uswgi - Unable to load configuration from from multiprocessing.semaphore_tracker
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Currently i'm deploying my Flask application to an Ubuntu server (AWS). When I try to start the uwsgi server and look with journalctl to the logs I notice a kind of warning/error.
Can I ignore it? I don't know how to solve it or where it came from. Stuck on this for 2 days right now. Who can help me?
Error:
*** Operational MODE: preforking ***
Jan 04 15:27:11 ip-172-31-39-12 uwsgi[21781]: unable to load configuration from from multiprocessing.semaphore_tracker import main;main(10)
python ubuntu nginx flask uwsgi
add a comment |
Currently i'm deploying my Flask application to an Ubuntu server (AWS). When I try to start the uwsgi server and look with journalctl to the logs I notice a kind of warning/error.
Can I ignore it? I don't know how to solve it or where it came from. Stuck on this for 2 days right now. Who can help me?
Error:
*** Operational MODE: preforking ***
Jan 04 15:27:11 ip-172-31-39-12 uwsgi[21781]: unable to load configuration from from multiprocessing.semaphore_tracker import main;main(10)
python ubuntu nginx flask uwsgi
I was wondering if you were ever able to find a solution to this, it's killing the performance of my application and so far I'm coming up empty.
– Sad Bones Malone
Feb 11 at 16:26
I still don't know how to fix this. I'm confused that I find very little information about it. Please let me know if you found something.
– Mike Evers
Feb 12 at 19:27
add a comment |
Currently i'm deploying my Flask application to an Ubuntu server (AWS). When I try to start the uwsgi server and look with journalctl to the logs I notice a kind of warning/error.
Can I ignore it? I don't know how to solve it or where it came from. Stuck on this for 2 days right now. Who can help me?
Error:
*** Operational MODE: preforking ***
Jan 04 15:27:11 ip-172-31-39-12 uwsgi[21781]: unable to load configuration from from multiprocessing.semaphore_tracker import main;main(10)
python ubuntu nginx flask uwsgi
Currently i'm deploying my Flask application to an Ubuntu server (AWS). When I try to start the uwsgi server and look with journalctl to the logs I notice a kind of warning/error.
Can I ignore it? I don't know how to solve it or where it came from. Stuck on this for 2 days right now. Who can help me?
Error:
*** Operational MODE: preforking ***
Jan 04 15:27:11 ip-172-31-39-12 uwsgi[21781]: unable to load configuration from from multiprocessing.semaphore_tracker import main;main(10)
python ubuntu nginx flask uwsgi
python ubuntu nginx flask uwsgi
asked Jan 4 at 15:40
Mike EversMike Evers
6519
6519
I was wondering if you were ever able to find a solution to this, it's killing the performance of my application and so far I'm coming up empty.
– Sad Bones Malone
Feb 11 at 16:26
I still don't know how to fix this. I'm confused that I find very little information about it. Please let me know if you found something.
– Mike Evers
Feb 12 at 19:27
add a comment |
I was wondering if you were ever able to find a solution to this, it's killing the performance of my application and so far I'm coming up empty.
– Sad Bones Malone
Feb 11 at 16:26
I still don't know how to fix this. I'm confused that I find very little information about it. Please let me know if you found something.
– Mike Evers
Feb 12 at 19:27
I was wondering if you were ever able to find a solution to this, it's killing the performance of my application and so far I'm coming up empty.
– Sad Bones Malone
Feb 11 at 16:26
I was wondering if you were ever able to find a solution to this, it's killing the performance of my application and so far I'm coming up empty.
– Sad Bones Malone
Feb 11 at 16:26
I still don't know how to fix this. I'm confused that I find very little information about it. Please let me know if you found something.
– Mike Evers
Feb 12 at 19:27
I still don't know how to fix this. I'm confused that I find very little information about it. Please let me know if you found something.
– Mike Evers
Feb 12 at 19:27
add a comment |
3 Answers
3
active
oldest
votes
You are ettempting to spawn subprocess(es) from inside your application (or from a lib you are using). According to this, an additional co-process is being also spawned - the semaphore tracker, responsible for returning back to system all named semaphores your subprocesses create. This is kind of important task, because if the named semaphore is leaked (not deleted), the associated system resource is occupied until next reboot.
The system has limited number of those resources, because they are located in the shared memory. You can ignore this if you are sure, that the number of named semaphores used by your app is not significant.
Notice that every type of lock defined in the multiprocessing module is a named semaphore under the hood. Moreover, every instance of multiprocessing.Queue, Barier, etc. instantiate their own locks.
