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Load the analysis-toolbox:
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[mandresm@albedo1:~]$ module load analysis-toolbox
[mandresm@albedo1:~]$ jupyter notebook --no-browser --ip=0.0.0.0
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[I 15:26:36.310 NotebookApp] Jupyter Notebook 6.5.3 is running at: |
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[I 15:26:36.310 NotebookApp] http://albedo1:8891/?token=asdasdads |
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[I 15:26:36.310 NotebookApp] or http://127.0.0.1:8891/?token=asdasdasd |
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[I 15:26:36.310 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). |
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[C 15:26:36.313 NotebookApp] |
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To access the notebook, open this file in a browser: |
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file:///albedo/home/mandresm/.local/share/jupyter/runtime/nbserver-3890270-open.html |
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Or copy and paste one of these URLs: |
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http://albedo1:8891/?token=asdasdaas |
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or http://127.0.0.1:8891/?token=asdasda |
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On the local machine, paste the URL including the albedo0 or albedo1 word into your browser, but replace albedo0 or albedo1 by by albedo0.dmawi.de or albedo1.dmawi.de respectively.
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Background: By default, Jupyer notebook uses /run/user/<uid> as default directory for small files like notebook_cookie_secret. If you log in by ssh, /run/user/<uid> is created and it is removed when you close your last login session on the computer. However, if you enter a node via Slurm sbatch, salloc, or srun, /run/user/<uid> is not available. XDG_RUNTIME_DIR sets a different path.
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mandresm@albedo1:~$ salloc --partition=gpu --gpus=1 -A computing.computing --time=00:30:00 |
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salloc: Pending job allocation 6526219 |
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salloc: job 6526219 queued and waiting for resources |
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salloc: job 6526219 has been allocated resources |
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salloc: Granted job allocation 6526219 |
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salloc: Waiting for resource configuration |
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salloc: Nodes gpu-001 are ready for job |
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mandresm@gpu-001:~$ export XDG_RUNTIME_DIR="/tmp/tmp_$SLURM_JOBID" |
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mandresm@gpu-001:~$ module load analysis-toolbox |
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mandresm@gpu-001:~$ jupyter notebook --no-browser --ip=0.0.0.0 |
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[I 15:37:11.953 NotebookApp] Serving notebooks from local directory: /albedo/home/mandresm |
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[I 15:37:11.953 NotebookApp] Jupyter Notebook 6.5.3 is running at: |
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[I 15:37:11.953 NotebookApp] http://gpu-001:8888/?token=123 |
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[I 15:37:11.953 NotebookApp] or http://127.0.0.1:8888/?token=123 |
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[I 15:37:11.953 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). |
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[C 15:37:11.958 NotebookApp] |
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To access the notebook, open this file in a browser: |
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file:///albedo/home/mandresm/.local/share/jupyter/runtime/nbserver-698858-open.html |
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Or copy and paste one of these URLs: |
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http://gpu-001:8888/?token=123 |
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or http://127.0.0.1:8888/?token=123 |
Now, we have to establish an SSH tunnel from your PC to the compute node, in this case gpu-001, to forward the Jupyter Notebook. Check the port number - usually 8888 for jupyter notebook, but it might differ. Open a new local terminal and execute:
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mandresm@blik0256:~$ ssh -NL localhost:8888:gpu-001:8888 mandresm@albedo1.dmawi.de |
If you have ssh automatically configured to connect to Albedo then the process will be idling at this point. If you are requested your password then enter your password and again there will be no output. You don't need to do anything here anymore, just leave this local terminal open.
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