Multiple keras models parallel - time efficient
$begingroup$
I am trying to load two different keras models in parallel. I tried to use the functional API model:
input1 = Input(inputShapeOfModel1)
input2 = Input(inputShapeOfModel2)
output1 = model1(input1)
output2 = model2(input2)
parallelModel = Model([input1,input2], [output1,output2])
This works but it does not run in parallel actually. Inference time is just the sum of each model's individual inference time.
My question is should this run concurrently?
I also tried to load them in different py files with gpu memory options. Still I haven't got parallelism (inference time is x1.5 for each model)
Is there any way to get inference time of both models as close to a single's model inference time?
Is the only solution to add a second gpu?
UPDATE: in different scripts they seem to be able to run in parallel, so there must be a way to efficiently run in python/keras as well.
keras tensorflow computer-vision gpu parallel
asked Sep 7 '18 at 4:19
Lara LarsenLara Larsen
13
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bumped to the homepage by Community♦ 6 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I am trying to load two different keras models in parallel. I tried to use the functional API model:
input1 = Input(inputShapeOfModel1)
input2 = Input(inputShapeOfModel2)
output1 = model1(input1)
output2 = model2(input2)
parallelModel = Model([input1,input2], [output1,output2])
This works but it does not run in parallel actually. Inference time is just the sum of each model's individual inference time.
My question is should this run concurrently?
I also tried to load them in different py files with gpu memory options. Still I haven't got parallelism (inference time is x1.5 for each model)
Is there any way to get inference time of both models as close to a single's model inference time?
Is the only solution to add a second gpu?
UPDATE: in different scripts they seem to be able to run in parallel, so there must be a way to efficiently run in python/keras as well.
keras tensorflow computer-vision gpu parallel
asked Sep 7 '18 at 4:19
Lara LarsenLara Larsen
13
$endgroup$
bumped to the homepage by Community♦ 6 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
1
$begingroup$
This might help: stackoverflow.com/questions/7207309/…
$endgroup$
– Erik van de Ven
Sep 24 '18 at 8:46
$begingroup$
have you got the answer ?
$endgroup$
– Lion
Mar 27 at 10:18
add a comment |
$begingroup$
I am trying to load two different keras models in parallel. I tried to use the functional API model:
input1 = Input(inputShapeOfModel1)
input2 = Input(inputShapeOfModel2)
output1 = model1(input1)
output2 = model2(input2)
parallelModel = Model([input1,input2], [output1,output2])
This works but it does not run in parallel actually. Inference time is just the sum of each model's individual inference time.
My question is should this run concurrently?
I also tried to load them in different py files with gpu memory options. Still I haven't got parallelism (inference time is x1.5 for each model)
Is there any way to get inference time of both models as close to a single's model inference time?
Is the only solution to add a second gpu?
UPDATE: in different scripts they seem to be able to run in parallel, so there must be a way to efficiently run in python/keras as well.
keras tensorflow computer-vision gpu parallel
asked Sep 7 '18 at 4:19
Lara LarsenLara Larsen
13
$endgroup$
I am trying to load two different keras models in parallel. I tried to use the functional API model:
input1 = Input(inputShapeOfModel1)
input2 = Input(inputShapeOfModel2)
output1 = model1(input1)
output2 = model2(input2)
parallelModel = Model([input1,input2], [output1,output2])
This works but it does not run in parallel actually. Inference time is just the sum of each model's individual inference time.
My question is should this run concurrently?
I also tried to load them in different py files with gpu memory options. Still I haven't got parallelism (inference time is x1.5 for each model)
Is there any way to get inference time of both models as close to a single's model inference time?
Is the only solution to add a second gpu?
UPDATE: in different scripts they seem to be able to run in parallel, so there must be a way to efficiently run in python/keras as well.
keras tensorflow computer-vision gpu parallel
keras tensorflow computer-vision gpu parallel
asked Sep 7 '18 at 4:19
Lara LarsenLara Larsen
13
asked Sep 7 '18 at 4:19
Lara LarsenLara Larsen
13
edited Sep 24 '18 at 5:57
Lara Larsen
asked Sep 7 '18 at 4:19
Lara LarsenLara Larsen
13
asked Sep 7 '18 at 4:19
Lara LarsenLara Larsen
13
asked Sep 7 '18 at 4:19
Lara LarsenLara Larsen
13
13
bumped to the homepage by Community♦ 6 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 6 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
1
$begingroup$
This might help: stackoverflow.com/questions/7207309/…
$endgroup$
– Erik van de Ven
Sep 24 '18 at 8:46
$begingroup$
have you got the answer ?