If for instance you are spawning many processes (workers) and each of them is instantiating multiprocessing.Lock or multiprocessing.RLock, the number of leaked (not deleted) named semaphores may be significant, exhausting the limit quickly, causing your app or others to run out of resources.
Here is a link to an explanation of those issues: https://docs.python.org/3/library/multiprocessing.html?highlight=semaphore%20tracker#contexts-and-start-methods
Can you give me a pointer on how to fix it? How do I make sure the semaphores are deleted?
– Mike Evers
Jan 23 at 9:44
Any update on this? I have the same problem :-)
– SmCaterpillar
Feb 18 at 15:43
add a comment |
Hey I've been struggling with the same problem, and while I don't know how to actually stop the specific semaphore warning from popping up changing some of my uWSGI options has helped ameliorate the problem.
My .ini config file is below:
[uwsgi]
module = wsgi:app
master = true
processes = 16
socket = api.sock
chmod-socket = 660
vacuum = true
harakiri = 30
die-on-term = true
max-requests = 3
The additions I made are the "harakiri" and "max-request" options. The harakiri option means if a request takes longer than 30 seconds the worker will recycle itself, the max request option means after three requests the worker will recycle itself. It seems to be working, so my theory is that while the semaphores may be untracked they are tied in some way to the worker, and recycling them regularly improves performance.
This is a "dumb fight" of a memory leak and I wish I had a more elegant solution, but has been working for me the past few days. Good luck!
I still get the warning when I use this config and restart my service.
– Mike Evers
Feb 14 at 10:51
As I noted it doesn't get rid of the warning, but it should improve the performance of your application.
– Sad Bones Malone
Feb 14 at 18:35
add a comment |
In my case this error was due to using uWSGI 2.0.17.1 with Flask 1.0.2 and scikit-learn 0.20.0.
Internally, scikit-learn imports joblib which, at import time, tries to spawn a semaphore tracking process (sklearn/externals/joblib/_multiprocessing_helpers.py).
The semaphore tracking process is spawned by generating a command with the name of the current executable and appending "-c 'from multiprocessing.semaphore_tracker import main;main(fd)".
The name of the current executable is expected to be 'python' but this is not so when using uWSGI. The resulting command is "/usr/local/bin/uwsgi -c 'from multiprocessing.semaphore_tracker import main;main(fd)" which fails and outputs the above error message.
A workaround, as documented here is to set the environment variable JOBLIB_MULTIPROCESSING=0.
Note that the only consequence of this, in my situation, was to generate a defunct uWSGI process that was eventually cleaned up.
Is there a risk of net applying the workaround?
– Mike Evers
Mar 28 at 20:31
AFAIK the only consequence is that it will spawn a uWSGI process that will directly become defunct and be reaped almost immediately.
– Jack Johnson
Apr 2 at 7:46
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
You are ettempting to spawn subprocess(es) from inside your application (or from a lib you are using). According to this, an additional co-process is being also spawned - the semaphore tracker, responsible for returning back to system all named semaphores your subprocesses create. This is kind of important task, because if the named semaphore is leaked (not deleted), the associated system resource is occupied until next reboot.
The system has limited number of those resources, because they are located in the shared memory. You can ignore this if you are sure, that the number of named semaphores used by your app is not significant.
Notice that every type of lock defined in the multiprocessing module is a named semaphore under the hood. Moreover, every instance of multiprocessing.Queue, Barier, etc. instantiate their own locks.
If for instance you are spawning many processes (workers) and each of them is instantiating multiprocessing.Lock or multiprocessing.RLock, the number of leaked (not deleted) named semaphores may be significant, exhausting the limit quickly, causing your app or others to run out of resources.
Here is a link to an explanation of those issues: https://docs.python.org/3/library/multiprocessing.html?highlight=semaphore%20tracker#contexts-and-start-methods
Can you give me a pointer on how to fix it? How do I make sure the semaphores are deleted?
– Mike Evers
Jan 23 at 9:44
Any update on this? I have the same problem :-)
– SmCaterpillar
Feb 18 at 15:43
add a comment |
You are ettempting to spawn subprocess(es) from inside your application (or from a lib you are using). According to this, an additional co-process is being also spawned - the semaphore tracker, responsible for returning back to system all named semaphores your subprocesses create. This is kind of important task, because if the named semaphore is leaked (not deleted), the associated system resource is occupied until next reboot.
The system has limited number of those resources, because they are located in the shared memory. You can ignore this if you are sure, that the number of named semaphores used by your app is not significant.
Notice that every type of lock defined in the multiprocessing module is a named semaphore under the hood. Moreover, every instance of multiprocessing.Queue, Barier, etc. instantiate their own locks.
