concurrent.futures with multinest
What is the best python based multinest package that optimizes for multi processing with concurrent.futures?
I've had issues getting multicast to use all of my CPUs with anything but multiprocessing.pool; but the python multinest operations seem to not be able to use that.
python-3.x multiprocessing bayesian concurrent.futures
asked Nov 13 '18 at 8:06
user3064438user3064438
133
add a comment |
What is the best python based multinest package that optimizes for multi processing with concurrent.futures?
I've had issues getting multicast to use all of my CPUs with anything but multiprocessing.pool; but the python multinest operations seem to not be able to use that.
python-3.x multiprocessing bayesian concurrent.futures
asked Nov 13 '18 at 8:06
user3064438user3064438
133
add a comment |
What is the best python based multinest package that optimizes for multi processing with concurrent.futures?
I've had issues getting multicast to use all of my CPUs with anything but multiprocessing.pool; but the python multinest operations seem to not be able to use that.
python-3.x multiprocessing bayesian concurrent.futures
asked Nov 13 '18 at 8:06
user3064438user3064438
133
What is the best python based multinest package that optimizes for multi processing with concurrent.futures?
I've had issues getting multicast to use all of my CPUs with anything but multiprocessing.pool; but the python multinest operations seem to not be able to use that.
python-3.x multiprocessing bayesian concurrent.futures
python-3.x multiprocessing bayesian concurrent.futures
asked Nov 13 '18 at 8:06
user3064438user3064438
133
asked Nov 13 '18 at 8:06
user3064438user3064438
133
asked Nov 13 '18 at 8:06
user3064438user3064438
133
asked Nov 13 '18 at 8:06
user3064438user3064438
133
asked Nov 13 '18 at 8:06
user3064438user3064438
133
133
add a comment |
add a comment |
1 Answer
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On the github issues section for dynesty (one of the two most common, pure-python MultiNest), we discussed this is as well
https://github.com/joshspeagle/dynesty/issues/100
There was not a very settled, final explanation, but the thought is that
(1) The cost function is not large enough to require all of the cores at once
(2) The bootstrap flag should be set to 0 to avoid bootstrapping; it's a trick implemented for speed that seems to be interfering.
I've used Nestle (github.com/kbarbary/nestle) and Dynesty (github.com/joshspeagle/dynesty); they both seem to have this problem no matter the complexity of the cost function.
I have had great success using PyMultiNest (github.com/JohannesBuchner/PyMultiNest); but it requires the fortran version of MultiNest (github.com/JohannesBuchner/MultiNest), which is very difficult to install correctly -- need to manually install OpenMPI. Both MultiNest and OpenMPI can have compiler issues depending on the OS, system, and configuration thereof.
I would suggest using PyMultiNest, except that it's so hard to install; Using Dynesty and Nestle are trivial; but they have had this issue with full parallelizations.
answered 15 hours ago
ExoWandererExoWanderer
13
add a comment |
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1 Answer
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1 Answer
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active
oldest
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active
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On the github issues section for dynesty (one of the two most common, pure-python MultiNest), we discussed this is as well
https://github.com/joshspeagle/dynesty/issues/100
There was not a very settled, final explanation, but the thought is that
(1) The cost function is not large enough to require all of the cores at once
(2) The bootstrap flag should be set to 0 to avoid bootstrapping; it's a trick implemented for speed that seems to be interfering.
I've used Nestle (github.com/kbarbary/nestle) and Dynesty (github.com/joshspeagle/dynesty); they both seem to have this problem no matter the complexity of the cost function.
I have had great success using PyMultiNest (github.com/JohannesBuchner/PyMultiNest); but it requires the fortran version of MultiNest (github.com/JohannesBuchner/MultiNest), which is very difficult to install correctly -- need to manually install OpenMPI. Both MultiNest and OpenMPI can have compiler issues depending on the OS, system, and configuration thereof.
I would suggest using PyMultiNest, except that it's so hard to install; Using Dynesty and Nestle are trivial; but they have had this issue with full parallelizations.
answered 15 hours ago
ExoWandererExoWanderer
13
add a comment |
On the github issues section for dynesty (one of the two most common, pure-python MultiNest), we discussed this is as well
https://github.com/joshspeagle/dynesty/issues/100
There was not a very settled, final explanation, but the thought is that
(1) The cost function is not large enough to require all of the cores at once
(2) The bootstrap flag should be set to 0 to avoid bootstrapping; it's a trick implemented for speed that seems to be interfering.
I've used Nestle (github.com/kbarbary/nestle) and Dynesty (github.com/joshspeagle/dynesty); they both seem to have this problem no matter the complexity of the cost function.
I have had great success using PyMultiNest (github.com/JohannesBuchner/PyMultiNest); but it requires the fortran version of MultiNest (github.com/JohannesBuchner/MultiNest), which is very difficult to install correctly -- need to manually install OpenMPI. Both MultiNest and OpenMPI can have compiler issues depending on the OS, system, and configuration thereof.
I would suggest using PyMultiNest, except that it's so hard to install; Using Dynesty and Nestle are trivial; but they have had this issue with full parallelizations.
answered 15 hours ago
ExoWandererExoWanderer
13
add a comment |
On the github issues section for dynesty (one of the two most common, pure-python MultiNest), we discussed this is as well
https://github.com/joshspeagle/dynesty/issues/100
There was not a very settled, final explanation, but the thought is that
(1) The cost function is not large enough to require all of the cores at once
(2) The bootstrap flag should be set to 0 to avoid bootstrapping; it's a trick implemented for speed that seems to be interfering.
I've used Nestle (github.com/kbarbary/nestle) and Dynesty (github.com/joshspeagle/dynesty); they both seem to have this problem no matter the complexity of the cost function.
I have had great success using PyMultiNest (github.com/JohannesBuchner/PyMultiNest); but it requires the fortran version of MultiNest (github.com/JohannesBuchner/MultiNest), which is very difficult to install correctly -- need to manually install OpenMPI. Both MultiNest and OpenMPI can have compiler issues depending on the OS, system, and configuration thereof.
I would suggest using PyMultiNest, except that it's so hard to install; Using Dynesty and Nestle are trivial; but they have had this issue with full parallelizations.
answered 15 hours ago
ExoWandererExoWanderer
13
On the github issues section for dynesty (one of the two most common, pure-python MultiNest), we discussed this is as well
https://github.com/joshspeagle/dynesty/issues/100
There was not a very settled, final explanation, but the thought is that
(1) The cost function is not large enough to require all of the cores at once
(2) The bootstrap flag should be set to 0 to avoid bootstrapping; it's a trick implemented for speed that seems to be interfering.
I've used Nestle (github.com/kbarbary/nestle) and Dynesty (github.com/joshspeagle/dynesty); they both seem to have this problem no matter the complexity of the cost function.
I have had great success using PyMultiNest (github.com/JohannesBuchner/PyMultiNest); but it requires the fortran version of MultiNest (github.com/JohannesBuchner/MultiNest), which is very difficult to install correctly -- need to manually install OpenMPI. Both MultiNest and OpenMPI can have compiler issues depending on the OS, system, and configuration thereof.
I would suggest using PyMultiNest, except that it's so hard to install; Using Dynesty and Nestle are trivial; but they have had this issue with full parallelizations.
answered 15 hours ago
ExoWandererExoWanderer
13
answered 15 hours ago
ExoWandererExoWanderer
13
answered 15 hours ago
ExoWandererExoWanderer
13
answered 15 hours ago
ExoWandererExoWanderer
13
13
add a comment |
add a comment |
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