Keras + Tensorflow: Prediction on multiple gpus
I created one simple example to show how to run keras model across multiple gpus. Basically, multiple processes are created and each of process owns a gpu. To specify the gpu id in process, setting env variable CUDA_VISIBLE_DEVICES is a very straightforward way (os.environ["CUDA_VISIBLE_DEVICES"]). Hope this git repo can help you.
https://github.com/yuanyuanli85/Keras-Multiple-Process-Prediction
You can use this function to parallelize a Keras model (credits to kuza55).
https://github.com/kuza55/keras-extras/blob/master/utils/multi_gpu.py
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from keras.layers import merge
from keras.layers.core import Lambda
from keras.models import Model
import tensorflow as tf
def make_parallel(model, gpu_count):
def get_slice(data, idx, parts):
shape = tf.shape(data)
size = tf.concat([ shape[:1] // parts, shape[1:] ],axis=0)
stride = tf.concat([ shape[:1] // parts, shape[1:]*0 ],axis=0)
start = stride * idx
return tf.slice(data, start, size)
outputs_all = []
for i in range(len(model.outputs)):
outputs_all.append([])
#Place a copy of the model on each GPU, each getting a slice of the batch
for i in range(gpu_count):
with tf.device('/gpu:%d' % i):
with tf.name_scope('tower_%d' % i) as scope:
inputs = []
#Slice each input into a piece for processing on this GPU
for x in model.inputs:
input_shape = tuple(x.get_shape().as_list())[1:]
slice_n = Lambda(get_slice, output_shape=input_shape, arguments={'idx':i,'parts':gpu_count})(x)
inputs.append(slice_n)
outputs = model(inputs)
if not isinstance(outputs, list):
outputs = [outputs]
#Save all the outputs for merging back together later
for l in range(len(outputs)):
outputs_all[l].append(outputs[l])
# merge outputs on CPU
with tf.device('/cpu:0'):
merged = []
for outputs in outputs_all:
merged.append(merge(outputs, mode='concat', concat_axis=0))
return Model(input=model.inputs, output=merged)