smalldog

import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import multi_gpu_model
import time as time

model = Sequential()
model.add(Dense(4000, input_dim=8000, activation='tanh'))
model.add(Dense(2000, input_dim=8000, activation='relu'))
model.add(Dense(500, activation='relu'))
model.add(Dense(300, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
print (model.summary())

print('(*) 4 gpus')
st = time.time()
model = multi_gpu_model(model, 4)
optimizer = keras.optimizers.Adam(lr=0.0001)
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
x = np.random.rand(131072, 8000)
y = np.random.randint(0, 2, (131072, 1))
model.fit(x, y, batch_size=2048*4)
print(f"Time {time.time() - st:.02f}s")

print('(*) 8 gpus')
st = time.time()
model = multi_gpu_model(model, 8)
optimizer = keras.optimizers.Adam(lr=0.0001)
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
x = np.random.rand(131072, 8000)
y = np.random.randint(0, 2, (131072, 1))
model.fit(x, y, batch_size=2048*4)
print(f"Time {time.time() - st:.02f}s")