08:43:20,274 DEBUG - tion.JupyterRuntimeServiceImpl - Getting session for VirtualFile: XXX.XX.XX.XXX:8000\Untitled1.ipynb: Old connection settings: null 08:43:20,274 DEBUG - tion.JupyterRuntimeServiceImpl - Start getting session for VirtualFile: XXX.XX.XX.XXX:8000\Untitled1.ipynb 08:43:19,407 TRACE - lectClickedCellEventDispatcher - Handled mouse click event in 51.3 microseconds 08:43:16,977 INFO - Set TRACE for the following categories: # For reference, this is what I'm getting from the moment I set the Log settings to record the TRACE, then do the test, to the moment I remove the TRACE option: Thing is, I don't think the TRACE will help you much. I am also experiencing the same issue as OP. Sorry once again, and looking forward to hearing back from you! Hopefully, we could include this fix in the nearest bug fix update. If you see the problem again, please go to the main menu Help | Show Log in Finder, zip the entire contents of the folder, and send to we see the logs, we will be able to provide you with an estimate on when this issue will be fixed. To keep it simple, please test the case when you’ve set up a tunnel and use localhost:8080. Once you’ve enabled the additional logging, please try again to reproduce the issue by restarting the IDE and trying to connect to the Jupyter server again. It means you have to remove this line from there as soon as you stop diagnosing the issue. Keep in mind, this line may dump the entire traffic (of the communication between the IDE and the Jupyter instance) to your DataSpell log directory. Please, go the main menu Help | Diagnostic Tools | Debug Log Settings and add the following line: #:trace Here’s what is necessary to do in order to diagnose the errors that you experienced. And we’re sincerely sorry that it didn’t work in your case.īecause we do want to fix the issues like that as soon as possible, we’d like to kindly ask for your cooperation. Basically, this is exactly what happened. While in most cases, the current integration works without major issues, there are a number of scenarios that we haven’t tested fully yet and thus may not work at all. Let me provide some context, and then also suggest what we can try right away to get this issue fixed.Īs it turns out, it’s not a trivial task to integrate Jupyter, JupyterLab, and JupyterHub into an IDE. Hi, I’m sorry to hear about it didn’t work for you as well as about your time waste. I feel like I just wasted several hours trying to get this to work. I get that this has still not had an official, full release yet but boy have I just been so turned off by all of this. Even the Database connections have SSH tunnel features (although I assume that's because it's just copied from other Jetbrains products) In Pycharm the Jupyter configurations are identical, despite Dataspell being Jupyter-centric. No option to use an SSH configuration or anything. The only thing that even mentions a remote connection in there is where you can enter a URL, nothing else. I've looked at the jupyter configurations and they're so minimal. Maybe the most infuriating part of all this is that I was able to successfully add the VM to Dataspell's SSH configurations, and even in the deployment settings was able to successfully create an SFTP connection so that when I create something on my local connection it copies it within my VM's jupyter directory. I have created SSH tunnels, so my localhost:8080 works perfectly on my browser but when I plug that exact same url into Dataspell it keeps telling me it "can't reach" the jupyter server behind the URL. I have used proxies, but those don't work presumably because of google auth issues. I have been trying everything and nothing has worked. However, their remote connections to Jupyter servers have been super disappointing.įor the life of me I cannot get it to work. I was so excited to hear about Dataspell having all the nice IDE features while also being Jupyter lab as well just being able to have access to my local and remote jupyter instances in one place. After they added the collaborative feature a few months ago, I started hosting an instance on a VM in the Google Cloud Platform. At my work we have a small team and we use Jupyter lab a lot every day.
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