Azure / Azure/MachineLearningNotebooks
The pattern used to make changes to Azure storage resources has been deprecated and no replacement or desired pattern has been described.
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- Jupyter Notebook
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描述
Greetings,
There are several other issues related to this topic but none seem to drive home the main point. Dataset.File.upload_directory() is not fully a replacement for Datastore.upload() because it does not allow data to propagate from inside an ML Studio workspace to Azure storage resources (like Blob storage). I understand if this is now an anti-pattern, but it seems odd that this functionality has been removed with no apparent public discussion of what the desired pattern for propagating changes upstream from an ML environment is. Lacking documentation about the desired pattern leads to unproductive (and wasteful) speculation about what is the appropriate method to make changes in Azure storage resources from within ML Studio.
For example:
* Is this a temporary change and this functionality will return? https://github.com/Azure/MachineLearningNotebooks/issues/1672
* Are we supposed to use the AZ cli to make changes to these resources? https://github.com/MicrosoftDocs/azure-docs/issues/84419
* Will writes to db (which I haven't even verified still work) or exports to storage resources via pipelines (I'm guessing through OutputFileDatasetConfig) continue to be supported or are these also anti-patterns?
Apologies if I have missed something, and I won't argue why I think this functionality should be supported, but it would be very helpful if you could describe the supported pattern for migrating data changes, additions, and deletions to storage resources from within Azure ML Studio.
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#### Document Details
⚠ *Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.*
* ID: b43f5598-ed69-4aad-9620-5b663902e1e7
* Version Independent ID: 93ee43dd-326c-2ef5-b027-e86a1bf30d49
* Content: [azureml.data.azure_storage_datastore.AzureBlobDatastore class - Azure Machine Learning Python](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.azure_storage_datastore.azureblobdatastore?view=azure-ml-py)
* Content Source: [AzureML-Docset/stable/docs-ref-autogen/azureml-core/azureml.data.azure_storage_datastore.AzureBlobDatastore.yml](https://github.com/MicrosoftDocs/MachineLearning-Python-pr/blob/live/AzureML-Docset/stable/docs-ref-autogen/azureml-core/azureml.data.azure_storage_datastore.AzureBlobDatastore.yml)
* Service: **machine-learning**
* Sub-service: **core**
* GitHub Login: @DebFro
* Microsoft Alias: **debfro**
貢獻指南
這個儲存庫沒有索引到貢獻指南
研究方向
從 AzureML-Docset/stable/docs-ref-autogen/azureml-core/azureml.data.azure_storage_datastore.AzureBlobDatastore.yml 和 AzureBlobDatastore 類別文件開始。檢視 Dataset.File.upload_directory()、Datastore.upload() 和 OutputFileDatasetConfig 的文件化行為,以及連結的 issue。只有在清楚記錄將資料變更從 Azure ML Studio 傳播到 Azure storage 的支援模式後,才算完成。
由索引模型根據 Issue 內容生成。
評估
- 技術堆疊
- azure, jupyter-notebook, machine-learning, python
- 領域
- cloud, documentation, machine-learning
- Issue 類型
- 文件
- 難度
- 5/5
- 預估耗時
- 一週以上
- 活躍度
- 停滯
- 描述清晰度
- 需要釐清
- 新手友好度
- 25/100