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Why model.fit_generator in keras is taking so much time even before picking the data?

1

$begingroup$

In Keras, I am training a model as in below

model.fit_generator(data_generator(),
 samples_per_epoch = count,
 validation_data=(x_val, y_val),
 nb_epoch=50,
 callbacks=getCallBacks(),
 verbose=1)

In data_generator function, I am printing few debugging statements.

On running above fit_generator function, it takes really long time to print what I had put in the data_generator().

Is there any series of steps which keras takes before picking out the data batch for the training which makes the process so slow or there is some other caveat?

keras

share|improve this question

asked Jun 19 '18 at 9:23

Divyanshu ShekharDivyanshu Shekhar

184113

$endgroup$

bumped to the homepage by Community♦ 22 mins ago

This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Did you put a debug statement at the top of your fit_generator function?
  $endgroup$
  – Imran
  Dec 17 '18 at 21:48
  

  
  
  
  

add a comment | 

1

$begingroup$

In Keras, I am training a model as in below

model.fit_generator(data_generator(),
 samples_per_epoch = count,
 validation_data=(x_val, y_val),
 nb_epoch=50,
 callbacks=getCallBacks(),
 verbose=1)

In data_generator function, I am printing few debugging statements.

On running above fit_generator function, it takes really long time to print what I had put in the data_generator().

Is there any series of steps which keras takes before picking out the data batch for the training which makes the process so slow or there is some other caveat?

keras

share|improve this question

asked Jun 19 '18 at 9:23

Divyanshu ShekharDivyanshu Shekhar

184113

$endgroup$

bumped to the homepage by Community♦ 22 mins ago

This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Did you put a debug statement at the top of your fit_generator function?
  $endgroup$
  – Imran
  Dec 17 '18 at 21:48
  

  
  
  
  

add a comment | 

$begingroup$

In Keras, I am training a model as in below

model.fit_generator(data_generator(),
 samples_per_epoch = count,
 validation_data=(x_val, y_val),
 nb_epoch=50,
 callbacks=getCallBacks(),
 verbose=1)

In data_generator function, I am printing few debugging statements.

On running above fit_generator function, it takes really long time to print what I had put in the data_generator().

Is there any series of steps which keras takes before picking out the data batch for the training which makes the process so slow or there is some other caveat?

keras

share|improve this question

asked Jun 19 '18 at 9:23

Divyanshu ShekharDivyanshu Shekhar

184113

$endgroup$

model.fit_generator(data_generator(),
 samples_per_epoch = count,
 validation_data=(x_val, y_val),
 nb_epoch=50,
 callbacks=getCallBacks(),
 verbose=1)

share|improve this question

asked Jun 19 '18 at 9:23

Divyanshu ShekharDivyanshu Shekhar

184113

share|improve this question

asked Jun 19 '18 at 9:23

Divyanshu ShekharDivyanshu Shekhar

184113

asked Jun 19 '18 at 9:23

Divyanshu ShekharDivyanshu Shekhar

184113

asked Jun 19 '18 at 9:23

Divyanshu ShekharDivyanshu Shekhar

184113

1 Answer
1

active

oldest

votes

0

$begingroup$

I have noticed this when training on one or more GPUs. I think it is due to Tensorflow having to acquire the resources (in the background it blocks the entire GPU memory). Perhaps some amount of data e.g. the first batch is copied over to the GPU.

I didn't find a way to reduce the waiting time dramatically, but you could try the tensorflow options to allocate less memory and allow memory growth as required. Check out the official docs and this issue for the Keras version.

Here is the latest recommendation form that issue: setting Tensorflow options befor eimporting Keras.

-------------------------- set gpu using tf ---------------------------
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
------------------- start importing keras module ---------------------
import keras.backend.tensorflow_backend as K
import keras......

share|improve this answer

answered Jun 19 '18 at 10:16

n1k31t4n1k31t4

6,5512421

$endgroup$

add a comment | 

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0

$begingroup$

I have noticed this when training on one or more GPUs. I think it is due to Tensorflow having to acquire the resources (in the background it blocks the entire GPU memory). Perhaps some amount of data e.g. the first batch is copied over to the GPU.

I didn't find a way to reduce the waiting time dramatically, but you could try the tensorflow options to allocate less memory and allow memory growth as required. Check out the official docs and this issue for the Keras version.

Here is the latest recommendation form that issue: setting Tensorflow options befor eimporting Keras.

-------------------------- set gpu using tf ---------------------------
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
------------------- start importing keras module ---------------------
import keras.backend.tensorflow_backend as K
import keras......

share|improve this answer

answered Jun 19 '18 at 10:16

n1k31t4n1k31t4

6,5512421

$endgroup$

add a comment | 

0

$begingroup$

I have noticed this when training on one or more GPUs. I think it is due to Tensorflow having to acquire the resources (in the background it blocks the entire GPU memory). Perhaps some amount of data e.g. the first batch is copied over to the GPU.

I didn't find a way to reduce the waiting time dramatically, but you could try the tensorflow options to allocate less memory and allow memory growth as required. Check out the official docs and this issue for the Keras version.

Here is the latest recommendation form that issue: setting Tensorflow options befor eimporting Keras.

-------------------------- set gpu using tf ---------------------------
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
------------------- start importing keras module ---------------------
import keras.backend.tensorflow_backend as K
import keras......

share|improve this answer

answered Jun 19 '18 at 10:16

n1k31t4n1k31t4

6,5512421

$endgroup$

add a comment | 

$begingroup$

I have noticed this when training on one or more GPUs. I think it is due to Tensorflow having to acquire the resources (in the background it blocks the entire GPU memory). Perhaps some amount of data e.g. the first batch is copied over to the GPU.

I didn't find a way to reduce the waiting time dramatically, but you could try the tensorflow options to allocate less memory and allow memory growth as required. Check out the official docs and this issue for the Keras version.

Here is the latest recommendation form that issue: setting Tensorflow options befor eimporting Keras.

-------------------------- set gpu using tf ---------------------------
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
------------------- start importing keras module ---------------------
import keras.backend.tensorflow_backend as K
import keras......

share|improve this answer

answered Jun 19 '18 at 10:16

n1k31t4n1k31t4

6,5512421

$endgroup$

-------------------------- set gpu using tf ---------------------------
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
------------------- start importing keras module ---------------------
import keras.backend.tensorflow_backend as K
import keras......

share|improve this answer

answered Jun 19 '18 at 10:16

n1k31t4n1k31t4

6,5512421

answered Jun 19 '18 at 10:16

n1k31t4n1k31t4

6,5512421

answered Jun 19 '18 at 10:16

n1k31t4n1k31t4

6,5512421