Dask with HTCondor scheduler
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Background
I have an image analysis pipeline with parallelised steps. The pipeline is in python and the parallelisation is controlled by dask.distributed. The minimum processing set up has 1 scheduler + 3 workers with 15 processes each. In the first short step of the analysis I use 1 process/worker but all RAM of the node then in all other analysis steps all nodes and processes are used.
Issue
The admin will install HTCondor as a scheduler for the cluster.
Thought
In order order to have my code running on the new setup I was planning to use the approach showed in the dask manual for SGE because the cluster has a shared network files system.
# job1
# Start a dask-scheduler somewhere and write connection information to file
qsub -b y /path/to/dask-scheduler --scheduler-file /path/to/scheduler.json
# Job2
# Start 100 dask-worker processes in an array job pointing to the same file
qsub -b y -t 1-100 /path/to/dask-worker --scheduler-file /path/to/scheduler.json
# Job3
# Start a process with the python code where the client is started this way
client = Client(scheduler_file='/path/to/scheduler.json')
Question and advice
If I understood correctly with this approach I will start scheduler, workers and analysis as independent jobs (different HTCondor submit files). How can I make sure that the order of execution will be correct? Is there a way I can use the same processing approach I have being using before or will be more efficient to translate the code to work better with HTCondor?
Thanks for the help!
python parallel-processing dask condor
asked Nov 26 '18 at 20:31
s1mc0d3s1mc0d3
10410
add a comment |
Background
I have an image analysis pipeline with parallelised steps. The pipeline is in python and the parallelisation is controlled by dask.distributed. The minimum processing set up has 1 scheduler + 3 workers with 15 processes each. In the first short step of the analysis I use 1 process/worker but all RAM of the node then in all other analysis steps all nodes and processes are used.
Issue
The admin will install HTCondor as a scheduler for the cluster.
Thought
In order order to have my code running on the new setup I was planning to use the approach showed in the dask manual for SGE because the cluster has a shared network files system.
# job1
# Start a dask-scheduler somewhere and write connection information to file
qsub -b y /path/to/dask-scheduler --scheduler-file /path/to/scheduler.json
# Job2
# Start 100 dask-worker processes in an array job pointing to the same file
qsub -b y -t 1-100 /path/to/dask-worker --scheduler-file /path/to/scheduler.json
# Job3
# Start a process with the python code where the client is started this way
client = Client(scheduler_file='/path/to/scheduler.json')
Question and advice
If I understood correctly with this approach I will start scheduler, workers and analysis as independent jobs (different HTCondor submit files). How can I make sure that the order of execution will be correct? Is there a way I can use the same processing approach I have being using before or will be more efficient to translate the code to work better with HTCondor?
Thanks for the help!
python parallel-processing dask condor
asked Nov 26 '18 at 20:31
s1mc0d3s1mc0d3
10410
Are you aware of github.com/dask/dask-jobqueue ?
– mdurant
Nov 26 '18 at 20:50
Thanks!. Just saw it! but no HTCondor support yet. github.com/dask/dask-jobqueue/issues/100
– s1mc0d3
Nov 27 '18 at 7:14
Please comment on the thread expressing your interest. Perhaps you can help test. That would be better than trying to come up with a custom script of your own.
– mdurant
Nov 27 '18 at 14:33
I will add a comment in the opened issue!
– s1mc0d3
Dec 3 '18 at 12:17
add a comment |
Background
I have an image analysis pipeline with parallelised steps. The pipeline is in python and the parallelisation is controlled by dask.distributed. The minimum processing set up has 1 scheduler + 3 workers with 15 processes each. In the first short step of the analysis I use 1 process/worker but all RAM of the node then in all other analysis steps all nodes and processes are used.
Issue
The admin will install HTCondor as a scheduler for the cluster.
Thought
In order order to have my code running on the new setup I was planning to use the approach showed in the dask manual for SGE because the cluster has a shared network files system.
# job1
# Start a dask-scheduler somewhere and write connection information to file
qsub -b y /path/to/dask-scheduler --scheduler-file /path/to/scheduler.json
# Job2
# Start 100 dask-worker processes in an array job pointing to the same file
qsub -b y -t 1-100 /path/to/dask-worker --scheduler-file /path/to/scheduler.json
# Job3
# Start a process with the python code where the client is started this way
client = Client(scheduler_file='/path/to/scheduler.json')
Question and advice
If I understood correctly with this approach I will start scheduler, workers and analysis as independent jobs (different HTCondor submit files). How can I make sure that the order of execution will be correct? Is there a way I can use the same processing approach I have being using before or will be more efficient to translate the code to work better with HTCondor?
