Keras Interface

FlexFlow provides a drop-in replacement for TensorFlow Keras. Running an existing Keras program on the FlexFlow backend only requires a few lines of changes to the program. The detailed instructions are as follows:

1. Replace the Keras header files

Redirect the program to import Keras functions from FlexFlow by using the following import header lines:

from flexflow.keras.models import Model, Sequential
from flexflow.keras.layers import Input, Dense, Conv2D, ...
from flexflow.keras.callbacks import Callback, ...

2. Modify the main Keras program

FlexFlow requires a Keras program to wrap its model construction in a Python function called top_level_task(). This allows FlexFlow to automatically parallelize DNN training across all GPUs on all compute nodes. For example, the following code snippet shows parallelizing AlexNet training in FlexFlow:

def top_level_task():
  model = Sequential()
  model.add(Conv2D(filters=64, input_shape=(3,229,229), kernel_size=(11,11), strides=(4,4), padding=(2,2), activation="relu"))
  model.add(MaxPooling2D(pool_size=(3,3), strides=(2,2), padding="valid"))
  model.add(Conv2D(filters=192, kernel_size=(5,5), strides=(1,1), padding=(2,2), activation="relu"))
  ## More lines for model construction
  ## Model compilation
  model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  ## Model training
  (x_train, y_train) = cifar10.load_data(), y_train, epochs=30)

if __name__ == "__main__":

More FlexFlow Keras examples are available on GitHub.