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# 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 == scheduler_host(hosts):
save(model_dir, mlp_model)
def model_fn(path_to_model_files):
import neomx # noqa: F401
ctx = mx.cpu()
sym, arg_params, aux_params = mx.model.load_checkpoint(
os.path.join(path_to_model_files, "compiled"), 0
)
mod = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
mod.bind(
for_training=False, data_shapes=[("data", (1, 1, 28, 28))], label_shapes=mod._label_shapes
)
mod.set_params(arg_params, aux_params, allow_missing=True)
return mod
def transform_fn(mod, payload, input_content_type, requested_output_content_type):
import neomx # noqa: F401
if input_content_type != "application/vnd+python.numpy+binary":
raise RuntimeError("Input content type must be application/vnd+python.numpy+binary")
inference_payload = np.asarray(json.loads(payload.decode("utf-8")))
result = mod.predict(inference_payload)
result = np.squeeze(result)
response_body = json.dumps(result.asnumpy().tolist())
content_type = "application/json"
return response_body, content_type
if __name__ == "__main__":
# Import here to prevent import during serving
from sagemaker_mxnet_container.training_utils import scheduler_host, save
parser = argparse.ArgumentParser()
parser.add_argument("--batch-size", type=int, default=100)
parser.add_argument("--epochs", type=int, default=1)
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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