$endgroup$
– Lion
Mar 27 at 10:18
add a comment |
1
$begingroup$
This might help: stackoverflow.com/questions/7207309/…
$endgroup$
– Erik van de Ven
Sep 24 '18 at 8:46
$begingroup$
have you got the answer ?
$endgroup$
– Lion
Mar 27 at 10:18
1
1
$begingroup$
This might help: stackoverflow.com/questions/7207309/…
$endgroup$
– Erik van de Ven
Sep 24 '18 at 8:46
$begingroup$
This might help: stackoverflow.com/questions/7207309/…
$endgroup$
– Erik van de Ven
Sep 24 '18 at 8:46
$begingroup$
have you got the answer ?
$endgroup$
– Lion
Mar 27 at 10:18
$begingroup$
have you got the answer ?
$endgroup$
– Lion
Mar 27 at 10:18
add a comment |
1 Answer
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$begingroup$
As was suggested by Erik van de Ven, it sounds like running each model on a different process should provide the requested parallelism.
I guess you could either run the fit function for each model in a different process
Or you could even load them on different cpu cores:
with K.device('cpu0'):
input1 = Input(inputShapeOfModel1)
output1 = model1(input1)
with K.device('gpu0'):
input2 = Input(inputShapeOfModel2)
output2 = model2(input2)
model = Model([input1, input2], [output1, output2])
I haven't tried any of these though, so i'm not sure what would provide the best result
answered Nov 16 '18 at 20:37
Gal AvineriGal Avineri
567
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add a comment |
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$begingroup$
As was suggested by Erik van de Ven, it sounds like running each model on a different process should provide the requested parallelism.
I guess you could either run the fit function for each model in a different process
Or you could even load them on different cpu cores:
with K.device('cpu0'):
input1 = Input(inputShapeOfModel1)
output1 = model1(input1)
with K.device('gpu0'):
input2 = Input(inputShapeOfModel2)
output2 = model2(input2)
model = Model([input1, input2], [output1, output2])
I haven't tried any of these though, so i'm not sure what would provide the best result
answered Nov 16 '18 at 20:37
Gal AvineriGal Avineri
567
$endgroup$
add a comment |
$begingroup$
As was suggested by Erik van de Ven, it sounds like running each model on a different process should provide the requested parallelism.
I guess you could either run the fit function for each model in a different process
Or you could even load them on different cpu cores:
with K.device('cpu0'):
input1 = Input(inputShapeOfModel1)
output1 = model1(input1)
with K.device('gpu0'):
input2 = Input(inputShapeOfModel2)
output2 = model2(input2)
model = Model([input1, input2], [output1, output2])
I haven't tried any of these though, so i'm not sure what would provide the best result
answered Nov 16 '18 at 20:37
Gal AvineriGal Avineri
567
$endgroup$
add a comment |
$begingroup$
As was suggested by Erik van de Ven, it sounds like running each model on a different process should provide the requested parallelism.
I guess you could either run the fit function for each model in a different process
Or you could even load them on different cpu cores:
with K.device('cpu0'):
input1 = Input(inputShapeOfModel1)
output1 = model1(input1)
with K.device('gpu0'):
input2 = Input(inputShapeOfModel2)
output2 = model2(input2)
model = Model([input1, input2], [output1, output2])
I haven't tried any of these though, so i'm not sure what would provide the best result
answered Nov 16 '18 at 20:37
Gal AvineriGal Avineri
567
$endgroup$
As was suggested by Erik van de Ven, it sounds like running each model on a different process should provide the requested parallelism.
I guess you could either run the fit function for each model in a different process
Or you could even load them on different cpu cores:
with K.device('cpu0'):
input1 = Input(inputShapeOfModel1)
output1 = model1(input1)
with K.device('gpu0'):
input2 = Input(inputShapeOfModel2)
output2 = model2(input2)
model = Model([input1, input2], [output1, output2])
I haven't tried any of these though, so i'm not sure what would provide the best result
answered Nov 16 '18 at 20:37
Gal AvineriGal Avineri
567
answered Nov 16 '18 at 20:37
Gal AvineriGal Avineri
567
answered Nov 16 '18 at 20:37
Gal AvineriGal Avineri
567
answered Nov 16 '18 at 20:37
Gal AvineriGal Avineri
567
567
add a comment |
add a comment |
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1
$begingroup$
This might help: stackoverflow.com/questions/7207309/…
$endgroup$
– Erik van de Ven
Sep 24 '18 at 8:46
$begingroup$
have you got the answer ?
$endgroup$
– Lion
Mar 27 at 10:18