如何在 ML Azure Pipeline 中使用环境

Question

背景

我已经从 conda environment.yml 加上一些 docker 配置和环境变量创建了一个 ML Workspace 环境。我可以从 Python 笔记本中访问它：

env = Environment.get(workspace=ws, name='my-environment', version='1')

我可以成功地使用它来运行一个 Python 脚本作为实验，即

runconfig = ScriptRunConfig(source_directory='script/', script='my-script.py', arguments=script_params)
runconfig.run_config.target = compute_target
runconfig.run_config.environment = env
run = exp.submit(runconfig)

问题

我现在想要运行这个相同的脚本作为管道，这样我就可以用不同的参数触发多个运行。我创建了如下管道：

pipeline_step = PythonScriptStep(
    source_directory='script', script_name='my-script.py',
    arguments=['-a', param1, '-b', param2],
    compute_target=compute_target,
    runconfig=runconfig
)
steps = [pipeline_step]
pipeline = Pipeline(workspace=ws, steps=steps)
pipeline.validate()

当我尝试运行管道时：

pipeline_run = Experiment(ws, 'my_pipeline_run').submit(
    pipeline, pipeline_parameters={...}
)

我收到以下错误：Response status code does not indicate success: 400 (Conda dependencies were not specified. Please make sure that all conda dependencies were specified i).

当我在 Azure 门户中查看管道运行时，似乎还没有选择环境：none 我的 conda 依赖项已配置，因此代码没有运行.我做错了什么？

Answer 1

您快完成了，但是您需要使用 RunConfiguration 而不是 ScriptRunConfig。更多信息 here

from azureml.core.runconfig import RunConfiguration

env = Environment.get(workspace=ws, name='my-environment', version='1')
# create a new runconfig object
runconfig = RunConfiguration()
runconfig.environment = env

pipeline_step = PythonScriptStep(
    source_directory='script', script_name='my-script.py',
    arguments=['-a', param1, '-b', param2],
    compute_target=compute_target,
    runconfig=runconfig
)

pipeline = Pipeline(workspace=ws, steps=[pipeline_step])

pipeline_run = Experiment(ws, 'my_pipeline_run').submit(pipeline)

如何在 ML Azure Pipeline 中使用环境

How do I use an environment in an ML Azure Pipeline

python

azure

azure-machine-learning-studio

azure-machine-learning-service