_value
    _set_auth_type(workspace)
  File "/home/site/wwwroot/.python_packages/lib/site-packages/azureml/dataprep/api/_datastore_helper.py", line 134, in _set_auth_type
    get_engine_api().set_aml_auth(SetAmlAuthMessageArgument(AuthType.SERVICEPRINCIPAL, json.dumps(auth)))
  File "/home/site/wwwroot/.python_packages/lib/site-packages/azureml/dataprep/api/engineapi/api.py", line 18, in get_engine_api
    _engine_api = EngineAPI()
  File "/home/site/wwwroot/.python_packages/lib/site-packages/azureml/dataprep/api/engineapi/api.py", line 55, in __init__
    self._message_channel = launch_engine()
  File "/home/site/wwwroot/.python_packages/lib/site-packages/azureml/dataprep/api/engineapi/engine.py", line 300, in launch_engine
    dependencies_path = runtime.ensure_dependencies()
  File "/home/site/wwwroot/.python_packages/lib/site-packages/dotnetcore2/runtime.py", line 141, in ensure_dependencies
    with _FileLock(deps_lock_path, raise_on_timeout=timeout_exception):
  File "/home/site/wwwroot/.python_packages/lib/site-packages/dotnetcore2/runtime.py", line 113, in __enter__
    self.acquire()
  File "/home/site/wwwroot/.python_packages/lib/site-packages/dotnetcore2/runtime.py", line 72, in acquire
    self.lockfile = os.open(self.lockfile_path, os.O_CREAT | os.O_EXCL | os.O_RDWR)

   at Microsoft.Azure.WebJobs.Script.Description.WorkerFunctionInvoker.InvokeCore(Object[] parameters, FunctionInvocationContext context) in /src/azure-functions-host/src/WebJobs.Script/Description/Workers/WorkerFunctionInvoker.cs:line 85
   at Microsoft.Azure.WebJobs.Script.Description.FunctionInvokerBase.Invoke(Object[] parameters) in /src/azure-functions-host/src/WebJobs.Script/Description/FunctionInvokerBase.cs:line 85
   at Microsoft.Azure.WebJobs.Script.Description.FunctionGenerator.Coerce[T](Task`1 src) in /src/azure-functions-host/src/WebJobs.Script/Description/FunctionGenerator.cs:line 225
   at Microsoft.Azure.WebJobs.Host.Executors.FunctionInvoker`2.InvokeAsync(Object instance, Object[] arguments) in C:\projects\azure-webjobs-sdk-rqm4t\src\Microsoft.Azure.WebJobs.Host\Executors\FunctionInvoker.cs:line 52
   at Microsoft.Azure.WebJobs.Host.Executors.FunctionExecutor.InvokeAsync(IFunctionInvoker invoker, ParameterHelper parameterHelper, CancellationTokenSource timeoutTokenSource, CancellationTokenSource functionCancellationTokenSource, Boolean throwOnTimeout, TimeSpan timerInterval, IFunctionInstance instance) in C:\projects\azure-webjobs-sdk-rqm4t\src\Microsoft.Azure.WebJobs.Host\Executors\FunctionExecutor.cs:line 587
   at Microsoft.Azure.WebJobs.Host.Executors.FunctionExecutor.ExecuteWithWatchersAsync(IFunctionInstanceEx instance, ParameterHelper parameterHelper, ILogger logger, CancellationTokenSource functionCancellationTokenSource) in C:\projects\azure-webjobs-sdk-rqm4t\src\Microsoft.Azure.WebJobs.Host\Executors\FunctionExecutor.cs:line 532
   at Microsoft.Azure.WebJobs.Host.Executors.FunctionExecutor.ExecuteWithLoggingAsync(IFunctionInstanceEx instance, ParameterHelper parameterHelper, IFunctionOutputDefinition outputDefinition, ILogger logger, CancellationTokenSource functionCancellationTokenSource) in C:\projects\azure-webjobs-sdk-rqm4t\src\Microsoft.Azure.WebJobs.Host\Executors\FunctionExecutor.cs:line 470
   at Microsoft.Azure.WebJobs.Host.Executors.FunctionExecutor.ExecuteWithLoggingAsync(IFunctionInstanceEx instance, FunctionStartedMessage message, FunctionInstanceLogEntry instanceLogEntry, ParameterHelper parameterHelper, ILogger logger, CancellationToken cancellationToken) in C:\projects\azure-webjobs-sdk-rqm4t\src\Microsoft.Azure.WebJobs.Host\Executors\FunctionExecutor.cs:line 278
   --- End of inner exception stack trace ---
   at Microsoft.Azure.WebJobs.Host.Executors.FunctionExecutor.ExecuteWithLoggingAsync(IFunctionInstanceEx instance, FunctionStartedMessage message, FunctionInstanceLogEntry instanceLogEntry, ParameterHelper parameterHelper, ILogger logger, CancellationToken cancellationToken) in C:\projects\azure-webjobs-sdk-rqm4t\src\Microsoft.Azure.WebJobs.Host\Executors\FunctionExecutor.cs:line 325
   at Microsoft.Azure.WebJobs.Host.Executors.FunctionExecutor.TryExecuteAsyncCore(IFunctionInstanceEx functionInstance, CancellationToken cancellationToken) in C:\projects\azure-webjobs-sdk-rqm4t\src\Microsoft.Azure.WebJobs.Host\Executors\FunctionExecutor.cs:line 117
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1 回答 1

2

问题是我的虚拟环境中的操作系统版本不兼容。

非常感谢PramodValavala-MSFT提出创建 docker 容器的想法!按照他的建议,我突然收到以下 dataset = Dataset.Tabular.from_delimited_files(path = datastore_paths)命令的错误消息:

例外:NotImplementedError:不支持的 Linux 发行版 debian 10。

这让我想起了 azure 机器学习文档中的以下警告:

某些数据集类依赖于 azureml-dataprep 包,该包仅与 64 位 Python 兼容。对于 Linux 用户,这些类仅在以下发行版上受支持:Red Hat Enterprise Linux(7、8)、Ubuntu(14.04、16.04、18.04)、Fedora(27、28)、Debian(8、9)和 CentOS( 7)。

选择预定义的 docker 镜像2.0-python3.7(运行 Debian 9)而不是 3.0-python3.7(运行 Debian 10)解决了这个问题(参见https://hub.docker.com/_/microsoft-azure-functions-python)。

我怀疑我最初使用的默认虚拟环境也在不兼容的操作系统上运行。

于 2020-08-16T22:27:27.400 回答