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View massif.549038
desc: (none)
cmd: ./target/release/awc-mem-leak
time_unit: i
#-----------
snapshot=0
#-----------
time=0
mem_heap_B=0
mem_heap_extra_B=0
mem_stacks_B=0
View gist:6ea036f8dbd5dba8052f92d10bf210ed
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@ajsyp
ajsyp / more-traceback.txt
Created Mar 9, 2017
More output for multi-GPU error on Kur
View more-traceback.txt
$ python parallel_bug.py
Using TensorFlow backend.
get_output_shape_for((None, 32))
self.name: dense_1
self.input_dim: 32
self.output_dim: 100
id(self): 140287380209560
get_output_shape_for((None, 32))
self.name: dense_1
self.input_dim: 32
@ajsyp
ajsyp / traceback.txt
Created Mar 9, 2017
Error with multi-GPU TensorFlow on Kur
View traceback.txt
$ python parallel_bug.py
Using TensorFlow backend.
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 32, 32) 0
____________________________________________________________________________________________________
timedistributed_1 (TimeDistribut (None, 32, 100) 3300 input_1[0][0]
____________________________________________________________________________________________________
lstm_1 (LSTM) (None, 32, 50) 30200 timedistributed_1[0][0]
@ajsyp
ajsyp / parallel_bug.py
Created Mar 9, 2017
Recreates the bug with multi-GPU TensorFlow in Kur
View parallel_bug.py
import keras.models as M
import keras.layers as L
from kur.utils.parallelism import make_parallel
# Pretend to have some 32 x 32 images.
input = x = L.Input(shape=(32, 32))
# Shape: (samples, 32, 32)
x = L.TimeDistributed(