If for instance you are spawning many processes (workers) and each of them is instantiating multiprocessing.Lock or multiprocessing.RLock, the number of leaked (not deleted) named semaphores may be significant, exhausting the limit quickly, causing your app or others to run out of resources.
Here is a link to an explanation of those issues: https://docs.python.org/3/library/multiprocessing.html?highlight=semaphore%20tracker#contexts-and-start-methods
Can you give me a pointer on how to fix it? How do I make sure the semaphores are deleted?
– Mike Evers
Jan 23 at 9:44
Any update on this? I have the same problem :-)
– SmCaterpillar
Feb 18 at 15:43
add a comment |
You are ettempting to spawn subprocess(es) from inside your application (or from a lib you are using). According to this, an additional co-process is being also spawned - the semaphore tracker, responsible for returning back to system all named semaphores your subprocesses create. This is kind of important task, because if the named semaphore is leaked (not deleted), the associated system resource is occupied until next reboot.
The system has limited number of those resources, because they are located in the shared memory. You can ignore this if you are sure, that the number of named semaphores used by your app is not significant.
Notice that every type of lock defined in the multiprocessing module is a named semaphore under the hood. Moreover, every instance of multiprocessing.Queue, Barier, etc. instantiate their own locks.
If for instance you are spawning many processes (workers) and each of them is instantiating multiprocessing.Lock or multiprocessing.RLock, the number of leaked (not deleted) named semaphores may be significant, exhausting the limit quickly, causing your app or others to run out of resources.
Here is a link to an explanation of those issues: https://docs.python.org/3/library/multiprocessing.html?highlight=semaphore%20tracker#contexts-and-start-methods
You are ettempting to spawn subprocess(es) from inside your application (or from a lib you are using). According to this, an additional co-process is being also spawned - the semaphore tracker, responsible for returning back to system all named semaphores your subprocesses create. This is kind of important task, because if the named semaphore is leaked (not deleted), the associated system resource is occupied until next reboot.
The system has limited number of those resources, because they are located in the shared memory. You can ignore this if you are sure, that the number of named semaphores used by your app is not significant.
Notice that every type of lock defined in the multiprocessing module is a named semaphore under the hood. Moreover, every instance of multiprocessing.Queue, Barier, etc. instantiate their own locks.
If for instance you are spawning many processes (workers) and each of them is instantiating multiprocessing.Lock or multiprocessing.RLock, the number of leaked (not deleted) named semaphores may be significant, exhausting the limit quickly, causing your app or others to run out of resources.
Here is a link to an explanation of those issues: https://docs.python.org/3/library/multiprocessing.html?highlight=semaphore%20tracker#contexts-and-start-methods
answered Jan 18 at 10:27
rafał grabierafał grabie
213
213
Can you give me a pointer on how to fix it? How do I make sure the semaphores are deleted?
– Mike Evers
Jan 23 at 9:44
Any update on this? I have the same problem :-)
– SmCaterpillar
Feb 18 at 15:43
add a comment |
Can you give me a pointer on how to fix it? How do I make sure the semaphores are deleted?
– Mike Evers
Jan 23 at 9:44
Any update on this? I have the same problem :-)
– SmCaterpillar
Feb 18 at 15:43
Can you give me a pointer on how to fix it? How do I make sure the semaphores are deleted?
– Mike Evers
Jan 23 at 9:44
Can you give me a pointer on how to fix it? How do I make sure the semaphores are deleted?
– Mike Evers
Jan 23 at 9:44
Any update on this? I have the same problem :-)
– SmCaterpillar
Feb 18 at 15:43
Any update on this? I have the same problem :-)
– SmCaterpillar
Feb 18 at 15:43
add a comment |
Hey I've been struggling with the same problem, and while I don't know how to actually stop the specific semaphore warning from popping up changing some of my uWSGI options has helped ameliorate the problem.
My .ini config file is below:
[uwsgi]
module = wsgi:app
master = true
processes = 16
socket = api.sock
chmod-socket = 660
vacuum = true
harakiri = 30
die-on-term = true
max-requests = 3
The additions I made are the "harakiri" and "max-request" options. The harakiri option means if a request takes longer than 30 seconds the worker will recycle itself, the max request option means after three requests the worker will recycle itself. It seems to be working, so my theory is that while the semaphores may be untracked they are tied in some way to the worker, and recycling them regularly improves performance.
This is a "dumb fight" of a memory leak and I wish I had a more elegant solution, but has been working for me the past few days. Good luck!
I still get the warning when I use this config and restart my service.
– Mike Evers
Feb 14 at 10:51
As I noted it doesn't get rid of the warning, but it should improve the performance of your application.
