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Known issues and limitations

Known issues and limitations

The following limitations and known issues apply to watsonx.

Notebook issues

You might encounter some of these issues when getting started with and using notebooks.

Failure to export a notebook to HTML in the Jupyter Notebook editor

When you are working with a Jupyter Notebook created in a tool other than Watson Studio, you might not be able to export the notebook to HTML. This issue occurs when the cell output is exposed.


  1. In the Jupyter Notebook UI, go to Edit and click Edit Notebook Metadata.

  2. Remove the following metadata:

    "widgets": {
       "state": {},
       "version": "1.1.2"
  3. Click Edit.

  4. Save the notebook.

Manual installation of some tensor libraries is not supported

Some tensor flow libraries are preinstalled, but if you try to install additional tensor flow libraries yourself, you get an error.

Connection to notebook kernel is taking longer than expected after running a code cell

If you try to reconnect to the kernel and immediately run a code cell (or if the kernel reconnection happened during code execution), the notebook doesn't reconnect to the kernel and no output is displayed for the code cell. You need to manually reconnect to the kernel by clicking Kernel > Reconnect. When the kernel is ready, you can try running the code cell again.

Using the predefined sqlContext object in multiple notebooks causes an error

You might receive an Apache Spark error if you use the predefined sqlContext object in multiple notebooks. Create a new sqlContext object for each notebook. See this Stack Overflow explanation.

Connection failed message

If your kernel stops, your notebook is no longer automatically saved. To save it, click File > Save manually, and you should get a Notebook saved message in the kernel information area, which appears before the Spark version. If you get a message that the kernel failed, to reconnect your notebook to the kernel click Kernel > Reconnect. If nothing you do restarts the kernel and you can't save the notebook, you can download it to save your changes by clicking File > Download as > Notebook (.ipynb). Then you need to create a new notebook based on your downloaded notebook file.

Can't connect to notebook kernel

If you try to run a notebook and you see the message Connecting to Kernel, followed by Connection failed. Reconnecting and finally by a connection failed error message, the reason might be that your firewall is blocking the notebook from running.

If Watson Studio is installed behind a firewall, you must add the WebSocket connection wss://dataplatform.cloud.ibm.com to the firewall settings. Enabling this WebSocket connection is required when you're using notebooks and RStudio.

Insufficient resources available error when opening or editing a notebook

If you see the following message when opening or editing a notebook, the environment runtime associated with your notebook has resource issues:

Insufficient resources available
A runtime instance with the requested configuration can't be started at this time because the required hardware resources aren't available.
Try again later or adjust the requested sizes.

To find the cause, try checking the status page for IBM Cloud incidents affecting Watson Studio. Additionally, you can open a support case at the IBM Cloud Support portal.

Machine learning issues

You might encounter some of these issues when working with machine learning tools.

Region requirements

You can only associate a Watson Machine Learning service instance with your project when the Watson Machine Learning service instance and the Watson Studio instance are located in the same region.

Accessing links if you create a service instance while associating a service with a project

While you are associating a Watson Machine Learning service to a project, you have the option of creating a new service instance. If you choose to create a new service, the links on the service page might not work. To access the service terms, APIs, and documentation, right click the links to open them in new windows.

Federated Learning assets cannot be searched in All assets, search results, or filter results in the new projects UI

You cannot search Federated Learning assets from the All assets view, the search results, or the filter results of your project.

Workaround: Click the Federated Learning asset to open the tool.

Deployment issues

  • A deployment that is inactive (no scores) for a set time (24 hours for the free plan or 120 hours for a paid plan) is automatically hibernated. When a new scoring request is submitted, the deployment is reactivated and the score request is served. Expect a brief delay of 1 to 60 seconds for the first score request after activation, depending on the model framework.
  • For some frameworks, such as SPSS modeler, the first score request for a deployed model after hibernation might result in a 504 error. If this happens, submit the request again; subsequent requests should succeed.

Watson Machine Learning limitations

AutoAI known limitations

  • Currently, AutoAI experiments do not support double-byte character sets. AutoAI only supports CSV files with ASCII characters. Users must convert any non-ASCII characters in the file name or content, and provide input data as a CSV as defined in this CSV standard.

  • To interact programmatically with an AutoAI model, use the REST API instead of the Python client. The APIs for the Python client required to support AutoAI are not generally available at this time.

Data module not found in IBM Federated Learning

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'

The issue possibly results from using an outdated DataHandler. 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.

SPSS Modeler issues

You might encounter some of these issues when working in SPSS Modeler.

SPSS Modeler runtime restrictions

Watson Studio does not include SPSS functionality in Peru, Ecuador, Colombia and Venezuela.

Timestamp data measured in microseconds

If you have timestamp data that is measured in microseconds, you can use the more precise data in your flow. However, you can use the data only within the following limitations.

  • You can import data that is measured in microseconds only from connectors that support SQL pushback. For more information about which connectors support SQL pushback, see Supported data sources for SPSS Modeler.
  • You need to manually save each Data Asset node that imports data from these connectors. Save these nodes after enabling the new option in the Flow Properties. For information about enabling the new option, see Setting properties for flows.

