Here are the answers to common troubleshooting questions about using IBM Watson Machine Learning.
Getting help and support for Watson Machine Learning
If you have problems or questions when using Watson Machine Learning, you can get help by searching for information or by asking questions through a forum. You can also open a support ticket.
When using the forums to ask a question, tag your question so that it is seen by the Watson Machine Learning development teams.
If you have technical questions about Watson Machine Learning, post your question on Stack Overflow 
and tag your question with "ibm-bluemix" and "machine-learning".
For questions about the service and getting started instructions, use the IBM developerWorks dW Answers forum. Include the "machine-learning" and "bluemix" tags.
Contents
- Authorization token has not been provided
- Invalid authorization token
- Authorization token and instance_id which was used in the request are not the same
- Authorization token is expired
- Public key needed for authentication is not available
- Operation timed out after {{timeout}}
- Unhandled exception of type {{type}} with {{status}}
- Unhandled exception of type {{type}} with {{response}}
- Unhandled exception of type {{type}} with {{json}}
- Unhandled exception of type {{type}} with {{message}}
- Requested object could not be found
- Underlying database reported too many requests
- The definition of the evaluation is not defined neither in the artifactModelVersion nor in the deployment. It needs to be specified " +\n "at least in one of the places
- Data module not found in IBM Federated Learning
- Evaluation requires learning configuration specified for the model
- Evaluation requires spark instance to be provided in
X-Spark-Service-Instanceheader - Model does not contain any version
- Patch operation can only modify existing learning configuration
- Patch operation expects exactly one replace operation
- The given payload is missing required fields: FIELD or the values of the fields are corrupted
- Provided evaluation method: METHOD is not supported. Supported values: VALUE
- There can be only one active evaluation per model. Request could not be completed because of existing active evaluation: {{url}}
- The deployment type {{type}} is not supported
- Incorrect input: ({{message}})
- Insufficient data - metric {{name}} could not be calculated
- For type {{type}} spark instance must be provided in
X-Spark-Service-Instanceheader - Action {{action}} has failed with message {{message}}
- Path
{{path}}is not allowed. Only allowed path for patch stream is/status - Patch operation is not allowed for instance of type
{{$type}} - Data connection
{{data}}is invalid for feedback_data_ref - Path {{path}} is not allowed. Only allowed path for patch model is
/deployed_version/urlor/deployed_version/hreffor V2 - Parsing failure: {{msg}}
- Runtime environment for selected model: {{env}} is not supported for
learning configuration. Supported environments: - [{{supported_envs}}] - Current plan '{{plan}}' only allows {{limit}} deployments
- Database connection definition is not valid ({{code}})
- There were problems while connecting underlying {{system}}
- Error extracting X-Spark-Service-Instance header: ({{message}})
- This functionality is forbidden for non beta users
- {{code}} {{message}}
- Rate limit exceeded
- Invalid query parameter
{{paramName}}value: {{value}} - Invalid token type: {{type}}
- Invalid token format. Bearer token format should be used
- Input JSON file is missing or invalid: 400
- Authorization token has expired: 401
- Unknown deployment identification:404
- Internal server error:500
- Invalid type for ml_artifact: Pipeline
- ValueError: Training_data_ref name and connection cannot be None, if Pipeline Artifact is not given.
Public key needed for authentication is not available.
What's happening
The REST API cannot be invoked successfully.
Why it's happening
This is internal service issue.
How to fix it
The issue needs to be fixed by support team.
Operation timed out after {{timeout}}
What's happening
The REST API cannot be invoked successfully.
Why it's happening
The timeout occurred during performing requested operation.
How to fix it
Try to invoke desired operation again.
Unhandled exception of type {{type}} with {{status}}
What's happening
The REST API cannot be invoked successfully.
Why it's happening
This is internal service issue.
How to fix it
Try to invoke desired operation again. If it occurs more times than it needs to be fixed by support team.
Unhandled exception of type {{type}} with {{response}}
What's happening
The REST API cannot be invoked successfully.
Why it's happening
This is internal service issue.
How to fix it
Try to invoke desired operation again. If it occurs more times than it needs to be fixed by support team.
Unhandled exception of type {{type}} with {{json}}
What's happening
The REST API cannot be invoked successfully.
Why it's happening
This is internal service issue.
How to fix it
Try to invoke desired operation again. If it occurs more times than it needs to be fixed by support team.
Unhandled exception of type {{type}} with {{message}}
What's happening
The REST API cannot be invoked successfully.
Why it's happening
This is internal service issue.
How to fix it
Try to invoke desired operation again. If it occurs more times than it needs to be fixed by support team.
Requested object could not be found.
What's happening
The REST API cannot be invoked successfully.
Why it's happening
The request resource could not be found.
How to fix it
Ensure that you are referring to the existing resource.
Underlying database reported too many requests.
What's happening
The REST API cannot be invoked successfully.