– Sad Bones Malone
Feb 14 at 18:35
add a comment |
Hey I've been struggling with the same problem, and while I don't know how to actually stop the specific semaphore warning from popping up changing some of my uWSGI options has helped ameliorate the problem.
My .ini config file is below:
[uwsgi]
module = wsgi:app
master = true
processes = 16
socket = api.sock
chmod-socket = 660
vacuum = true
harakiri = 30
die-on-term = true
max-requests = 3
The additions I made are the "harakiri" and "max-request" options. The harakiri option means if a request takes longer than 30 seconds the worker will recycle itself, the max request option means after three requests the worker will recycle itself. It seems to be working, so my theory is that while the semaphores may be untracked they are tied in some way to the worker, and recycling them regularly improves performance.
This is a "dumb fight" of a memory leak and I wish I had a more elegant solution, but has been working for me the past few days. Good luck!
I still get the warning when I use this config and restart my service.
– Mike Evers
Feb 14 at 10:51
As I noted it doesn't get rid of the warning, but it should improve the performance of your application.
– Sad Bones Malone
Feb 14 at 18:35
add a comment |
Hey I've been struggling with the same problem, and while I don't know how to actually stop the specific semaphore warning from popping up changing some of my uWSGI options has helped ameliorate the problem.
My .ini config file is below:
[uwsgi]
module = wsgi:app
master = true
processes = 16
socket = api.sock
chmod-socket = 660
vacuum = true
harakiri = 30
die-on-term = true
max-requests = 3
The additions I made are the "harakiri" and "max-request" options. The harakiri option means if a request takes longer than 30 seconds the worker will recycle itself, the max request option means after three requests the worker will recycle itself. It seems to be working, so my theory is that while the semaphores may be untracked they are tied in some way to the worker, and recycling them regularly improves performance.
This is a "dumb fight" of a memory leak and I wish I had a more elegant solution, but has been working for me the past few days. Good luck!
Hey I've been struggling with the same problem, and while I don't know how to actually stop the specific semaphore warning from popping up changing some of my uWSGI options has helped ameliorate the problem.
My .ini config file is below:
[uwsgi]
module = wsgi:app
master = true
processes = 16
socket = api.sock
chmod-socket = 660
vacuum = true
harakiri = 30
die-on-term = true
max-requests = 3
The additions I made are the "harakiri" and "max-request" options. The harakiri option means if a request takes longer than 30 seconds the worker will recycle itself, the max request option means after three requests the worker will recycle itself. It seems to be working, so my theory is that while the semaphores may be untracked they are tied in some way to the worker, and recycling them regularly improves performance.
This is a "dumb fight" of a memory leak and I wish I had a more elegant solution, but has been working for me the past few days. Good luck!
answered Feb 13 at 17:39
Sad Bones MaloneSad Bones Malone
5719
5719
I still get the warning when I use this config and restart my service.
– Mike Evers
Feb 14 at 10:51
As I noted it doesn't get rid of the warning, but it should improve the performance of your application.
– Sad Bones Malone
Feb 14 at 18:35
add a comment |
I still get the warning when I use this config and restart my service.
– Mike Evers
Feb 14 at 10:51
As I noted it doesn't get rid of the warning, but it should improve the performance of your application.
– Sad Bones Malone
Feb 14 at 18:35
I still get the warning when I use this config and restart my service.
– Mike Evers
Feb 14 at 10:51
I still get the warning when I use this config and restart my service.
– Mike Evers
Feb 14 at 10:51
As I noted it doesn't get rid of the warning, but it should improve the performance of your application.
– Sad Bones Malone
Feb 14 at 18:35
As I noted it doesn't get rid of the warning, but it should improve the performance of your application.
– Sad Bones Malone
Feb 14 at 18:35
add a comment |
In my case this error was due to using uWSGI 2.0.17.1 with Flask 1.0.2 and scikit-learn 0.20.0.
Internally, scikit-learn imports joblib which, at import time, tries to spawn a semaphore tracking process (sklearn/externals/joblib/_multiprocessing_helpers.py).
The semaphore tracking process is spawned by generating a command with the name of the current executable and appending "-c 'from multiprocessing.semaphore_tracker import main;main(fd)".
The name of the current executable is expected to be 'python' but this is not so when using uWSGI. The resulting command is "/usr/local/bin/uwsgi -c 'from multiprocessing.semaphore_tracker import main;main(fd)" which fails and outputs the above error message.
A workaround, as documented here is to set the environment variable JOBLIB_MULTIPROCESSING=0.
Note that the only consequence of this, in my situation, was to generate a defunct uWSGI process that was eventually cleaned up.
