Trial receive the hyper-parameter/architecture configure from Tuner, and send intermediate result to Assessor and final result to Tuner.
So when user want to write a Trial running on NNI, she/he should:
1)Have an original Trial could run,
Trial's code could be any machine learning code that could run in local. Here we use mnist-keras.py as example:
import argparse
import logging
import keras
import numpy as np
from keras import backend as K
from keras.datasets import mnist
from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D
from keras.models import Sequential
K.set_image_data_format('channels_last')
H, W = 28, 28
NUM_CLASSES = 10
def create_mnist_model(hyper_params, input_shape=(H, W, 1), num_classes=NUM_CLASSES):
layers = [
Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D(pool_size=(2, 2)),
Flatten(),
Dense(100, activation='relu'),
Dense(num_classes, activation='softmax')
]
model = Sequential(layers)
if hyper_params['optimizer'] == 'Adam':
optimizer = keras.optimizers.Adam(lr=hyper_params['learning_rate'])
else:
optimizer = keras.optimizers.SGD(lr=hyper_params['learning_rate'], momentum=0.9)
model.compile(loss=keras.losses.categorical_crossentropy, optimizer=optimizer, metrics=['accuracy'])
return model
def load_mnist_data(args):
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = (np.expand_dims(x_train, -1).astype(np.float) / 255.)[:args.num_train]
x_test = (np.expand_dims(x_test, -1).astype(np.float) / 255.)[:args.num_test]
y_train = keras.utils.to_categorical(y_train, NUM_CLASSES)[:args.num_train]
y_test = keras.utils.to_categorical(y_test, NUM_CLASSES)[:args.num_test]
return x_train, y_train, x_test, y_test
class SendMetrics(keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs={}):
pass
def train(args, params):
x_train, y_train, x_test, y_test = load_mnist_data(args)
model = create_mnist_model(params)
model.fit(x_train, y_train, batch_size=args.batch_size, epochs=args.epochs, verbose=1,
validation_data=(x_test, y_test), callbacks=[SendMetrics()])
_, acc = model.evaluate(x_test, y_test, verbose=0)
def generate_default_params():
return {
'optimizer': 'Adam',
'learning_rate': 0.001
}
if __name__ == '__main__':
PARSER = argparse.ArgumentParser()
PARSER.add_argument("--batch_size", type=int, default=200, help="batch size", required=False)
PARSER.add_argument("--epochs", type=int, default=10, help="Train epochs", required=False)
PARSER.add_argument("--num_train", type=int, default=1000, help="Number of train samples to be used, maximum 60000", required=False)
PARSER.add_argument("--num_test", type=int, default=1000, help="Number of test samples to be used, maximum 10000", required=False)
ARGS, UNKNOWN = PARSER.parse_known_args()
PARAMS = generate_default_params()
train(ARGS, PARAMS)2)Get configure from Tuner
User import nni and use nni.get_next_parameter() to receive configure. Please noted 10, 24 and 25 line in the following code.
import argparse
import logging
import keras
import numpy as np
from keras import backend as K
from keras.datasets import mnist
from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D
from keras.models import Sequential
import nni
...
if __name__ == '__main__':
PARSER = argparse.ArgumentParser()
PARSER.add_argument("--batch_size", type=int, default=200, help="batch size", required=False)
PARSER.add_argument("--epochs", type=int, default=10, help="Train epochs", required=False)
PARSER.add_argument("--num_train", type=int, default=1000, help="Number of train samples to be used, maximum 60000", required=False)
PARSER.add_argument("--num_test", type=int, default=1000, help="Number of test samples to be used, maximum 10000", required=False)
ARGS, UNKNOWN = PARSER.parse_known_args()
PARAMS = generate_default_params()
RECEIVED_PARAMS = nni.get_next_parameter()
PARAMS.update(RECEIVED_PARAMS)
train(ARGS, PARAMS)3) Send intermediate result
Use nni.report_intermediate_result to send intermediate result to Assessor. Please noted 5 line in the following code.
...
class SendMetrics(keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs={}):
nni.report_intermediate_result(logs)
def train(args, params):
x_train, y_train, x_test, y_test = load_mnist_data(args)
model = create_mnist_model(params)
model.fit(x_train, y_train, batch_size=args.batch_size, epochs=args.epochs, verbose=1,
validation_data=(x_test, y_test), callbacks=[SendMetrics()])
_, acc = model.evaluate(x_test, y_test, verbose=0)
...4) Send final result
Use nni.report_final_result to send final result to Tuner. Please noted 15 line in the following code.
...
class SendMetrics(keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs={}):
nni.report_intermediate_result(logs)
def train(args, params):
x_train, y_train, x_test, y_test = load_mnist_data(args)
model = create_mnist_model(params)
model.fit(x_train, y_train, batch_size=args.batch_size, epochs=args.epochs, verbose=1,
validation_data=(x_test, y_test), callbacks=[SendMetrics()])
_, acc = model.evaluate(x_test, y_test, verbose=0)
nni.report_final_result(acc)
...Here is the complete example:
import argparse
import logging
import keras
import numpy as np
from keras import backend as K
from keras.datasets import mnist
from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D
from keras.models import Sequential
import nni
LOG = logging.getLogger('mnist_keras')
K.set_image_data_format('channels_last')
H, W = 28, 28
NUM_CLASSES = 10
def create_mnist_model(hyper_params, input_shape=(H, W, 1), num_classes=NUM_CLASSES):
'''
Create simple convolutional model
'''
layers = [
Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D(pool_size=(2, 2)),
Flatten(),
Dense(100, activation='relu'),
Dense(num_classes, activation='softmax')
]
model = Sequential(layers)
if hyper_params['optimizer'] == 'Adam':
optimizer = keras.optimizers.Adam(lr=hyper_params['learning_rate'])
else:
optimizer = keras.optimizers.SGD(lr=hyper_params['learning_rate'], momentum=0.9)
model.compile(loss=keras.losses.categorical_crossentropy, optimizer=optimizer, metrics=['accuracy'])
return model
def load_mnist_data(args):
'''
Load MNIST dataset
'''
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = (np.expand_dims(x_train, -1).astype(np.float) / 255.)[:args.num_train]
x_test = (np.expand_dims(x_test, -1).astype(np.float) / 255.)[:args.num_test]
y_train = keras.utils.to_categorical(y_train, NUM_CLASSES)[:args.num_train]
y_test = keras.utils.to_categorical(y_test, NUM_CLASSES)[:args.num_test]
LOG.debug('x_train shape: %s', (x_train.shape,))
LOG.debug('x_test shape: %s', (x_test.shape,))
return x_train, y_train, x_test, y_test
class SendMetrics(keras.callbacks.Callback):
'''
Keras callback to send metrics to NNI framework
'''
def on_epoch_end(self, epoch, logs={}):
'''
Run on end of each epoch
'''
LOG.debug(logs)
nni.report_intermediate_result(logs)
def train(args, params):
'''
Train model
'''
x_train, y_train, x_test, y_test = load_mnist_data(args)
model = create_mnist_model(params)
model.fit(x_train, y_train, batch_size=args.batch_size, epochs=args.epochs, verbose=1,
validation_data=(x_test, y_test), callbacks=[SendMetrics()])
_, acc = model.evaluate(x_test, y_test, verbose=0)
LOG.debug('Final result is: %d', acc)
nni.report_final_result(acc)
def generate_default_params():
'''
Generate default hyper parameters
'''
return {
'optimizer': 'Adam',
'learning_rate': 0.001
}
if __name__ == '__main__':
PARSER = argparse.ArgumentParser()
PARSER.add_argument("--batch_size", type=int, default=200, help="batch size", required=False)
PARSER.add_argument("--epochs", type=int, default=10, help="Train epochs", required=False)
PARSER.add_argument("--num_train", type=int, default=1000, help="Number of train samples to be used, maximum 60000", required=False)
PARSER.add_argument("--num_test", type=int, default=1000, help="Number of test samples to be used, maximum 10000", required=False)
ARGS, UNKNOWN = PARSER.parse_known_args()
try:
# get parameters from tuner
RECEIVED_PARAMS = nni.get_next_parameter()
LOG.debug(RECEIVED_PARAMS)
PARAMS = generate_default_params()
PARAMS.update(RECEIVED_PARAMS)
# train
train(ARGS, PARAMS)
except Exception as e:
LOG.exception(e)
raise