To save a Data Asset node, do the following:

  1. Double-click the node to open its properties.
  2. Click Save, and then close the properties.

Merge node and unicode characters

The Merge node treats the following very similar Japanese characters as the same character.
Japanese characters

Connection issues

You might encounter this issue when working with connections.

Cloudera Impala connection does not work with LDAP authentication

If you create a connection to a Cloudera Impala data source and the Cloudera Impala server is set up for LDAP authentication, the username and password authentication method in IBM watsonx will not work.

Workaround: Disable the Enable LDAP Authentication option on the Impala server. See Configuring LDAP Authentication in the Cloudera documentation.

Watson Pipelines known issues

The issues pertain to Watson Pipelines.

Nesting loops more than 2 levels can result in pipeline error

Nesting loops more than 2 levels can result in an error when you run the pipeline, such as Error retrieving the run. Reviewing the logs can show an error such as text in text not resolved: neither pipeline_input nor node_output. If you are looping with output from a Bash script, the log might list an error like this: PipelineLoop can't be run; it has an invalid spec: non-existent variable in $(params.run-bash-script-standard-output). To resolve the problem, do not nest loops more than 2 levels.

Asset browser does not always reflect count for total numbers of asset type

When selecting an asset from the asset browser, such as choosing a source for a Copy node, you see that some of the assets list the total number of that asset type available, but notebooks do not. That is a current limitation.

Cannot delete pipeline versions

Currently, you cannot delete saved versions of pipelines that you no longer need.

Deleting an AutoAI experiment fails under some conditions

Using a Delete AutoAI experiment node to delete an AutoAI experiment that was created from the Projects UI does not delete the AutoAI asset. However, the rest of the flow can complete successfully.

Cache appears enabled but is not enabled

If the Copy assets Pipelines node's Copy mode is set to Overwrite, cache is displayed as enabled but remains disabled.

Watson Pipelines limitations

These limitations apply to Watson Pipelines.

Single pipeline limits

These limitation apply to a single pipeline, regardless of configuration.

  • Any single pipeline cannot contain more than 120 standard nodes
  • Any pipeline with a loop cannot contain more than 600 nodes across all iterations (for example, 60 iterations - 10 nodes each)

Limitations by configuration size

Small configuration

A SMALL configuration supports 600 standard nodes (across all active pipelines) or 300 nodes run in a loop. For example:

  • 30 standard pipelines with 20 nodes run in parallel = 600 standard nodes
  • 3 pipelines containing a loop with 10 iterations and 10 nodes in each iteration = 300 nodes in a loop

Medium configuration

A MEDIUM configuration supports 1200 standard nodes (across all active pipelines) or 600 nodes run in a loop. For example:

  • 30 standard pipelines with 40 nodes run in parallel = 1200 standard nodes
  • 6 pipelines containing a loop with 10 iterations and 10 nodes in each iteration = 600 nodes in a loop

Large configuration

A LARGE configuration supports 4800 standard nodes (across all active pipelines) or 2400 nodes run in a loop. For example:

  • 80 standard pipelines with 60 nodes run in parallel = 4800 standard nodes
  • 24 pipelines containing a loop with 10 iterations and 10 nodes in each iteration = 2400 nodes in a loop

Input and output size limits

Input and output values, which include pipeline parameters, user variables, and generic node inputs and outputs, cannot exceed 10 KB of data.

Batch input limited to data assets

Currently, input for batch deployment jobs is limited to data assets. This means that certain types of deployments, which require JSON input or multiple files as input, are not supported. For example, SPSS models and Decision Optimization solutions that require multiple files as input are not supported.

Issues with Cloud Object Storage

These issue apply to working with Cloud Object Storage.

Issues with Cloud Object Storage when Key Protect is enabled

Key Protect in conjunction with Cloud Object Storage is not supported for working with Watson Machine Learning assets. If you are using Key Protect, you might encounter these issues when you are working with assets in Watson Studio.

  • Training or saving these Watson Machine Learning assets might fail:
    • Auto AI
    • Federated Learning
    • Watson Pipelines
  • You might be unable to save an SPSS model or a notebook model to a project

Issues with watsonx.governance

Delay showing prompt template deployment data in a factsheet

When a deployment is created for a prompt template, the facts for the deployment are not added to factsheet immediately. You must first evaluate the deployment or view the lifecycle tracking page to add the facts to the factsheet.

Display issues for existing Factsheet users

If you previously used factsheets with IBM Knowledge Catalog and you create a new AI use case in watsonx.governance, you might see some display issues, such as duplicate Risk level fields in the General information and Details section of the AI use case interface.

To resolve display problems, update the model_entry_user asset type definition. For details on updating a use case programmatically, see Customizing details for a use case or factsheet.

Redundant attachment links in factsheet

A factsheet tracks all of the events for an asset over all phases of the lifecycle. Attachments show up in each stage, creating some redundancy in the factsheet.

Generative AI search and answer
These answers are generated by a large language model in watsonx.ai based on content from the product documentation. Learn more