Thanks for the help!
python parallel-processing dask condor
asked Nov 26 '18 at 20:31
s1mc0d3s1mc0d3
10410
Background
I have an image analysis pipeline with parallelised steps. The pipeline is in python and the parallelisation is controlled by dask.distributed. The minimum processing set up has 1 scheduler + 3 workers with 15 processes each. In the first short step of the analysis I use 1 process/worker but all RAM of the node then in all other analysis steps all nodes and processes are used.
Issue
The admin will install HTCondor as a scheduler for the cluster.
Thought
In order order to have my code running on the new setup I was planning to use the approach showed in the dask manual for SGE because the cluster has a shared network files system.
# job1
# Start a dask-scheduler somewhere and write connection information to file
qsub -b y /path/to/dask-scheduler --scheduler-file /path/to/scheduler.json
# Job2
# Start 100 dask-worker processes in an array job pointing to the same file
qsub -b y -t 1-100 /path/to/dask-worker --scheduler-file /path/to/scheduler.json
# Job3
# Start a process with the python code where the client is started this way
client = Client(scheduler_file='/path/to/scheduler.json')
Question and advice
If I understood correctly with this approach I will start scheduler, workers and analysis as independent jobs (different HTCondor submit files). How can I make sure that the order of execution will be correct? Is there a way I can use the same processing approach I have being using before or will be more efficient to translate the code to work better with HTCondor?
Thanks for the help!
python parallel-processing dask condor
python parallel-processing dask condor
asked Nov 26 '18 at 20:31
s1mc0d3s1mc0d3
10410
asked Nov 26 '18 at 20:31
s1mc0d3s1mc0d3
10410
asked Nov 26 '18 at 20:31
s1mc0d3s1mc0d3
10410
asked Nov 26 '18 at 20:31
s1mc0d3s1mc0d3
10410
asked Nov 26 '18 at 20:31
s1mc0d3s1mc0d3
10410
10410
Are you aware of github.com/dask/dask-jobqueue ?
– mdurant
Nov 26 '18 at 20:50
Thanks!. Just saw it! but no HTCondor support yet. github.com/dask/dask-jobqueue/issues/100
– s1mc0d3
Nov 27 '18 at 7:14
Please comment on the thread expressing your interest. Perhaps you can help test. That would be better than trying to come up with a custom script of your own.
– mdurant
Nov 27 '18 at 14:33
I will add a comment in the opened issue!
– s1mc0d3
Dec 3 '18 at 12:17
add a comment |
Are you aware of github.com/dask/dask-jobqueue ?
– mdurant
Nov 26 '18 at 20:50
Thanks!. Just saw it! but no HTCondor support yet. github.com/dask/dask-jobqueue/issues/100
– s1mc0d3
Nov 27 '18 at 7:14
Please comment on the thread expressing your interest. Perhaps you can help test. That would be better than trying to come up with a custom script of your own.
– mdurant
Nov 27 '18 at 14:33
I will add a comment in the opened issue!
– s1mc0d3
Dec 3 '18 at 12:17
Are you aware of github.com/dask/dask-jobqueue ?
– mdurant
Nov 26 '18 at 20:50
Are you aware of github.com/dask/dask-jobqueue ?
– mdurant
Nov 26 '18 at 20:50
Thanks!. Just saw it! but no HTCondor support yet. github.com/dask/dask-jobqueue/issues/100
– s1mc0d3
Nov 27 '18 at 7:14
Thanks!. Just saw it! but no HTCondor support yet. github.com/dask/dask-jobqueue/issues/100
– s1mc0d3
Nov 27 '18 at 7:14
Please comment on the thread expressing your interest. Perhaps you can help test. That would be better than trying to come up with a custom script of your own.
– mdurant
Nov 27 '18 at 14:33
Please comment on the thread expressing your interest. Perhaps you can help test. That would be better than trying to come up with a custom script of your own.
– mdurant
Nov 27 '18 at 14:33
I will add a comment in the opened issue!
– s1mc0d3
Dec 3 '18 at 12:17
I will add a comment in the opened issue!
– s1mc0d3
Dec 3 '18 at 12:17
add a comment |
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Are you aware of github.com/dask/dask-jobqueue ?
– mdurant
Nov 26 '18 at 20:50
Thanks!. Just saw it! but no HTCondor support yet. github.com/dask/dask-jobqueue/issues/100
– s1mc0d3
Nov 27 '18 at 7:14
Please comment on the thread expressing your interest. Perhaps you can help test. That would be better than trying to come up with a custom script of your own.
– mdurant
Nov 27 '18 at 14:33
I will add a comment in the opened issue!
– s1mc0d3
Dec 3 '18 at 12:17