AWS Dev Day 資料: AWS におけるエッジでの機械学習

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BUILD TRAIN DEPLOY
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( .
estimator = MXNet(entry_point= train.py',
base_job_name=job_name,
role=role,
train_instance_count=1,
train_instance_type='ml.p3.2xlarge',
framework_version='1.2.1',
py_version='py3',
hyperparameters={
'num_classes': 4,
'epochs': 10,
'learning_rate': 0.001,
'mini_batch_size': 16,
'momentum': 0.9,
'wd': 0.0001
})
estimator.fit(inputs=data_channels, logs=True)
def train(current_host, channel_input_dirs,
hyperparameters, hosts, num_cpus, num_gpus,
model_dir):
logging.info(mx.__version__)
classes = hyperparameters.get('num_classes', 23)
epochs = hyperparameters.get('epochs', 5)
lr = hyperparameters.get('learning_rate', 0.001)
per_device_batch_size =
hyperparameters.get('mini_batch_size', 1)
momentum = hyperparameters.get('momentum', 0.9)
wd = hyperparameters.get('wd', 0.0001)
network = hyperparameters.get('network',
'mobilenet1.0 )
train_path = channel_input_dirs['train']
validation_path = channel_input_dirs['validation']
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26. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
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finetune_net = gluon.model_zoo.vision.get_model(‘mobilenet1.0’, pretrained=True)
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{
“Prediction”: {
…
},
“s3path”: “s3://xxx”
}
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AWS
IoT
SageMakerGreengrass
Service
Results
Images
Upload
images
Kinesis
Firehose
Deploy trained model
Read training
data
Greengrass Core
Raspberry Pi
S3 (Data Lake)
Upload
results

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