© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

http://hunkim.github.io/ml/

More compute
Accuracy
Scale (data size, model size)
neural
networks
other
approaches
Now
© Jeff Dean, Trends and Developments in Deep Learning Research
http://www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research

BlindTool by Joseph Paul Cohen on Nexus 4
Mobile Application
• https://github.com/dmlc/mxnet.js/
• http://rupeshs.github.io/machineye
MXNetJS in Web Browser
Web Applications
• https://www.youtube.com/watch
?v=UHUC4ueEiwM
• https://play.google.com/store/ap
ps/details?id=the.blindtool

Deep Drone: Object Detection and Tracking
for Smart Drones on Embedded System
TX1 with
customized
board
Drone
• https://web.stanford.edu/class/cs231a/prev_projects_20
16/deep-drone-object__2_.pdf
Deep RL | Playing Flappy Birds
• https://github.com/li-haoran/DRL-FlappyBird
• https://github.com/devsisters/DQN-tensorflow
Human-Level Control
through Deep Reinforcement Learning

Spot Instances (80% ↓)
= $30 per hour

$aws ec2-run-instances ami-b232d0db
--instance-count 20
--instance-type p2.8xlarge
--region us-east-1
$aws ec2-stop-instances
i-10a64379 i-10a64280 ...

1GiB
GPU Memory
2 GiB
4 GiB
8 GiB

NVIDIA Tesla
GPU Card
P2: GPU-accelerated computing
§ Enabling a high degree of parallelism – each
GPU has thousands of cores
§ Consistent, well documented set of APIs
(CUDA, OpenACC, OpenCL)
§ Supported by a wide variety of ISVs and open
source frameworks
Xilinx UltraScale+
FPGA
F1: FPGA-accelerated computing
§ Massively parallel – each FPGA includes millions
of parallel system logic cells
§ Flexible – no fixed instruction set, can implement
wide or narrow datapaths
§ Programmable using available, cloud-based FPGA
development tools

https://aws.amazon.com/ko/batch/

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http://bit.ly/deepami
http://bit.ly/deepubuntu

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기반 예제
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http://mxnet.io/
https://github.com/dmlc/mxnet
http://incubator.apache.org/projects/mxnet.html

3

http://bit.ly/deepcfn
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1
4.75
8.5
12.25
16
1 4.75 8.5 12.25 16
Speedup(x)
# GPUs
Resnet 152 Inceptin V3
Alexnet Ideal
91% Efficiency
88% Efficiency
# GPUs
• EC2 16x P2.16xlarge by AWS CloudFormation
• Mounted on Amazon EFS

../../tools/launch.py -n
$DEEPLEARNING_WORKERS_COUNT -H
$DEEPLEARNING_WORKERS_PATH
python train_mnist.py
--gpus $(seq -s , 0 1 $
(($DEEPLEARNING_WORKER_GPU_COUNT - 1)))
--network lenet --kv-store dist_sync
parameter
server
network
choice
update
policy

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https://www.youtube.com/
watch?v=q6gx9yk0nQo
https://www.slideshare.net/AIFrontie
rs/scaling-deep-learning-with-mxnet

http://j.mp/2hne9IL

import mxnet as mx
bucket = 'my-model-bucket’
s3_client = boto3.client('s3')
f_params = 'resnet-18-0000.params'
f_symbol = 'resnet-18-symbol.json'
#params
f_params_file = tempfile.NamedTemporaryFile()
s3_client.download_file(bucket, f_params, f_params_file.name)
#symbol
f_symbol_file = tempfile.NamedTemporaryFile()
s3_client.download_file(bucket, f_symbol, f_symbol_file.name)
def lambda_handler(event, context):
model = load_model(f_params_file, f_symbol_file)
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$ wrk -t40 -c120 -d60s https://48l7awor09.execute-api.us-
east-1.amazonaws.com/dev/predict?url=img_url
Thread Stats Avg Stdev Max +/- Stdev
Latency 1.18s 96.82ms 1.98s 81.23%
Req/Sec 2.39 2.87 10.00 87.91%
4520 requests in 1.00m, 3.16MB read
Socket errors: connect 0, read 0, write 0, timeout 0
Requests/sec: 75.23
Transfer/sec: 53.85KB

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딥러닝 개발자를 위한 AWS 크레딧 제공!
http://bit.ly/awskr-feedback
AWS Activate 패키지
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