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More details about the timeout error
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(More details about the timeout error) |
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Most JupyterHub errors are caused by the underlying job scheduler which is either unresponsive or not able to find appropriate resources for your session. For example: | Most JupyterHub errors are caused by the underlying job scheduler which is either unresponsive or not able to find appropriate resources for your session. For example: | ||
* | |||
** Just like any interactive job on any cluster, a longer requested time can cause a longer wait time in queue. | <b>Spawn failed: Timeout</b> | ||
** There may be no available | [[File:JupyterHub Spawn failed Timeout.png|thumb|upright=1.1|JupyterHub - Spawn failed: Timeout]] | ||
* When starting a new session, JupyterHub automatically submits on your behalf a new [[Running_jobs#Interactive_jobs|interactive job]] to the cluster. If the job does not start within five minutes, a "Timeout" error message is raised and the session is cancelled. | |||
** Just like any interactive job on any cluster, a longer requested time can cause a longer wait time in the queue. Requesting a GPU or too many CPU cores can also cause a longer wait time. Make sure to request only the resources you need for your session. | |||
** If you already have another interactive job on the same cluster, your Jupyter session will be waiting along with other regular batch jobs in the queue. If possible, stop or cancel any other interactive job before using JupyterHub. | |||
** There may be just no resource available at the moment. Check the [https://status.alliancecan.ca/ status page] for any issue and try again later. | |||
= References = <!--T:7--> | = References = <!--T:7--> | ||
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