In a short space of time, fast.ai has become a popular Deep Learning library, driven by the success of the fast.ai online Massive Open Online Course (MOOC). It has allowed SW developers to achieve, in the span of a few weeks, state-of-the-art results in domains such as Computer Vision (CV), Natural Language Processing (NLP), and structured data machine learning. In this chalk talk, we go into the details of building, training, and deploying fast.ai-based models using Amazon SageMaker.

2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Building, Training, and Deploying fast.ai
Models Using Amazon SageMaker
Matt McClean
Solution Architect Manager
Amazon Web Services
A I M 4 2 8
Andrew Shaw
Researcher
fast.ai

3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high-
performance
algorithms
One-click
training
Automatic model
tuning
Build Train Deploy
Amazon SageMaker

4. Amazon SageMaker
1
I
Notebook instances
2
I
Algorithms
3
I
ML training service
4
I
ML hosting service

5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Training code
• Matrix factorization
• Regression
• Principal component analysis
• K-means clustering
• Gradient boosted trees
• And more!
Built-in algorithms
Bring your own script
(Amazon SageMaker builds the container)
Amazon SageMaker
estimators in Apache
Spark
Bring your own algorithm
(you build the container)
Amazon SageMaker training options

6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon SageMaker training service
Training Image Inference ImageTraining ImageTraining DataTraining Data
Model artifacts

7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon SageMaker hosting service
Training Image Inference ImageTraining ImageTraining DataTraining DataModel artifacts
Inference Image
Model artifacts
Endpoint

8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Custom algorithms with Amazon SageMaker
Train image
Train data (S3)
Hyperparameters
Model artifacts
Inference image
HTTP POST Response

9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Training image run
docker run {image} train
Amazon SageMaker run command

10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Training image run
/opt/ml
├── input
│ ├── config
│ │ ├── hyperparameters.json
│ │ └── resourceConfig.json
│ └── data
│ └── <channel_name>
│ └── <input data>
├── model
│ └── <model files>
└── output
└── failure

11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Inference image run
/opt/ml/model
Model artifacts directory
docker run {image} serve
Amazon SageMaker run command

12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Inference image server
post: /invocations
get: /ping
Inference calls
Endpoint health
HTTP
request
NGINX
GUNICORN
FLASK
ML code
Python

13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
fast.ai—Making neural nets uncool again
Deep learning for coders
• MOOC taught by Jeremy Howard
• Part 1 happening now
fastai Library
• Deep learning framework built on PyTorch
• SOTA best practices made easy
• Easily extensible
Founded by Jeremy Howard and Rachel Thomas

14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Research
Vision
NLP

15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Dogs vs. cats
Arch: Resnet50 Fastai Keras
Lines of code (excluding imports) 5 31
Stage 1 error 0.65% 2.05%
Stage 2 error 0.50% 0.80%
Test time augmentation (TTA) error 0.40% N/A*
Stage 1 time 9:30 8:30
Stage 2 time 12:48 17:38

16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Links
fast.ai Library - https://github.com/fastai/fastai
fast.ai Online course - http://course.fast.ai/
Amazon SageMaker fast.ai container -
https://github.com/mattmcclean/sagemaker-fastai-container or https://bit.ly/2RWxteU
Amazon SageMaker fast.ai example –
https://github.com/mattmcclean/sagemaker-fastai-example or https://bit.ly/2P8yxxJ

18. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Matt McClean
mmcclean@amazon.com
Andrew Shaw
andrew.t.shaw@gmail.com

19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.