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4021124 | 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 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | # 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 absolute_import
import argparse
import gzip
import json
import logging
import os
import struct
import mxnet as mx
import numpy as np
def load_data(path):
with gzip.open(find_file(path, "labels.gz")) as flbl:
struct.unpack(">II", flbl.read(8))
labels = np.fromstring(flbl.read(), dtype=np.int8)
with gzip.open(find_file(path, "images.gz")) as fimg:
_, _, rows, cols = struct.unpack(">IIII", fimg.read(16))
images = np.fromstring(fimg.read(), dtype=np.uint8).reshape(len(labels), rows, cols)
images = images.reshape(images.shape[0], 1, 28, 28).astype(np.float32) / 255
return labels, images
def find_file(root_path, file_name):
for root, dirs, files in os.walk(root_path):
if file_name in files:
return os.path.join(root, file_name)
def build_graph():
data = mx.sym.var("data")
data = mx.sym.flatten(data=data)
fc1 = mx.sym.FullyConnected(data=data, num_hidden=128)
act1 = mx.sym.Activation(data=fc1, act_type="relu")
fc2 = mx.sym.FullyConnected(data=act1, num_hidden=64)
act2 = mx.sym.Activation(data=fc2, act_type="relu")
fc3 = mx.sym.FullyConnected(data=act2, num_hidden=10)
return mx.sym.SoftmaxOutput(data=fc3, name="softmax")
def get_train_context(num_gpus):
if num_gpus:
return [mx.gpu(i) for i in range(num_gpus)]
else:
return mx.cpu()
def train(
batch_size,
epochs,
learning_rate,
num_gpus,
training_channel,
testing_channel,
hosts,
current_host,
model_dir,
):
(train_labels, train_images) = load_data(training_channel)
(test_labels, test_images) = load_data(testing_channel)
# Data parallel training - shard the data so each host
# only trains on a subset of the total data.
shard_size = len(train_images) // len(hosts)
for i, host in enumerate(hosts):
if host == current_host:
start = shard_size * i
end = start + shard_size
break
train_iter = mx.io.NDArrayIter(
train_images[start:end], train_labels[start:end], batch_size, shuffle=True
)
val_iter = mx.io.NDArrayIter(test_images, test_labels, batch_size)
logging.getLogger().setLevel(logging.DEBUG)
kvstore = "local" if len(hosts) == 1 else "dist_sync"
mlp_model = mx.mod.Module(symbol=build_graph(), context=get_train_context(num_gpus))
mlp_model.fit(
train_iter,
eval_data=val_iter,
kvstore=kvstore,
optimizer="sgd",
optimizer_params={"learning_rate": learning_rate},
eval_metric="acc",
batch_end_callback=mx.callback.Speedometer(batch_size, 100),
num_epoch=epochs,
)
if len(hosts) == 1 or current_host == hosts[0]:
save(model_dir, mlp_model)
def save(model_dir, model):
model.symbol.save(os.path.join(model_dir, "model-symbol.json"))
model.save_params(os.path.join(model_dir, "model-0000.params"))
signature = [
{"name": data_desc.name, "shape": [dim for dim in data_desc.shape]}
for data_desc in model.data_shapes
]
with open(os.path.join(model_dir, "model-shapes.json"), "w") as f:
json.dump(signature, f)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--batch-size", type=int, default=100)
parser.add_argument("--epochs", type=int, default=10)
parser.add_argument("--learning-rate", type=float, default=0.1)
parser.add_argument("--model-dir", type=str, default=os.environ["SM_MODEL_DIR"])
parser.add_argument("--train", type=str, default=os.environ["SM_CHANNEL_TRAIN"])
parser.add_argument("--test", type=str, default=os.environ["SM_CHANNEL_TEST"])
parser.add_argument("--current-host", type=str, default=os.environ["SM_CURRENT_HOST"])
parser.add_argument("--hosts", type=list, default=json.loads(os.environ["SM_HOSTS"]))
args = parser.parse_args()
num_gpus = int(os.environ["SM_NUM_GPUS"])
train(
args.batch_size,
args.epochs,
args.learning_rate,
num_gpus,
args.train,
args.test,
args.hosts,
args.current_host,
args.model_dir,
)
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