Why it's happening
The user has sent too many requests in a given amount of time.
How to fix it
Try to invoke desired operation again.
The definition of the evaluation is not defined neither in the artifactModelVersion nor in the deployment. It needs to be specified " +\n "at least in one of the places.
What's happening
The REST API cannot be invoked successfully.
Why it's happening
Learning Configuration does not contain all required information
How to fix it
Provide definition in learning configuration
Evaluation requires learning configuration specified for the model.
What's happening
There is no possibility to create learning iteration.
Why it's happening
There is no learning configuration defined for the model.
How to fix it
Create learning configuration and try to create learning iteration again.
Evaluation requires spark instance to be provided in X-Spark-Service-Instance header
What's happening
The REST API cannot be invoked successfully.
Why it's happening
There is no all required information in learning configuration
How to fix it
Provide spark_service in Learning Configuration or in X-Spark-Service-Instance header
Model does not contain any version.
What's happening
There is no possibility to create neither deployment nor set learning configuration.
Why it's happening
There is inconsistency related to the persistence of the model.
How to fix it
Try to persist the model again and try perform the action again.
Data module not found in IBM Federated Learning.
What's happening
The data handler for IBM Federated Learning is trying to extract a data module from the FL library but is unable to find it. You might see the following error message:
ModuleNotFoundError: No module named 'ibmfl.util.datasets'
Why it's happening
Possibly an outdated DataHandler.
How to fix it
Please review and update your DataHandler to conform to the latest spec. Here is the link to the most recent MNIST data handler or ensure your sample versions are up-to-date.
Patch operation can only modify existing learning configuration.
What's happening
There is no possibility to invoke patch REST API method to patch learning configuration.
Why it's happening
There is no learning configuration set for this model or model does not exist.
How to fix it
Endure that model exists and has already learning configuration set.
Patch operation expects exactly one replace operation.
What's happening
The deployment cannot be patched.
Why it's happening
The patch payload contains more than one operation or the patch operation is different than replace.
How to fix it
Use only one operation in the patch payload which is replace operation
The given payload is missing required fields: FIELD or the values of the fields are corrupted.
What's happening
There is no possibility to process action which is related to access to the underlying data set.
Why it's happening
The access to the data set is not properly defined.
How to fix it
Correct the access definition for the data set.
Provided evaluation method: METHOD is not supported. Supported values: VALUE.
What's happening
There is no possibility to create learning configuration.
Why it's happening
The wrong evaluation method was used to create learning configuration.
How to fix it
Use supported evaluation method which is one of: regression, binary, multiclass.
There can be only one active evaluation per model. Request could not be completed because of existing active evaluation: {{url}}
What's happening
There is no possibility to create another learning iteration
Why it's happening
There can be only one running evaluation for the model.
How to fix it
See the already running evaluation or wait till it ends and start the new one.
The deployment type {{type}} is not supported.
What's happening
There is no possibility to create deployment.
Why it's happening
Not supported deployment type was used.
How to fix it
Supported deployment type should be used.
Incorrect input: ({{message}})
What's happening
The REST API cannot be invoked successfully.
Why it's happening
There is an issue with parsing json.
How to fix it
Ensure that the correct json is passed in the request.
Insufficient data - metric {{name}} could not be calculated
What's happening
Learning iteration has failed
Why it's happening
Value for metric with defined threshold could not be calculated because of insufficient feedback data
How to fix it
Review and improve data in data source feedback_data_ref in learning configuration
For type {{type}} spark instance must be provided in X-Spark-Service-Instance header
What's happening
Deployment cannot be created
Why it's happening
batch and streaming deployments require spark instance to be provided
How to fix it
Provide spark instance in X-Spark-Service-Instance header
Action {{action}} has failed with message {{message}}
What's happening
The REST API cannot be invoked successfully.
Why it's happening
There was an issue with invoking underlying service.
How to fix it
If there is suggestion how to fix the issue than follow it. Contact the support team if there is no suggestion in the message or the suggestion does not solve the issue.
Path {{path}} is not allowed. Only allowed path for patch stream is /status
What's happening
There is no possibility to patch the stream deployment.
Why it's happening
The wrong path was used to patch the stream deployment.
How to fix it
Patch the stream deployment with supported path option which is /status (it allows to start/stop stream processing.
Patch operation is not allowed for instance of type {{$type}}
What's happening
There is no possibility to patch the deployment.
Why it's happening
The wrong deployment type is being patched.
How to fix it
Patch the stream deployment type.
Data connection {{data}} is invalid for feedback_data_ref
What's happening
There is no possibility to create learning configuration for the model.
Why it's happening
Not supported data source was used when defining feedback_data_ref.
How to fix it
Use only supported data source type which is dashdb
Path {{path}} is not allowed. Only allowed path for patch model is /deployed_version/url or /deployed_version/href for V2
What's happening
There is no option to patch model.
