Comments (6)
I don't know what more robust means in this case. What exactly do you want it to do?
from ipyparallel.
I guess what I was looking for was something that extended the Logger class from the logging package so I could get logging messages from engines with something like:
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()
def func(logger):
logger.debug("hostname: %s", socket.gethostname())
logger.warning("hostname: %s", socket.gethostname())
c = Client()
dv = c[:]
dv.apply_async(func,range(10),{'logger':logger})
Right now I just capture stdout from the engines in a similar manner to what's done in iopubwatcher.py. Perhaps that isn't the best method for logging, though.
from ipyparallel.
Did you manage to get something working with the logger class?
Ran into a similar problem and it works in multiprocessing, but not ipyparallel.
from ipyparallel.
@RexFuzzle I didn't get a logger class working. What I am doing now is basically running main from iopubwatcher.py with multiprocessing.Process and writing output to a file instead of stdout.
from ipyparallel.
I'm not going to pretend I understand what is going on here but in the [logging cookbook](git push --set-upstream origin logging.remastered) there is mention of a ZeroMQ ‘publish’ socket in conjunction with a ZeroMQ ‘subscribe’ socket. Maybe that's the the way to pass your engine produced log chatter back to the hub?
from ipyparallel.
Hi! I’m going through and cleaning up old/stale issues on this repo. I’m especially closing old “how to” or debugging questions to keep the Issues list to bug reports and feature requests. These can be directed to the Jupyter forum if still relevant.
The 'modern' Jupyter approach I'd use for logging special messages is to use displaypub with custom mimetypes, which you can then watch for like the iopub watcher:
from IPython.display import publish_display_data
publish_display_data({"text/x-my-log": "log-message"})
from ipyparallel.
Related Issues (20)
- No module named 'jupyter_server' HOT 2
- How to make it work with torch DDP HOT 4
- Transition from `CompositeError` to builtin `ExceptionGroup` HOT 1
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- Print in multiprocessing.Process crashing the engine HOT 7
- Windows ssh support by ipcluster HOT 39
- map_sync with pandas operation function does not finish. HOT 1
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- sync_imports not working as intended HOT 9
- ipyparallel and pymoo doesn't work HOT 2
- AsyncResult.join doesn't work
- AsyncResult.abort() call hangs if not all jobs can be stopped HOT 1
- Question: engines and databases HOT 1
- BroadcastView map Not Implemented HOT 3
- Cannot run ipythonparallel with openmpi HOT 7
- 60s timeout on get_connection_info() is not configurable HOT 1
- please release/tag/pypi the current version as it supports JupyterLab 4.x HOT 2
- SSHEngineLauncher does not work as expected HOT 2
- Outstanding task on client but hub says completed when using broadcast view
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from ipyparallel.