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  1. Red Hat Enterprise Linux AI
  2. RHELAI-3538

Command "ilab taxonomy diff" outputs a blank error message

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      To Reproduce Steps to reproduce the behavior:

      1. Create a qna.yaml file inside any desired directory inside the default "/root/.local/share/instructlab/taxonomy/knowledge/" directory.
      2. Run the "ilab taxonomy diff" command to test your qna.yml file.
      3. If the file has any errors, the previous command will fail, but it will output a blank error message like the following:

      [root@bastion redhataccelerators]# ilab taxonomy diff
      knowledge/technology/redhataccelerators/qna.yaml
      Reading taxonomy failed with the following error: 
      [root@bastion redhataccelerators]#
      

      Expected behavior
      The command should output what is the actual error found.

      Screenshots

      • Attached Image

      Device Info (please complete the following information):

      • Hardware Specs: [e.g. Apple M2 Pro Chip, 16 GB Memory, etc.]
        [root@bastion redhataccelerators]# hostnamectl 
         Static hostname: bastion.hrdnh.internal
               Icon name: computer-vm
                 Chassis: vm 🖴
              Machine ID: ec2a61c5ad048b14138ac906def5cd15
                 Boot ID: 9ce3fe82342148229c642ccaf015e8ab
          Virtualization: amazon
        Operating System: Red Hat Enterprise Linux 9.20241104.0.4 (Plow)
             CPE OS Name: cpe:/o:redhat:enterprise_linux:9::baseos
                  Kernel: Linux 5.14.0-427.42.1.el9_4.x86_64
            Architecture: x86-64
         Hardware Vendor: Amazon EC2
          Hardware Model: g6.12xlarge
        Firmware Version: 1.0
        [root@bastion redhataccelerators]# free -ht
                       total        used        free      shared  buff/cache   available
        Mem:           181Gi       2.2Gi       179Gi       1.0Mi       1.7Gi       179Gi
        Swap:          8.0Gi          0B       8.0Gi
        Total:         189Gi       2.2Gi       187Gi
        [root@bastion redhataccelerators]# nproc
        48
        [root@bastion redhataccelerators]# nvidia-smi 
        Mon Mar  3 18:03:13 2025       
        +-----------------------------------------------------------------------------------------+
        | NVIDIA-SMI 550.127.05             Driver Version: 550.127.05     CUDA Version: 12.4     |
        |-----------------------------------------+------------------------+----------------------+
        | GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
        | Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
        |                                         |                        |               MIG M. |
        |=========================================+========================+======================|
        |   0  NVIDIA L4                      On  |   00000000:38:00.0 Off |                    0 |
        | N/A   27C    P8             11W /   72W |       1MiB /  23034MiB |      0%      Default |
        |                                         |                        |                  N/A |
        +-----------------------------------------+------------------------+----------------------+
        |   1  NVIDIA L4                      On  |   00000000:3A:00.0 Off |                    0 |
        | N/A   26C    P8             11W /   72W |       1MiB /  23034MiB |      0%      Default |
        |                                         |                        |                  N/A |
        +-----------------------------------------+------------------------+----------------------+
        |   2  NVIDIA L4                      On  |   00000000:3C:00.0 Off |                    0 |
        | N/A   29C    P8             11W /   72W |       1MiB /  23034MiB |      0%      Default |
        |                                         |                        |                  N/A |
        +-----------------------------------------+------------------------+----------------------+
        |   3  NVIDIA L4                      On  |   00000000:3E:00.0 Off |                    0 |
        | N/A   26C    P8             11W /   72W |       1MiB /  23034MiB |      0%      Default |
        |                                         |                        |                  N/A |
        +-----------------------------------------+------------------------+----------------------+
                                                                                                 
        +-----------------------------------------------------------------------------------------+
        | Processes:                                                                              |
        |  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
        |        ID   ID                                                               Usage      |
        |=========================================================================================|
        |  No running processes found                                                             |
        +-----------------------------------------------------------------------------------------+
        
