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README.md: Add some debugging hints
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@@ -152,6 +152,47 @@ clusters, as well as an option to run a local notebook directly on the jupyterhu
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```
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## Debugging batchspawner
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Sometimes it can be hard to debug batchspawner, but it's not really
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once you know how the pieces interact. Check the following places for
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error messages:
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* Check the JupyterHub logs for errors.
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* Check the JupyterHub logs for the batch script that got submitted
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and the command used to submit it. Are these correct? (Note that
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there are submission environment variables too, which aren't
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displayed.)
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* At this point, it's a matter of checking the batch system. Is the
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job ever scheduled? Does it run? Does it succeed? Check the batch
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system status and output of the job. The most comon failure
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patterns are a) job never starting due to bad scheduler options, b)
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job waiting in the queue beyond the `start_timeout`, causing
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JupyterHub to kill the job.
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* At this point the job starts. Does it fail immediately, or before
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Jupyter starts? Check the scheduler output files (stdout/stderr of
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the job), wherever it is stored. To debug the job script, you can
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add debugging into the batch script, such as an `env` or `set
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-x`.
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* At this point Jupyter itself starts - check its error messages. Is
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it starting with the right options? Can it communicate with the
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hub? At this point there usually isn't anything
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batchspawner-specific, with the one exception below. The error log
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would be in the batch script output (same file as above). There may
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also be clues in the JupyterHub logfile.
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Common problems:
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* Did you `import batchspawner` in the `jupyterhub_config.py` file?
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This is needed in order to activate the batchspawer API in
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JupyterHub.
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## Changelog
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### dev (requires minimum JupyterHub 0.7.2 and Python 3.4)

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