Datasets:
HenryAI
/
KerasBERTv1-Data

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# Return the MNIST dataset in the form of a `tf.data.Dataset`.
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
# Preprocess the data (these are Numpy arrays)
x_train = x_train.reshape(-1, 784).astype("float32") / 255
x_test = x_test.reshape(-1, 784).astype("float32") / 255
y_train = y_train.astype("float32")
y_test = y_test.astype("float32")
# Reserve num_val_samples samples for validation
x_val = x_train[-num_val_samples:]
y_val = y_train[-num_val_samples:]
x_train = x_train[:-num_val_samples]
y_train = y_train[:-num_val_samples]
return (
tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(batch_size),
tf.data.Dataset.from_tensor_slices((x_val, y_val)).batch(batch_size),
tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(batch_size),
)
# Create a MirroredStrategy.
strategy = tf.distribute.MirroredStrategy()
print("Number of devices: {}".format(strategy.num_replicas_in_sync))
# Open a strategy scope.
with strategy.scope():
# Everything that creates variables should be under the strategy scope.
# In general this is only model construction & `compile()`.
model = get_compiled_model()
# Train the model on all available devices.
train_dataset, val_dataset, test_dataset = get_dataset()
model.fit(train_dataset, epochs=2, validation_data=val_dataset)
# Test the model on all available devices.
model.evaluate(test_dataset)
WARNING: Logging before flag parsing goes to stderr.
W0829 16:54:57.025418 4592479680 cross_device_ops.py:1115] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.
Number of devices: 1
Epoch 1/2
1563/1563 [==============================] - 3s 2ms/step - loss: 0.3767 - sparse_categorical_accuracy: 0.8889 - val_loss: 0.1257 - val_sparse_categorical_accuracy: 0.9623
Epoch 2/2
1563/1563 [==============================] - 2s 2ms/step - loss: 0.1053 - sparse_categorical_accuracy: 0.9678 - val_loss: 0.0944 - val_sparse_categorical_accuracy: 0.9710
313/313 [==============================] - 0s 779us/step - loss: 0.0900 - sparse_categorical_accuracy: 0.9723
[0.08995261788368225, 0.9722999930381775]
Using callbacks to ensure fault tolerance
When using distributed training, you should always make sure you have a strategy to recover from failure (fault tolerance). The simplest way to handle this is to pass ModelCheckpoint callback to fit(), to save your model at regular intervals (e.g. every 100 batches or every epoch). You can then restart training from your saved model.
Here's a simple example:
import os
from tensorflow import keras
# Prepare a directory to store all the checkpoints.
checkpoint_dir = "./ckpt"
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
def make_or_restore_model():
# Either restore the latest model, or create a fresh one
# if there is no checkpoint available.
checkpoints = [checkpoint_dir + "/" + name for name in os.listdir(checkpoint_dir)]
if checkpoints:
latest_checkpoint = max(checkpoints, key=os.path.getctime)
print("Restoring from", latest_checkpoint)
return keras.models.load_model(latest_checkpoint)
print("Creating a new model")
return get_compiled_model()
def run_training(epochs=1):
# Create a MirroredStrategy.
strategy = tf.distribute.MirroredStrategy()
# Open a strategy scope and create/restore the model
with strategy.scope():
model = make_or_restore_model()
callbacks = [
# This callback saves a SavedModel every epoch
# We include the current epoch in the folder name.
keras.callbacks.ModelCheckpoint(
filepath=checkpoint_dir + "/ckpt-{epoch}", save_freq="epoch"
)
]
model.fit(
train_dataset,
epochs=epochs,
callbacks=callbacks,
validation_data=val_dataset,
verbose=2,
)
# Running the first time creates the model