      • OS Version: [e.g. Mac OS 14.4.1, Fedora Linux 40]
        [root@bastion redhataccelerators]# cat /etc/os-release 
        NAME="Red Hat Enterprise Linux"
        VERSION="9.20241104.0.4 (Plow)"
        ID="rhel"
        ID_LIKE="fedora"
        VERSION_ID="9.4"
        PLATFORM_ID="platform:el9"
        PRETTY_NAME="Red Hat Enterprise Linux 9.20241104.0.4 (Plow)"
        ANSI_COLOR="0;31"
        LOGO="fedora-logo-icon"
        CPE_NAME="cpe:/o:redhat:enterprise_linux:9::baseos"
        HOME_URL="https://www.redhat.com/"
        DOCUMENTATION_URL="https://access.redhat.com/documentation/en-us/red_hat_enterprise_linux/9"
        BUG_REPORT_URL="https://issues.redhat.com/"
        REDHAT_BUGZILLA_PRODUCT="Red Hat Enterprise Linux 9"
        REDHAT_BUGZILLA_PRODUCT_VERSION=9.4
        REDHAT_SUPPORT_PRODUCT="Red Hat Enterprise Linux"
        REDHAT_SUPPORT_PRODUCT_VERSION="9.4"
        OSTREE_VERSION='9.20241104.0'
        VARIANT="RHEL AI"
        VARIANT_ID=rhel_ai
        RHEL_AI_VERSION_ID='1.3.0'
        [root@bastion redhataccelerators]# uname -a
        Linux bastion.hrdnh.internal 5.14.0-427.42.1.el9_4.x86_64 #1 SMP PREEMPT_DYNAMIC Fri Oct 18 14:35:40 EDT 2024 x86_64 x86_64 x86_64 GNU/Linux
        
      • InstructLab Version: [output of \\\{{{}ilab --version{}}}]
        [root@bastion redhataccelerators]# ilab --version
        ilab, version 0.21.2
        
      • Provide the output of these two commands:
        • sudo bootc status --format json | jq .status.booted.image.image.image to print the name and tag of the bootc image, should look like registry.stage.redhat.io/rhelai1/bootc-intel-rhel9:1.3-1732894187
          [root@bastion redhataccelerators]# bootc status --format json | jq .status.booted.image.image.image
          "registry.stage.redhat.io/rhelai1/bootc-aws-nvidia-rhel9:1.3.1"
          
        • ilab system info to print detailed information about InstructLab version, OS, and hardware – including GPU / AI accelerator hardware
          [root@bastion redhataccelerators]# ilab system info
          Platform:
            sys.version: 3.11.7 (main, Oct  9 2024, 00:00:00) [GCC 11.4.1 20231218 (Red Hat 11.4.1-3)]
            sys.platform: linux
            os.name: posix
            platform.release: 5.14.0-427.42.1.el9_4.x86_64
            platform.machine: x86_64
            platform.node: bastion.hrdnh.internal
            platform.python_version: 3.11.7
            os-release.ID: rhel
            os-release.VERSION_ID: 9.4
            os-release.PRETTY_NAME: Red Hat Enterprise Linux 9.4 (Plow)
            memory.total: 181.77 GB
            memory.available: 179.52 GB
            memory.used: 0.81 GB
          
          InstructLab:
            instructlab.version: 0.21.2
            instructlab-dolomite.version: 0.2.0
            instructlab-eval.version: 0.4.1
            instructlab-quantize.version: 0.1.0
            instructlab-schema.version: 0.4.1
            instructlab-sdg.version: 0.6.1
            instructlab-training.version: 0.6.1
          
