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Training script used by SageMaker
import argparse
import logging
import os
from pickle import load
import mxnet as mx
import numpy as np
from mxnet import autograd, nd, gluon
from mxnet.contrib import onnx as onnx_mxnet
from mxnet.gluon.loss import L2Loss
from mxnet.gluon.nn import Dense, Dropout, HybridSequential
from mxnet.gluon.trainer import Trainer
from mxnet.initializer import Xavier
logging.basicConfig(level=logging.INFO)
def train(data_dir, num_gpus):
mx.random.seed(42)
with open("{}/train/data.p".format(data_dir), "rb") as pickle:
train_nd = load(pickle)
with open("{}/test/data.p".format(data_dir), "rb") as pickle:
test_nd = load(pickle)
train_data = gluon.data.DataLoader(train_nd, 64, shuffle=True)
validation_data = gluon.data.DataLoader(test_nd, 64, shuffle=True)
net = HybridSequential()
with net.name_scope():
net.add(Dense(9))
net.add(Dropout(.25))
net.add(Dense(16))
net.add(Dropout(.25))
net.add(Dense(1))
net.hybridize()
ctx = mx.gpu() if num_gpus > 0 else mx.cpu()
# Also known as Glorot
net.collect_params().initialize(Xavier(magnitude=2.24), ctx=ctx)
loss = L2Loss()
trainer = Trainer(net.collect_params(), optimizer="adam")
smoothing_constant = .01
for e in range(5):
moving_loss = 0
for i, (data, label) in enumerate(train_data):
data = data.as_in_context(ctx)
label = label.as_in_context(ctx)
with autograd.record():
output = net(data)
loss_result = loss(output, label)
loss_result.backward()
trainer.step(64)
curr_loss = nd.mean(loss_result).asscalar()
moving_loss = (curr_loss if ((i == 0) and (e == 0))
else (1 - smoothing_constant) * moving_loss + smoothing_constant * curr_loss)
test_mae = measure_performance(net, ctx, validation_data)
train_mae = measure_performance(net, ctx, train_data)
print("Epoch %s. Loss: %s, Train_mae %s, Test_mae %s" % (e, moving_loss, train_mae, test_mae))
return net
def measure_performance(model, ctx, data_iter):
mae = mx.metric.MAE()
for _, (data, labels) in enumerate(data_iter):
data = data.as_in_context(ctx)
labels = labels.as_in_context(ctx)
output = model(data)
predictions = output
mae.update(preds=predictions, labels=labels)
return mae.get()[1]
def save(net, model_dir):
net.export("model", epoch=4)
onnx_mxnet.export_model(sym="model-symbol.json",
params="model-0004.params",
input_shape=[(1, 4)],
input_type=np.float32,
onnx_file_path="{}/model.onnx".format(model_dir),
verbose=True)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--model-dir', type=str, default=os.environ["SM_MODEL_DIR"])
parser.add_argument("--data-dir", type=str, default=os.environ["SM_CHANNEL_TRAINING"])
parser.add_argument("--gpus", type=int, default=os.environ["SM_NUM_GPUS"])
args, _ = parser.parse_known_args()
net = train(args.data_dir, args.gpus)
save(net, args.model_dir)
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