[Komal Thareja]

Hi Ajay,

node.os_reboot() is recommended to be executed only if you are doing CPU pinning or NUMA tuning. This failed because your VM was already in shutoff state. If the intent is to just reboot the VM, please use sudo reboot via node.execute(). Also, what kind of workload is your application/experiment running? We are noticing some kernel level CPU locks on the host where your VM is running. We want to investigate if something from your experiment is triggering this. Could you please share more details about the experiment workload being executed on this VM?

Appreciate your help with this!

Thanks,

Komal

[Komal Thareja]

Hi Ajay,

Your VM was in a shutoff state, which I’ve now restored. Could you please share the notebook that outlines the type of workload you’re running on this VM? We’ve observed similar instances with your slices in the past, so having this information would help us identify the root cause of your VMs shutting down.

Thanks,
Komal

[Komal Thareja]

Hi Nirmala,

Could you please change the permissions of the key as indicated in the error message using the command below via terminal in your JH container? This shall fix the issue.

chmod 600 /home/fabric/work/fabric_config/Nirmala

Thanks,

Komal

[Komal Thareja]

Hi Garegin,

Thank you for sharing your observation. There was performance fix to improve how the interfaces are handled in the fablib. I suspect this may have introduced this issue. I will investigate this and post a fix once ready.

For now, please use the workaround as you suggested. Apologies for the inconvenience.

Thanks,

Komal

[Komal Thareja]

We have scheduled maintenance from July 28 to early August. This feature is planned to be rolled out during that period and should be available afterward.

Best regards,

Komal

[Komal Thareja]

Hi Suhib,

Issue has been resolved, and slice provisioning is now functioning correctly. Could you please try your slice again?

Thanks,

Komal

[Komal Thareja]

The Network AM has been restored, and slice provisioning is now functioning correctly.

Thanks,

Komal

[Komal Thareja]

Hi Jiri,

Thank you for bringing this to our attention. We’ve identified an issue with our Network AM and are actively investigating it. Apologies for the inconvenience.

Your slice is currently in the Dead state, and all associated resources have been released. You can toggle the view on the portal to hide Dead/Closing slices if needed.

Thanks,

Komal

[Komal Thareja]

Posting an update here:

The component responsible for pushing SSH keys to bastion host encountered an error due to a network event at RENCI. This component has been restored and keys should work now.

Geoff confirmed that his keys are working. @Suhib – Please try your slice/using the keys again and let us know if you still are running into this error.

Thank you Geoff and Suhib for bringing this to our attention! Appreciate it!

Best,

Komal

1 user thanked author for this post.

Suhib Atef Mustafa AlHendi

[Komal Thareja]

Hi,

For JH environment:

Could you please try running the notebook: jupyter-examples-rel1.8.*/configure_and_validate/configure_and_validate.ipynb and share the output observed?

Also, after this please try running the Hello Fabric notebook and share your observation.

For your local setup:

Could you please trying setting up the environment as suggested here: https://learn.fabric-testbed.net/knowledge-base/advanced-jupyter-hub/#running-fabric-containers-locally ?

Best,

Komal

[Komal Thareja]

Hi Fatih,

Could you please share your Slice ID and also what kind of NICs are you using in your slice?

Thanks,

Komal

[Komal Thareja]

Hi Ajay,

Thanks for reaching out. Could you please share any details about what may have caused the VM to crash? This information will help us better understand the root cause.

It appears that the PCI devices were detached from your VM during the crash. I’ve gone ahead and restored the VM — you should now be able to access it and use the GPUs as expected.

Please let me know if you continue to face any issues.

Best,
Komal Thareja

[Komal Thareja]

As a quick follow-up:

In addition to SmartNIC reservations, FABRIC also supports CPU pinning and NUMA tuning options, which can help further minimize resource contention on shared hosts. While these do not fully isolate you from other users on the physical host, they can significantly reduce interference for CPU and memory-bound workloads.

You can find working examples demonstrating how to request CPU pinning and NUMA-optimized resources in the FABRIC Jupyter notebook examples repository:

• Optimized iPerf3 Experiment with SmartNIC and CPU Pinning

• Optimized iPerf3 Experiment with CPU Pinning and NUMA Tuning (without SmartNIC)

These examples show how you can specify pinned CPUs and memory placement via FABlib when creating your slice.

Please feel free to reach out if you’d like any assistance setting this up.

Best,

Komal

[Komal Thareja]

Dear Fatih,

Thank you for reaching out.

At present, there is no way to prevent other users from having VMs on the same physical host unless you are able to reserve the entire host (when host doesn’t have other allocations). However, one option you may consider is requesting SmartNIC-based resources (such as CX6 or CX5). When you reserve a SmartNIC, the NIC is dedicated exclusively to your slice, ensuring that only your experiment’s traffic passes through that NIC. While this does not isolate CPU or memory resources on the host, it can minimize potential interference on the data plane network traffic.

Please let us know if you would like assistance with requesting such resources or if you have any further questions.

Best regards,
Komal Thareja

[Komal Thareja]

Jupyter Hub is back up and accessible. You should be able to use JH containers. GCP outage has been resolved.

Refer https://learn.fabric-testbed.net/forums/topic/out/ for more details.

Thanks,

Komal

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