Machine Learning Module Error Codes

 

Updated: August 15, 2016

This topic lists the errors that might be reported in individual modules in Azure Machine Learning Studio, either when an experiment fails, or when you are editing the properties of the module. To resolve the issue, click the error name in the following table and read about common causes.

There are two ways to get the full text of an error message in Studio:

  • Click the link, View Output Log, in the right pane and scroll to the bottom of the. The detailed error message is generally displayed in the last two lines of the window.

  • Select the module that has the error, and click the red X. Only the pertinent error text is displayed.

If the error message text is not helpful, please send us information about the context and any desired additions or changes. You can either submit feedback on the error topic, or visit the Azure Machine Learning forum and post a question.

Exception

Description

Error 0000

Internal Error

Error 0001

Exception occurs if one or more specified columns of data set couldn't be found.

Error 0002

Exception occurs if one or more parameters could not be parsed or converted from specified type into required by target method type.

Error 0003

Exception occurs if one or more of inputs are null or empty.

Error 0004

Exception occurs if parameter is less than or equal to specific value.

Error 0005

Exception occurs if parameter is less than a specific value.

Error 0006

Exception occurs if parameter is greater than or equal to the specified value.

Error 0007

Exception occurs if parameter is greater than a specific value.

Error 0008

Exception occurs if parameter is not in range.

Error 0009

Exception occurs if Azure storage account name or container name specified incorrectly.

Error 0010

Exception occurs if input datasets have column names that should match but do not.

Error 0011

Exception occurs if passed column set argument does not apply to any of dataset columns.

Error 0012

Exception occurs if instance of class could not be created with passed set of arguments.

Error 0013

Exception occurs if passed to module learner has invalid type.

Error 0014

Exception occurs if amount of column unique values is greater than allowed.

Error 0015

Exception occurs if database connection has failed.

Error 0016

Exception occurs if input datasets passed to the module should have compatible column types but do not.

Error 0017

Exception occurs if one or more specified columns have type unsupported by current module.

Error 0018

Exception occurs if input dataset is not valid.

Error 0019

Exception occurs if column is expected to contain sorted values, but it does not.

Error 0020

Exception occurs if number of columns in some of the datasets passed to the module is too small.

Error 0021

Exception occurs if number of rows in some of the datasets passed to the module is too small.

Error 0022

Exception occurs if number of selected columns in input dataset does not equal to the expected number.

Error 0023

Exception occurs if target column of input dataset is not valid for the current trainer module.

Error 0024

Exception occurs if dataset does not contain a label column.

Error 0025

Exception occurs if dataset does not contain a score column.

Error 0026

Exception occurs if columns with the same name is not allowed.

Error 0027

Exception occurs in case when two objects have to be of the same size but are not.

Error 0028

Exception occurs in the case when column set contains duplicated column names and it is not allowed.

Error 0029

Exception occurs in case when invalid URI is passed.

Error 0030

Exception uccurs in the case when it is not possible to download a file.

Error 0031

Exception occurs if number of columns in column set is less than needed.

Error 0032

Exception occurs if argument is not a number.

Error 0033

Exception occurs if argument is Infinity.

Error 0034

Exception occurs if more than one rating exists for a given user-item pair.

Error 0035

Exception occurs if no features were provided for a given user or item.

Error 0036

Exception occurs if multiple feature vectors were provided for a given user or item.

Error 0037

Exception occurs if multiple label columns are specified and just one is allowed.

Error 0038

Exception occurs if number of elements expected should be an exact value, but is not.

Error 0039

Exception occurs if operation has failed.

Error 0040

Exception occurs when calling a deprecated module.

Error 0041

Exception occurs when calling a deprecated module.

Error 0042

Exception occurs when it is not possible to convert column to another type.

Error 0043

Exception occurs when element type does not explicitly implement Equals.

Error 0044

Exception occurs when it is not possible to derive element type of column from the existing values.

Error 0045

Exception occurs when it is not possible to create a column because of mixed element types in the source.

Error 0046

Exception occurs when it is not possible to create directory on specified path.

Error 0047

Exception occurs if number of feature columns in some of the datasets passed to the module is too small.

Error 0048

Exception occurs in the case when it is not possible to open a file.

Error 0049

Exception occurs in the case when it is not possible to parse a file.

Error 0050

Exception occurs in the case when input and output files are the same.

Error 0051

Exception occurs in the case when several output files are the same.

Error 0052

Exception occurs if Azure storage account key is specified incorrectly.

Error 0053

Exception occurs in the case when there are no user features or items for machbox reccomendations.

Error 0054

Exception occurs if there is too few distinct values in the column to complete operation.

Error 0055

Exception occurs when calling a deprecated module.

Error 0056

Exception occurs if columns are selected in a column picker violates the Selected Columns Category constraint.

Error 0057

Exception occurs when attempting to create a file or blob that already exists.

Error 0058

Exception occurs if dataset does not contain the expected label column.

Error 0059

Exception occurs if a column index specified in a column picker cannot be parsed.

Error 0060

Exception occurs when an out of range column range is specified in a column picker.

Error 0061

Exception occurs when attempting to add a row to a DataTable that has a different number of columns than the table.

Error 0062

Exception occurs when attempting to compare two models with different learner types.

Error 0063

Exception occurs when R script evaluation fails with an error.

Error 0064

Exception occurs if Azure storage account name or storage key is specified incorrectly.

Error 0065

Exception occurs if Azure blob name is specified incorrectly.

