Internal server error while connecting to jupyter lab instance

Abderrahim MEHDAOUI 21 Reputation points

I have created an ML-compute instance from scratch, nothing particularly installed.
But when I try to connect to Jupiter Lab directly through the URL provided by the user interface I get the following error :

"error": {
"code": "ServiceError",
"severity": null,
"message": "InternalServerError",
"messageFormat": null,
"messageParameters": null,
"referenceCode": null,
"detailsUri": null,
"target": null,
"details": [],
"innerError": null,
"debugInfo": null
"correlation": {
"operation": "716efa38ccc70341b4b5b93bc16e441b",
"request": "170d9ccac55c9d42"
"environment": "westeurope",
"location": "westeurope",
"time": "2021-01-18T14:00:19.9517739+00:00",
"componentName": "notebook-instance-proxy"

I also tried to connect through SSH to this compute instance, I notice that there is a conflict about the version of the pillow package.
Then I've fixed the version conflict but the error still persists even after rebooting the machine.
After that, I've connected to the machine through SSH tunnelling and I could start the Jupiter lab instance (even though the error still persist when connecting through the Azure ML Interface).

Could you please investigate the connection issue through the user interface please?
Thanks in advance

Azure Machine Learning
Azure Machine Learning
An Azure machine learning service for building and deploying models.
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  1. Abderrahim MEHDAOUI 21 Reputation points

    @Ramr-msft , I was able to launch a Jupyter Lab instance on Google Chrome.
    But it's still not working on Safari, there must be a compatibility issue.
    My safari version is 14.0.2

    Ticket can be close.

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  2. Ramr-msft 17,621 Reputation points

    @Abderrahim MEHDAOUI Thanks for the details. Can you please try turning off "Deny 3rd party cookies" and it works great for me with this config. We have forwarded to the product team to check for the fix. You can also raise a user voice request here so the community can vote and provide their feedback, the product team then checks this feedback and implements the same in future releases.

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