Some newer account types are not supported by Azure Machine Learning.
#Sheetcam some paths were not generated correctly password#
Use another Azure storage utility to verify that the account name and password have been entered correctly, and that the container exists. We recommend that you use the new wizard for the Import Data component, which helps you look up and check names.Īlso check whether the account, container, or blob has been deleted. Such problems often occur when you try to manually enter the account name, password, or container path. You didn't fully specify the file path (path to the blob).You didn't specify the container name, or the container does not exist.The password for the storage account is wrong or has changed.Some new account types are not supported for use with Machine Learning designer. Errors on passwords or account names can happen for many reasons: This error occurs in Azure Machine Learning designer when you specify parameters for an Azure storage account, but the name or password cannot be resolved. One or more specified columns were not found.Ĭolumn with name or index "Įxception occurs when the Azure storage account name or container name is specified incorrectly. Revisit the component throwing this exception and validate that the column name or names are correct and that all referenced columns do exist. This error may occur if you have manually typed in a column name or if the column selector has provided a suggested column that did not exist in your dataset when you ran the pipeline. You will receive this error if a column selection is made for a component, but the selected column(s) do not exist in the input data set. Error 0001Įxception occurs if one or more specified columns of data set couldn't be found.
Select the failed component, go to the Outputs+logs tab, you can find the detailed log in the 70_driver_log.txt file under the azureml-logs category.įor detailed component error, you can check it in the error_info.json under module_statistics category.įollowing are error codes of components in the designer. You can find the error message in the designer following these steps: The Linux compilation process will automatically invoke mkimage, therefore, it is important to include the path to the U-Boot tools in the PATH environment variable.This article describes the error messages and exception codes in Azure Machine Learning designer to help you troubleshoot your machine learning pipelines. is the Linux binary image wrapped with a U-Boot header. When building linux.bin, as explained in the Build Kernel page, the kernel build process automatically creates an additional image, with the ".ub" suffix. It is part of U-Boot itself and if U-Boot has been compiled as specified on this page, it will be found under /build/tools/. The mkimage utility is used for this purpose, however, it is not part of the MicroBlaze GNU tools. The kernel and the root filesystem must be wrapped with a U-Boot header in order for U-Boot to accept these files. Instructions to obtain a root filesystem can be found at the Build and Modify a Root File System page. Specific details on building the kernel for MicroBlaze are contained in the Build Kernel page. Running Linux using U-Boot Preparing the Linux images for U-Boot Type "help" or "?" for a complete listing of available commands.ĭocumentation on U-Boot may be found at. The list of U-boot commands can be accessed while in the U-Boot prompt.
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