          Torch:
            torch.version: 2.4.1
            torch.backends.cpu.capability: AVX2
            torch.version.cuda: 12.4
            torch.version.hip: None
            torch.cuda.available: True
            torch.backends.cuda.is_built: True
            torch.backends.mps.is_built: False
            torch.backends.mps.is_available: False
            torch.cuda.bf16: True
            torch.cuda.current.device: 0
            torch.cuda.0.name: NVIDIA L4
            torch.cuda.0.free: 21.8 GB
            torch.cuda.0.total: 22.0 GB
            torch.cuda.0.capability: 8.9 (see https://developer.nvidia.com/cuda-gpus#compute)
            torch.cuda.1.name: NVIDIA L4
            torch.cuda.1.free: 21.8 GB
            torch.cuda.1.total: 22.0 GB
            torch.cuda.1.capability: 8.9 (see https://developer.nvidia.com/cuda-gpus#compute)
            torch.cuda.2.name: NVIDIA L4
            torch.cuda.2.free: 21.8 GB
            torch.cuda.2.total: 22.0 GB
            torch.cuda.2.capability: 8.9 (see https://developer.nvidia.com/cuda-gpus#compute)
            torch.cuda.3.name: NVIDIA L4
            torch.cuda.3.free: 21.8 GB
            torch.cuda.3.total: 22.0 GB
            torch.cuda.3.capability: 8.9 (see https://developer.nvidia.com/cuda-gpus#compute)
          
          llama_cpp_python:
            llama_cpp_python.version: 0.2.79
            llama_cpp_python.supports_gpu_offload: True
          

      Bug impact
      Without knowing what the error is, the user can't fix it properly.

      Known workaround
      No known workaround.