Error 0066

Exception occurs if a resource could not be uploaded to an Azure Blob.

Error 0067

Exception occurs if a dataset has a different number of columns than expected.

Error 0068

Exception occurs if the specified Hive script is not correct.

Error 0069

Exception occurs if the specified SQL script is not correct.

Error 0070

Exception occurs when attempting to access non-existent Azure table.

Error 0071

Exception occurs if provided credentials are incorrect.

Error 0072

Exception occurs in the case of connection timeout.

Error 0073

Exception occurs if an error occurs while converting a column to another type.

Error 0074

Exception occurs when the Metadata Editor tries to convert a sparse column to categorical.

Error 0075

Exception occurs when an invalid binning function is used when quantizing a dataset.

Error 0077

Exception occurs when unknown blob file write mode passed.

Error 0078

Exception occurs when the HTTP option for Import Data receives a 3xx status code indicating redirection.

Error 0079

Exception occurs if Azure storage container name is specified incorrectly.

Error 0080

Exception occurs when column with all values missing is not allowed by module.

Error 0081

Exception occurs in PCA module if number of dimensions to reduce to is equal to number of feature columns in input dataset, containing at least one sparse feature column.

Error 0082

Exception occurs when a model cannot be successfully deserialized.

Error 0083

Exception occurs if dataset used for training cannot be used for concrete type of learner.

Error 0084

Exception occurs when scores produced from an R Script are evaluated. This is currently unsupported.

Error 0085

Exception occurs when script evaluation fails with an error.

Error 0086

Exception occurs when a counting transform is invalid.

Error 0087

Exception occurs when an invalid count table type is specified for learning with counts modules.

Error 0088

Exception occurs when an invalid counting type is specified for learning with counts modules.

Error 0089

Exception occurs when the specified number of classes is less than the actual number of classes in a dataset used for counting.

Error 0090

Exception occurs when Hive table creation fails.

Error 0100

Exception occurs when an unsupported language is specified for a custom module.

Error 0101

All port and parameter ID's must be unique.

Error 0102

Thrown when a ZIP file cannot be extracted

Error 0103

Thrown when a ZIP file does not contain any .xml files

Error 0104

Thrown when a module definition file references a script that cannot be located

Error 0105

Thrown when a module definition file defines an unsupported parameter type

Error 0106

Thrown when a module definition file defines an unsupported input type

Error 0107

Thrown when a module definition file defines an unsupported output type

Error 0108

Thrown when a module definition file defines more input or output ports than are supported

Error 0109

Thrown when a module definition file defines a column picker incorrectly

Error 0110

Thrown when a module definition file defines a column picker that references a non-existent input port ID

Error 0111

Thrown when a module definition file defines an invalid property

Error 0112

Thrown when a module definition file cannot be parsed

Error 0113

Thrown when a module definition file contains errors.

Error 0114

Thrown when building a custom module fails.

Error 0115

Thrown when a custom module default script has an unsupported extention.

Error 0121

Thrown when SQL write fails because the table is not writeable

Error 0122

Exception occurs if multiple weight columns are specified and just one is allowed.

Error 0123

Exception occurs if column of vectors is specified to be Label column.

Error 0124

Exception occurs if non-numeric or categorical column is specified to be the weight column.

Error 0125

Thrown when schema for multiple datasets do not match.

Error 0126

Exception occurs if the user specifies a SQL domain that is not supported in Azure ML.

Error 0127

Image pixel size exceeds allowed limit.

Error 0128

Number of conditional probabilities for categorical columns exceeds limit.

Error 0129

Number of columns in the dataset exceeds allowed limit.

Error 0130

Exception occurs when all rows in the training dataset contain missing values.

Error 0131

Exception occurs if one or more datasets in a zip file fails to be unzipped and registered correctly.

Error 0132

No file name was specified for unpacking; multiple files were found in zip file.

Error 0133

The specified file was not found in the zip file.

Error 0134

Exception occurs when label column is missing or has insufficient number of labeled rows.

Error 0135

Only centroid-based cluster is supported.

Error 0136

No file name was returned; unable to process the file as a result.

Error 0137

Azure Storage SDK encountered an error converting between table properties and dataset columns during read or write.

Error 0138

Memory has been exhausted, unable to complete running of module.

Error 0139

Exception occurs when it is not possible to convert column to another type.

Error 0140

Exception occurs if passed column set argument does not contain other columns except label column.

Error 0141

Exception occurs if the number of the selected numerical columns and unique values in the categorical and string columns is too small.

Error 0142

Exception occurs when the system cannot load certificate to authenticate

Error 0143

Can't parse user-provided URL that is supposed to be from GitHub.

Error 0144

User-provided GitHub url is missing the expected part.

Error 0145

Cannot create the replication directory for some reason.

Error 0146

When the user files are unzipped into the local directory, the combined path might be too long.

Error 0147

Could not download stuff from GitHub for some reason

Error 0148

Unauthorized access issues while extracting data or creating directory.

Error 0149

The user file does not exist inside GitHub bundle.

Error 0150

The scripts that come from the user package could not be unzipped, most likely because of the collision with Github files.

Error 0151

There was an error writing to cloud storage. Please check the url.

Error 0152

The Azure cloud type was specified incorrectly in the module context.

Error 0153

The storage end point specified is invalid.

Error 0154

The specified server name could not be resolved

Error 0155

The DocDb Client threw an exception

Error 0156

Bad response for HCatalog Server

Error 1000

Internal library exception.

Show: