您可以将每个管道阶段的输出与其输入相关联。例如,给定模型评估的结果,我们应该能够轻松识别与所述评估有关的所有工件(模型评估配置、模型规范、模型参数、训练脚本、训练数据等)。
Azure 机器学习管道参考文章:
https ://github.com/Azure/MachineLearningNotebooks/blob/4a3f8e7025334ea8c0de0bada69b031ce54c24a0/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-databricks -as-compute-target.ipynb
我们有一个 AMLS 管道,它试图使用日期字符串进行参数化,以在旧历史日期的上下文中处理我们的管道。
这是我们用来提交管道的代码
from azureml.core.authentication import InteractiveLoginAuthentication
import requests
auth = InteractiveLoginAuthentication()
aad_token = auth.get_authentication_header()
rest_endpoint = published_pipeline.endpoint
print("You can perform HTTP POST on URL {} to trigger this pipeline".format(rest_endpoint))
# specify the param when running the pipeline
response = requests.post(rest_endpoint,
headers=aad_token,
json={"ExperimentName": "dtpred-Dock2RTEG-EX-param",
"RunSource": "SDK",
"DataPathAssignments": {"input_datapath": {"DataStoreName": "erpgen2datastore","RelativePath": "teams/PredictiveInsights/DatePrediction/2019/10/10"}},
"ParameterAssignments": {"param_inputDate": "2019/10/10"}})
run_id = response.json()["Id"]
print('Submitted pipeline run: ', run_id)