Comments (3)
Sorry, let me edit that, that log was from the worker and not the master, much more info to come in a sec.
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Updated the original description and title
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The python issue above was becuase conda was using latest, which brings with it Python 3.9. So that was not the AMI's fault.
However, even when using conda with Python 3.8 you will still have issues since the AWS Amazon Linux 2 AMI does not have initctl
and instead uses systemctl
.
Also, you will need to make the boot volume greater than the 10GB default as the bootstrap action will finish, but Hive fill fail to install afterwards as you will run out of disk space. 20GB will suffice.
This bootstrap worked for me.
https://gist.github.com/Nogbit/f15e1c2be59bcc4ad122171b2e56cdeb
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Related Issues (20)
- Conda environment does not activate HOT 1
- Dask Scheduler host/port Not Written to Skein Key-Value Storage When YARN Application Restarts HOT 5
- Move default branch from "master" -> "main" HOT 1
- YarnCluster.shutdown() Won't Work on EMR, results in `concurrent.futures._base.CancelledError` HOT 1
- Verify that Read the Docs is building after master -> main HOT 7
- YarnCluster hangs HOT 11
- wait_for_workers got stuck when to create cluster but application failed on yarn HOT 3
- dask-yarn job fails with dumps_msgpack ImportError HOT 3
- register workers of scheduler are less than workers in dashborad HOT 1
- can't upload files HOT 2
- Application Failure When Submitting Dask-Yarn Model Inferencing Job Remotely
- FileNotFoundError: [Errno 2] No such file or directory: 'yarn' HOT 3
- Jupyter Notebook Cell Hangs after submitting job to remote EMR cluster
- distributed 2022.3.0 no more compatible with dask-yarn because of missing "status" attribute in YarnCluster HOT 7
- YarnCluster() does not initialize but runs indefinetly HOT 3
- AttributeError while running dask on amazon EMR. HOT 3
- .skein.sh: line 2: environment/bin/python: No such file or directory HOT 4
- Bootstrapping for 40min, when use the script. HOT 2
- Issue with deprecated format_bytes HOT 3
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