Is there a risk of net applying the workaround?
– Mike Evers
Mar 28 at 20:31
AFAIK the only consequence is that it will spawn a uWSGI process that will directly become defunct and be reaped almost immediately.
– Jack Johnson
Apr 2 at 7:46
add a comment |
In my case this error was due to using uWSGI 2.0.17.1 with Flask 1.0.2 and scikit-learn 0.20.0.
Internally, scikit-learn imports joblib which, at import time, tries to spawn a semaphore tracking process (sklearn/externals/joblib/_multiprocessing_helpers.py).
The semaphore tracking process is spawned by generating a command with the name of the current executable and appending "-c 'from multiprocessing.semaphore_tracker import main;main(fd)".
The name of the current executable is expected to be 'python' but this is not so when using uWSGI. The resulting command is "/usr/local/bin/uwsgi -c 'from multiprocessing.semaphore_tracker import main;main(fd)" which fails and outputs the above error message.
A workaround, as documented here is to set the environment variable JOBLIB_MULTIPROCESSING=0.
Note that the only consequence of this, in my situation, was to generate a defunct uWSGI process that was eventually cleaned up.
Is there a risk of net applying the workaround?
– Mike Evers
Mar 28 at 20:31
AFAIK the only consequence is that it will spawn a uWSGI process that will directly become defunct and be reaped almost immediately.
– Jack Johnson
Apr 2 at 7:46
add a comment |
In my case this error was due to using uWSGI 2.0.17.1 with Flask 1.0.2 and scikit-learn 0.20.0.
Internally, scikit-learn imports joblib which, at import time, tries to spawn a semaphore tracking process (sklearn/externals/joblib/_multiprocessing_helpers.py).
The semaphore tracking process is spawned by generating a command with the name of the current executable and appending "-c 'from multiprocessing.semaphore_tracker import main;main(fd)".
The name of the current executable is expected to be 'python' but this is not so when using uWSGI. The resulting command is "/usr/local/bin/uwsgi -c 'from multiprocessing.semaphore_tracker import main;main(fd)" which fails and outputs the above error message.
A workaround, as documented here is to set the environment variable JOBLIB_MULTIPROCESSING=0.
Note that the only consequence of this, in my situation, was to generate a defunct uWSGI process that was eventually cleaned up.
In my case this error was due to using uWSGI 2.0.17.1 with Flask 1.0.2 and scikit-learn 0.20.0.
Internally, scikit-learn imports joblib which, at import time, tries to spawn a semaphore tracking process (sklearn/externals/joblib/_multiprocessing_helpers.py).
The semaphore tracking process is spawned by generating a command with the name of the current executable and appending "-c 'from multiprocessing.semaphore_tracker import main;main(fd)".
The name of the current executable is expected to be 'python' but this is not so when using uWSGI. The resulting command is "/usr/local/bin/uwsgi -c 'from multiprocessing.semaphore_tracker import main;main(fd)" which fails and outputs the above error message.
A workaround, as documented here is to set the environment variable JOBLIB_MULTIPROCESSING=0.
Note that the only consequence of this, in my situation, was to generate a defunct uWSGI process that was eventually cleaned up.
edited Mar 27 at 18:35
answered Mar 27 at 16:54
Jack JohnsonJack Johnson
11
11
Is there a risk of net applying the workaround?
– Mike Evers
Mar 28 at 20:31
AFAIK the only consequence is that it will spawn a uWSGI process that will directly become defunct and be reaped almost immediately.
– Jack Johnson
Apr 2 at 7:46
add a comment |
Is there a risk of net applying the workaround?
– Mike Evers
Mar 28 at 20:31
AFAIK the only consequence is that it will spawn a uWSGI process that will directly become defunct and be reaped almost immediately.
– Jack Johnson
Apr 2 at 7:46
Is there a risk of net applying the workaround?
– Mike Evers
Mar 28 at 20:31
Is there a risk of net applying the workaround?
– Mike Evers
Mar 28 at 20:31
AFAIK the only consequence is that it will spawn a uWSGI process that will directly become defunct and be reaped almost immediately.
– Jack Johnson
Apr 2 at 7:46
AFAIK the only consequence is that it will spawn a uWSGI process that will directly become defunct and be reaped almost immediately.
– Jack Johnson
Apr 2 at 7:46
add a comment |
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I was wondering if you were ever able to find a solution to this, it's killing the performance of my application and so far I'm coming up empty.
– Sad Bones Malone
Feb 11 at 16:26
I still don't know how to fix this. I'm confused that I find very little information about it. Please let me know if you found something.
– Mike Evers
Feb 12 at 19:27