Why it's happening
The wrong path was used during patching of the model.
How to fix it
Patch model with supported path which allows to update the version of deployed model.
Parsing failure: {{msg}}
What's happening
The REST API cannot be invoked successfully.
Why it's happening
The requested payload could not be parsed successfully.
How to fix it
Ensure that your request payload is correct and can be parsed correctly.
Runtime environment for selected model: {{env}} is not supported for learning configuration. Supported environments: [{{supported_envs}}].
What's happening
There is no option to create learning configuration
Why it's happening
The model for which the learning_configuration was tried to be created is not supported.
How to fix it
Create learning configuration for model which has supported runtime.
Current plan '{{plan}}' only allows {{limit}} deployments
What's happening
There is no possibility to create deployment.
Why it's happening
The limit for number of deployments was reached for the current plan.
How to fix it
Upgrade to the plan which does not have such limitation.
Database connection definition is not valid ({{code}})
What's happening
There is no possibility utilize the learning configuration functionality.
Why it's happening
Database connection definition is not valid.
How to fix it
Try to fix the issue which is described by code returned by underlying database.
There were problems while connecting underlying {{system}}
What's happening
The REST API cannot be invoked successfully.
Why it's happening
There was an issue during connection to the underlying system. It might be temporary network issue.
How to fix it
Try to invoke desired operation again. If it occurs more times than contact support team.
Error extracting X-Spark-Service-Instance header: ({{message}})
What's happening
There is no possibility to invoke REST API which requires Spark credentials
Why it's happening
There is an issue with base-64 decoding or parsing Spark credentials.
How to fix it
Ensure that the correct Spark credentials were correctly base-64 encoded. For more information, see the documentation.
This functionality is forbidden for non beta users.
What's happening
The desired REST API cannot be invoked successfully.
Why it's happening
REST API which was invoked is currently in beta.
How to fix it
If you are interested in participating, add yourself to the wait list. The details can be found in documentation.
{{code}} {{message}}
What's happening
The REST API cannot be invoked successfully.
Why it's happening
There was an issue with invoking underlying service.
How to fix it
If there is suggestion how to fix the issue then follow it. Contact the support team if there is no suggestion in the message or the suggestion does not solve the issue.
Rate limit exceeded.
What's happening
Rate limit exceeded.
Why it's happening
Rate limit for current plan has been exceeded.
How to fix it
To solve this problem, acquire another plan with a greater rate limit
Invalid query parameter {{paramName}} value: {{value}}
What's happening
Validation error as passed incorrect value for query parameter.
Why it's happening
Error in getting result for query.
How to fix it
Correct query parameter value. The details can be found in documentation.
Invalid token type: {{type}}
What's happening
Error regarding token type.
Why it's happening
Error in authorization.
How to fix it
Token should be started with Bearer prefix
Invalid token format. Bearer token format should be used.
What's happening
Error regarding token format.
Why it's happening
Error in authorization.
How to fix it
Token should be bearer token and should start with Bearer prefix
Input JSON file is missing or invalid: 400
What's happening
The following message displays when you try to score online: Input JSON file is missing or invalid.
Why it's happening
This message displays when the scoring input payload doesn't match the expected input type that is required for scoring the model. Specifically, the following reasons may apply:
- The input payload is empty.
- The input payload schema is not valid.
- The input datatypes does not match the expected datatypes.
How to fix it
Correct the input payload. Make sure that the payload has correct syntax, a valid schema, and proper data types. After you make corrections, try to score online again. For syntax issues, verify the JSON file by using the jsonlint command.
Unknown deployment identification:404
What's happening
The following message displays when you try to score online Unknown deployment identification.
Why it's happening
This message displays when the deployment ID that is used for scoring does not exists.
How to fix it
Make sure you are providing the correct deployment ID. If not, deploy the model with the deployment ID and then try scoring it again.
Internal server error:500
What's happening
The following message displays when you try to score online: Internal server error
Why it's happening
This message displays if the downstream data flow on which the online scoring depends fails.
How to fix it
After waiting for a period of time, try to score online again. If it fails again then contact IBM Support.
Invalid type for ml_artifact: Pipeline
What's happening
The following message displays when you try to publish Spark model using Common API client library on your workstation.
Why it's happening
This message displays if you have invalid pyspark setup in operating system.
How to fix it
Set up system environment paths according to the instruction:
SPARK_HOME={installed_spark_path}
JAVA_HOME={installed_java_path}
PYTHONPATH=$SPARK_HOME/python/
ValueError: Training_data_ref name and connection cannot be None, if Pipeline Artifact is not given.
What's happening
The training data set is missing or has not been properly referenced.
Why it's happening
The Pipeline Artifact is a training data set in this instance.
How to fix it
When persisting a Spark PipelineModel you MUST supply a training data set, if you don't the client says it doesn't support PipelineModels, rather than saying a PipelineModel must be accompanied by the training set.