&引用;失败异常:OSError:[Errno 30]只读文件系统“;在Python Azure函数中使用AzureML时 问题
我正在尝试准备一个新的实验,然后提交给Azure机器学习Python中的Azure函数。因此,我为我的Azure ML工作区注册了一个新的数据集,其中包含使用&引用;失败异常:OSError:[Errno 30]只读文件系统“;在Python Azure函数中使用AzureML时 问题,python,azure-functions,readonly,azureml,oserror,Python,Azure Functions,Readonly,Azureml,Oserror,我正在尝试准备一个新的实验,然后提交给Azure机器学习Python中的Azure函数。因此,我为我的Azure ML工作区注册了一个新的数据集,其中包含使用dataset.register(…)的我的ML模型的培训数据。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) 然后我得到一个故障异常:OSError:[Errno 30]只读文件系统… 思想 我知道,
dataset.register(…
)的我的ML模型的培训数据。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)
然后我得到一个故障异常:OSError:[Errno 30]只读文件系统…
思想
datastore\u path
下创建数据集作为对blob存储的引用,然后将其注册到我的Azure机器学习工作区。但似乎来自分隔文件的方法正在尝试写入文件系统(可能是一些缓存?)
os.chdir(tempfile.gettempdir())将当前工作目录更改为此临时文件夹
,但这没有帮助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
问题是我的虚拟环境中的操作系统版本不兼容 非常感谢他提出的创建docker容器的想法!根据他的建议,我突然收到了以下关于
dataset=dataset.Tabular.from_delimited_files(path=datastore\u path)
命令的错误消息:
异常:NotImplementedError:不支持的Linux发行版debian 10
这让我想起azure机器学习文档中的以下警告:
某些数据集类依赖于azureml dataprep
软件包,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,
Debian(8,9)和CentOS(7)
选择预定义的docker映像2.0-python3.7
(运行Debian 9)而不是3.0-python3.7
(运行Debian 10)解决了问题(请参阅)
我怀疑我最初使用的默认虚拟环境也运行在不兼容的操作系统上