      Additional context

      [root@bastion redhataccelerators]# pwd
      /root/.local/share/instructlab/taxonomy/knowledge/technology/redhataccelerators
      [root@bastion redhataccelerators]# cat qna.yaml 
      version: 3
      domain: technology
      created_by: AlexonOliveiraRH
      seed_examples:
        - context: |
            The Red Hat Accelerators program is a global enterprise customer community of Red Hat technology experts and
            enthusiasts who willingly share their knowledge and expertise with peers in the industry, their community,
            and with Red Hat.
          questions_and_answers:
            - question: What kind of community is the Red Hat Accelerators?
              answer: It is a global enterprise customer community.
            - question: What kind of professional is part of the Red Hat Accelerators?
              answer: Red Hat technology experts and enthusiasts.
            - question: What kind of contributions do Accelerators community members make?
              answer: |
                They willingly share their knowledge and expertise with peers in the industry, their community, and with
                Red Hat.
        - context: |
            Members engage and contribute in the following ways:
            - Be involved: Network with industry peers who share your passion and interests.
            - Get inside: Gain exclusive access to product teams, executives and business leaders as well as upcoming
              releases and strategic direction.
            - Share knowledge: Teach others what you know from your experiences.
            - Join the conversation: Participate and contribute in technical discussions, product feedback sessions and share
              your insights.
          questions_and_answers:
            - question: What is expected of Accelerators community members in terms of engagement?
              answer: Be involved, get inside, share knowledge and join the conversation.
            - question: How can the members of Accelerators community join the conversation?
              answer: Participate and contribute in technical discussions, product feedback sessions and share your insights.
            - question: How can the members of Accelerators community share knowledge?
              answer: Teach others what you know from your experiences.
        - context: |
            One of the many benefits of being a member of the program is officially identifying yourself as a Red Hat
            Accelerator. A digital badge lets others know you are a part of our exclusive community group. Red Hat
            Accelerator badges may be used on personal websites, blogs, email signatures and social media networks.
            The details on proper usage of the digital badges will be provided to Red Hat Accelerators after being accepted
            into the program.
            Also, as a Red Hat Accelerator, you'll bolster your Red Hat technology knowledge, skills, and profile by having
            access to the following:
            - Peer to peer networking: Members join a unique community of Red Hat enterprise customers just like you,
            providing the opportunity to network, learn, and share your passion and interests.
            - Member perks: Members gain special privileges, including early access to products and technologies, cool swag
            merchandise, and VIP treatment at sponsored events.
            - Speak to Red Hat: You'll get direct access to Red Hat experts, product team members, and materials, allowing
            you to share your experience and provide feedback to influence product development and feature/function
            improvements.
            - Be recognized: Gain validation of your expertise and get the recognition you deserve by helping to build your
            public profile and persona.
            - Share your expertise: You have a unique set of experiences and expertise with Red Hat products. Share,
            contribute, and teach others in the community during technical discussions, group meetings, and events.
          questions_and_answers:
            - question: What is one of the many benefits of being a member of the Accelerators program?
              answer: |
                It is officially identifying yourself as a Red Hat Accelerator. A digital badge lets others know you are a
                part of our exclusive community group
            - question: What are five extended benefits of being part of the Accelerators community?
              answer: Peer to peer networking, member perks, speak to Red Hat, be recognized and share your expertise.
            - question: What are some of the perks for members of the Accelerators community?
              answer: |
                Members gain special privileges, including early access to products and technologies, cool swag
              merchandise, and VIP treatment at sponsored events.
        - context: |
            We want to hear from you, understand your unique IT needs, and build that long-term partnership your business
            relies on. This is your opportunity to be part of our unique enterprise customer community-sharing your feedback,
            insight, and experiences while networking with like-minded industry experts and gaining new professional skills
            along the way.
            - Feedback matters: Accelerator members provide trusted feedback, from real-world experience. We need your input
            to help us improve.
            - Spread the word: Members are encouraged to speak publicly and share their experience, knowledge, and solutions
            using Red Hat technologies.
            - Product validation: Your unique membership periodically provides early access to new products and features,
            before they are released, giving you the opportunity to see, demo, and validate Red Hat technologies for product
            teams.
            - Understanding your needs: Your experiences help us understand your unique IT requirements. These help us
            develop products you need to solve your most difficult business challenges.
          questions_and_answers:
            - question: What is the Accelerators community interested in hearing?
              answer: |
                Wants to hear from you, understand your unique IT needs, and build that long-term partnership your business
                relies on.
            - question: What are the four ways the Accelerators community uses to listen to its members?
              answer: |
                Feedback matters, spread the world, product validation, and understanding your needs.
            - question: What type of product validation do Accelerators community members have access to?
              answer: |
                Your unique membership periodically provides early access to new products and features, before they are
                released, giving you the opportunity to see, demo, and validate Red Hat technologies for product teams.
        - context: |
            The program is led by Andi Fisher, Lili Mihaila and Kat Ho, having Alexon Oliveira, Jason Beard, Jason Breitweg,
            Joshua Loscar, Kent Perrier, Matthew Sweikert, Morgan Peterman, Rick Greene and Uco Mesdag as fellow
            facilitators. The facilitators also take the lead in presentations called "TAM Talk" and "Lightening Talk",
            which are divided into three different pillars, currently Ansible, OpenShift and Platform, in addition to
            helping with the moderation and collaboration of the community's different internal communication channels.
            The program is made up of customers and partners around the world who share a desire to advocate for Red Hat.
          questions_and_answers:
            - question: Who leads the Red Hat Accelerators program?
              answer: The program is led by Andi Fisher, Lili Mihaila and Kat Ho.
            - question: Who helps by facilitating the Red Hat Accelerators program?
              answer: |
                Alexon Oliveira, Jason Beard, Jason Breitweg, Joshua Loscar, Kent Perrier, Matthew Sweikert, Morgan Peterman,
                Rick Greene and Uco Mesdag.
            - question: What are the three technology pillars of the Accelerators program?
              answer: Ansible, OpenShift and Platform.
      document_outline: |
        Red Hat Accelerators' Description and Facts
      document:
        repo: https://github.com/AlexonOliveiraRH/instructlab-examples.git
        commit: 2ba0d11f63b0b2d06a4b24d38efa069260921e54
        patterns:
          - redhataccelerators/facts.md
      [root@bastion redhataccelerators]# ilab taxonomy diff
      knowledge/technology/redhataccelerators/qna.yaml
      Reading taxonomy failed with the following error: 
      [root@bastion redhataccelerators]# 
      

              rh-ee-bmurdock Bill Murdock
              rhn-support-alolivei Alexon Ferreira de Oliveira
              Kamesh Akella Kamesh Akella
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                Created:
                Updated:
                Resolved: