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6-4 Model Training Using Multiple GPUs

We recommend using pre-defined fit method for training when using multiple GPU, which only requires two additional lines of code.

In Colab notebook, choose GPU in Edit -> Notebook Settings -> Hardware Accelerator

Note: the following code only executes on Colab.

You may use the following link for testing (tf_multiGPU, in Chinese)

https://colab.research.google.com/drive/1j2kp_t0S_cofExSN7IyJ4QtMscbVlXU-

Introduction to MirroredStrategy：

  • The strategy gives a copy to each of the N computing devices before training.
  • When a batch of training data is received, devide the data into N portions and transfer them into N devices (data parallelism)
  • Each device calculate the local variables (mirrored variables) to calculate the gradient according to the received portion of data.
  • Implement All-reduce operation in parallel computing, exchange the gradient data and calculate summation, allows each device to obtain the gradient sum from all the devices.
  • Update the local variables (mirrored variables) using the gradient sum.
  • Proceed to the next round of training when all the devices updated their local variables (This is a fully synchronized strategy).
%tensorflow_version 2.x
import tensorflow as tf
print(tf.__version__)
from tensorflow.keras import * 
# Simulate two logical GPUs with one physical GPU
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    # Set two logical GPUs for simulation
    try:
        tf.config.experimental.set_virtual_device_configuration(gpus[0],
            [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024),
             tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)])
        logical_gpus = tf.config.experimental.list_logical_devices('GPU')
        print(len(gpus), "Physical GPU,", len(logical_gpus), "Logical GPUs")
    except RuntimeError as e:
        print(e)

1. Data Preparation

MAX_LEN = 300
BATCH_SIZE = 32
(x_train,y_train),(x_test,y_test) = datasets.reuters.load_data()
x_train = preprocessing.sequence.pad_sequences(x_train,maxlen=MAX_LEN)
x_test = preprocessing.sequence.pad_sequences(x_test,maxlen=MAX_LEN)

MAX_WORDS = x_train.max()+1
CAT_NUM = y_train.max()+1

ds_train = tf.data.Dataset.from_tensor_slices((x_train,y_train)) \
          .shuffle(buffer_size = 1000).batch(BATCH_SIZE) \
          .prefetch(tf.data.experimental.AUTOTUNE).cache()
   
ds_test = tf.data.Dataset.from_tensor_slices((x_test,y_test)) \
          .shuffle(buffer_size = 1000).batch(BATCH_SIZE) \
          .prefetch(tf.data.experimental.AUTOTUNE).cache()

2. Model Defining

tf.keras.backend.clear_session()
def create_model():
    
    model = models.Sequential()

    model.add(layers.Embedding(MAX_WORDS,7,input_length=MAX_LEN))
    model.add(layers.Conv1D(filters = 64,kernel_size = 5,activation = "relu"))
    model.add(layers.MaxPool1D(2))
    model.add(layers.Conv1D(filters = 32,kernel_size = 3,activation = "relu"))
    model.add(layers.MaxPool1D(2))
    model.add(layers.Flatten())
    model.add(layers.Dense(CAT_NUM,activation = "softmax"))
    return(model)

def compile_model(model):
    model.compile(optimizer=optimizers.Nadam(),
                loss=losses.SparseCategoricalCrossentropy(from_logits=True),
                metrics=[metrics.SparseCategoricalAccuracy(),metrics.SparseTopKCategoricalAccuracy(5)]) 
    return(model)

3. Model Training

# Add the following two lines of code
strategy = tf.distribute.MirroredStrategy()  
with strategy.scope(): 
    model = create_model()
    model.summary()
    model = compile_model(model)
    
history = model.fit(ds_train,validation_data = ds_test,epochs = 10)  
WARNING:tensorflow:NCCL is not supported when using virtual GPUs, fallingback to reduction to one device
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1')
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding (Embedding)        (None, 300, 7)            216874    
_________________________________________________________________
conv1d (Conv1D)              (None, 296, 64)           2304      
_________________________________________________________________
max_pooling1d (MaxPooling1D) (None, 148, 64)           0         
_________________________________________________________________
conv1d_1 (Conv1D)            (None, 146, 32)           6176      
_________________________________________________________________
max_pooling1d_1 (MaxPooling1 (None, 73, 32)            0         
_________________________________________________________________
flatten (Flatten)            (None, 2336)              0         
_________________________________________________________________
dense (Dense)                (None, 46)                107502    
=================================================================
Total params: 332,856
Trainable params: 332,856
Non-trainable params: 0
_________________________________________________________________
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
Train for 281 steps, validate for 71 steps
Epoch 1/10
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:GPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1').
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:GPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1').
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:GPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1').
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:GPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1').
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:GPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1').
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:GPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1').
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:GPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1').
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:GPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1').
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:GPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1').
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:GPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1').
281/281 [==============================] - 15s 53ms/step - loss: 2.0270 - sparse_categorical_accuracy: 0.4653 - sparse_top_k_categorical_accuracy: 0.7481 - val_loss: 1.7517 - val_sparse_categorical_accuracy: 0.5481 - val_sparse_top_k_categorical_accuracy: 0.7578
Epoch 2/10
281/281 [==============================] - 4s 14ms/step - loss: 1.5206 - sparse_categorical_accuracy: 0.6045 - sparse_top_k_categorical_accuracy: 0.7938 - val_loss: 1.5715 - val_sparse_categorical_accuracy: 0.5993 - val_sparse_top_k_categorical_accuracy: 0.7983
Epoch 3/10
281/281 [==============================] - 4s 14ms/step - loss: 1.2178 - sparse_categorical_accuracy: 0.6843 - sparse_top_k_categorical_accuracy: 0.8547 - val_loss: 1.5232 - val_sparse_categorical_accuracy: 0.6327 - val_sparse_top_k_categorical_accuracy: 0.8112
Epoch 4/10
281/281 [==============================] - 4s 13ms/step - loss: 0.9127 - sparse_categorical_accuracy: 0.7648 - sparse_top_k_categorical_accuracy: 0.9113 - val_loss: 1.6527 - val_sparse_categorical_accuracy: 0.6296 - val_sparse_top_k_categorical_accuracy: 0.8201
Epoch 5/10
281/281 [==============================] - 4s 14ms/step - loss: 0.6606 - sparse_categorical_accuracy: 0.8321 - sparse_top_k_categorical_accuracy: 0.9525 - val_loss: 1.8791 - val_sparse_categorical_accuracy: 0.6158 - val_sparse_top_k_categorical_accuracy: 0.8219
Epoch 6/10
281/281 [==============================] - 4s 14ms/step - loss: 0.4919 - sparse_categorical_accuracy: 0.8799 - sparse_top_k_categorical_accuracy: 0.9725 - val_loss: 2.1282 - val_sparse_categorical_accuracy: 0.6037 - val_sparse_top_k_categorical_accuracy: 0.8112
Epoch 7/10
281/281 [==============================] - 4s 14ms/step - loss: 0.3947 - sparse_categorical_accuracy: 0.9051 - sparse_top_k_categorical_accuracy: 0.9814 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.6046 - val_sparse_top_k_categorical_accuracy: 0.8094
Epoch 8/10
281/281 [==============================] - 4s 14ms/step - loss: 0.3335 - sparse_categorical_accuracy: 0.9207 - sparse_top_k_categorical_accuracy: 0.9863 - val_loss: 2.4255 - val_sparse_categorical_accuracy: 0.5993 - val_sparse_top_k_categorical_accuracy: 0.8099
Epoch 9/10
281/281 [==============================] - 4s 14ms/step - loss: 0.2919 - sparse_categorical_accuracy: 0.9304 - sparse_top_k_categorical_accuracy: 0.9911 - val_loss: 2.5571 - val_sparse_categorical_accuracy: 0.6020 - val_sparse_top_k_categorical_accuracy: 0.8126
Epoch 10/10
281/281 [==============================] - 4s 14ms/step - loss: 0.2617 - sparse_categorical_accuracy: 0.9342 - sparse_top_k_categorical_accuracy: 0.9937 - val_loss: 2.6700 - val_sparse_categorical_accuracy: 0.6077 - val_sparse_top_k_categorical_accuracy: 0.8148
CPU times: user 1min 2s, sys: 8.59 s, total: 1min 10s
Wall time: 58.5 s



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