File size: 2,944 Bytes
476455e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | # Copyright 2018 Uber Technologies, Inc. All Rights Reserved.
# Modifications Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
import tensorflow as tf
import smdistributed.dataparallel.tensorflow as dist
tf.random.set_seed(42)
dist.init()
gpus = tf.config.experimental.list_physical_devices("GPU")
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
if gpus:
tf.config.experimental.set_visible_devices(gpus[dist.local_rank()], "GPU")
(mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data(
path="mnist-%d.npz" % dist.rank()
)
dataset = tf.data.Dataset.from_tensor_slices(
(tf.cast(mnist_images[..., tf.newaxis] / 255.0, tf.float32), tf.cast(mnist_labels, tf.int64))
)
dataset = dataset.repeat().shuffle(10000).batch(128)
mnist_model = tf.keras.Sequential(
[
tf.keras.layers.Conv2D(32, [3, 3], activation="relu"),
tf.keras.layers.Conv2D(64, [3, 3], activation="relu"),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation="relu"),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(10, activation="softmax"),
]
)
loss = tf.losses.SparseCategoricalCrossentropy()
# LR for 8 node run : 0.000125
# LR for single node run : 0.001
opt = tf.optimizers.Adam(0.000125 * dist.size())
checkpoint_dir = "./checkpoints"
checkpoint = tf.train.Checkpoint(model=mnist_model, optimizer=opt)
@tf.function
def training_step(images, labels, first_batch):
with tf.GradientTape() as tape:
probs = mnist_model(images, training=True)
loss_value = loss(labels, probs)
tape = dist.DistributedGradientTape(tape)
grads = tape.gradient(loss_value, mnist_model.trainable_variables)
opt.apply_gradients(zip(grads, mnist_model.trainable_variables))
if first_batch:
dist.broadcast_variables(mnist_model.variables, root_rank=0)
dist.broadcast_variables(opt.variables(), root_rank=0)
loss_value = dist.oob_allreduce(loss_value) # Average the loss across workers
return loss_value
for batch, (images, labels) in enumerate(dataset.take(10000 // dist.size())):
loss_value = training_step(images, labels, batch == 0)
if batch % 50 == 0 and dist.rank() == 0:
print("Step #%d\tLoss: %.6f" % (batch, loss_value))
if dist.rank() == 0:
checkpoint.save(checkpoint_dir)
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