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#
# 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.
from __future__ import print_function
import argparse
import logging
import os
import chainer
from chainer import serializers, training
from chainer.datasets import tuple_dataset
import chainer.functions as F
import chainer.links as L
from chainer.training import extensions
import chainermn
import numpy as np
import sagemaker_containers
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
class MLP(chainer.Chain):
def __init__(self, n_units, n_out):
super(MLP, self).__init__()
with self.init_scope():
# the size of the inputs to each layer will be inferred
self.l1 = L.Linear(None, n_units) # n_in -> n_units
self.l2 = L.Linear(None, n_units) # n_units -> n_units
self.l3 = L.Linear(None, n_out) # n_units -> n_out
def __call__(self, x):
h1 = F.relu(self.l1(x))
h2 = F.relu(self.l2(h1))
return self.l3(h2)
def _preprocess_mnist(raw, withlabel, ndim, scale, image_dtype, label_dtype, rgb_format):
images = raw["x"][-100:]
if ndim == 2:
images = images.reshape(-1, 28, 28)
elif ndim == 3:
images = images.reshape(-1, 1, 28, 28)
if rgb_format:
images = np.broadcast_to(images, (len(images), 3) + images.shape[2:])
elif ndim != 1:
raise ValueError("invalid ndim for MNIST dataset")
images = images.astype(image_dtype)
images *= scale / 255.0
if withlabel:
labels = raw["y"][-100:].astype(label_dtype)
return tuple_dataset.TupleDataset(images, labels)
return images
if __name__ == "__main__":
env = sagemaker_containers.training_env()
parser = argparse.ArgumentParser()
# Data and model checkpoints directories
parser.add_argument("--epochs", type=int, default=1)
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument("--communicator", type=str, default="pure_nccl")
parser.add_argument("--frequency", type=int, default=20)
parser.add_argument("--units", type=int, default=1000)
parser.add_argument("--model-dir", type=str)
parser.add_argument("--output-data-dir", type=str, default=env.output_data_dir)
parser.add_argument("--host", type=str, default=env.current_host)
parser.add_argument("--num-gpus", type=int, default=env.num_gpus)
parser.add_argument("--train", type=str, default=env.channel_input_dirs["train"])
parser.add_argument("--test", type=str, default=env.channel_input_dirs["test"])
args = parser.parse_args()
train_file = np.load(os.path.join(args.train, "train.npz"))
test_file = np.load(os.path.join(args.test, "test.npz"))
logger.info("Current host: {}".format(args.host))
communicator = "naive" if args.num_gpus == 0 else args.communicator
comm = chainermn.create_communicator(communicator)
device = comm.intra_rank if args.num_gpus > 0 else -1
print("==========================================")
print("Using {} communicator".format(comm))
print("Num unit: {}".format(args.units))
print("Num Minibatch-size: {}".format(args.batch_size))
print("Num epoch: {}".format(args.epochs))
print("==========================================")
model = L.Classifier(MLP(args.units, 10))
if device >= 0:
chainer.cuda.get_device(device).use()
# Create a multi node optimizer from a standard Chainer optimizer.
optimizer = chainermn.create_multi_node_optimizer(chainer.optimizers.Adam(), comm)
optimizer.setup(model)
preprocess_mnist_options = {
"withlabel": True,
"ndim": 1,
"scale": 1.0,
"image_dtype": np.float32,
"label_dtype": np.int32,
"rgb_format": False,
}
train_dataset = _preprocess_mnist(train_file, **preprocess_mnist_options)
test_dataset = _preprocess_mnist(test_file, **preprocess_mnist_options)
train_iter = chainer.iterators.SerialIterator(train_dataset, args.batch_size)
test_iter = chainer.iterators.SerialIterator(
test_dataset, args.batch_size, repeat=False, shuffle=False
)
updater = training.StandardUpdater(train_iter, optimizer, device=device)
trainer = training.Trainer(updater, (args.epochs, "epoch"), out=args.output_data_dir)
# Create a multi node evaluator from a standard Chainer evaluator.
evaluator = extensions.Evaluator(test_iter, model, device=device)
evaluator = chainermn.create_multi_node_evaluator(evaluator, comm)
trainer.extend(evaluator)
# Some display and output extensions are necessary only for one worker.
# (Otherwise, there would just be repeated outputs.)
if comm.rank == 0:
if extensions.PlotReport.available():
trainer.extend(
extensions.PlotReport(
["main/loss", "validation/main/loss"], "epoch", file_name="loss.png"
)
)
trainer.extend(
extensions.PlotReport(
["main/accuracy", "validation/main/accuracy"], "epoch", file_name="accuracy.png"
)
)
trainer.extend(extensions.snapshot(), trigger=(args.frequency, "epoch"))
trainer.extend(extensions.dump_graph("main/loss"))
trainer.extend(extensions.LogReport())
trainer.extend(
extensions.PrintReport(
[
"epoch",
"main/loss",
"validation/main/loss",
"main/accuracy",
"validation/main/accuracy",
"elapsed_time",
]
)
)
trainer.extend(extensions.ProgressBar())
trainer.run()
# only save the model in the master node
if args.host == env.hosts[0]:
serializers.save_npz(os.path.join(env.model_dir, "model.npz"), model)
def model_fn(model_dir):
model = L.Classifier(MLP(1000, 10))
serializers.load_npz(os.path.join(model_dir, "model.npz"), model)
return model.predictor
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