Ste*_*fan 3 python azure-machine-learning-service azureml azureml-python-sdk
我使用 Microsoft Azure 机器学习 (Azure-ml) 来运行我的 (python) 实验。
为了指定我使用的 VM 和 python 环境:
from azureml.core import Environment
from azureml.core import ScriptRunConfig
# Other imports and code...
# Specify VM and Python environment:
vm_env = Environment.from_conda_specification(name='my-test-env', file_path=PATH_TO_YAML_FILE)
vm_env.docker.enabled = True
vm_env.docker.base_image = 'mcr.microsoft.com/azureml/openmpi3.1.2-cuda10.2-cudnn7-ubuntu18.04'
# Finally, use the environment in the ScriptRunConfig:
src = ScriptRunConfig(source_directory=DEPLOY_CONTAINER_FOLDER_PATH,
script=SCRIPT_FILE_TO_EXECUTE,
arguments=EXECUTE_ARGUMENTS,
compute_target=compute_target,
environment=vm_env)
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我收到该行的以下警告vm_env.docker.enabled = True:
'enabled' is deprecated. Please use the azureml.core.runconfig.DockerConfiguration object with the 'use_docker' param instead.
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关于DockerSection Class和的文档对于DockerConfiguration Class应用DockerConfiguration Class.
我无法弄清楚如何使用该azureml.core.runconfig.DockerConfiguration对象。有人可以为我提供一个例子吗?谢谢!
小智 9
为使用 RunConfiguration 的任何人添加另一个示例:
改变:
run_config = RunConfiguration()
run_config.environment.docker.enabled = True
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到:
run_config = RunConfiguration()
docker_config = DockerConfiguration(use_docker=True)
run_config.docker = docker_config
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run_config稍后可以用作 PythonScriptStep 等的参数。
请注意,docker 属性从内部移至environment直接RunConfiguration。
小智 6
该ScriptRunConfig班现在接受一个docker_runtime_config参数,它是你传递DockerConfiguration的对象。
所以,代码看起来像这样:
from azureml.core import Environment
from azureml.core import ScriptRunConfig
from azureml.core.runconfig import DockerConfiguration
# Other imports and code...
# Specify VM and Python environment:
vm_env = Environment.from_conda_specification(name='my-test-env', file_path=PATH_TO_YAML_FILE)
vm_env.docker.base_image = 'mcr.microsoft.com/azureml/openmpi3.1.2-cuda10.2-cudnn7-ubuntu18.04'
docker_config = DockerConfiguration(use_docker=True)
# Finally, use the environment in the ScriptRunConfig:
src = ScriptRunConfig(source_directory=DEPLOY_CONTAINER_FOLDER_PATH,
script=SCRIPT_FILE_TO_EXECUTE,
arguments=EXECUTE_ARGUMENTS,
compute_target=compute_target,
environment=vm_env,
docker_runtime_config=docker_config)
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