Deep Learning has become ubiquitous with abundance of data, commoditization of compute and storage. Pre-trained models are readily available for many use-cases. Distributed Inference has many applications such as pre-computing results offline, backfilling historic data with predictions from state-of-the-art models, etc.Inference on large scale datasets comes with many challenges prevalent in distributed data processing. Attendees will learn how to efficiently run deep learning prediction on large data sets, leveraging Apache Spark and Apache MXNet (incubating). In this session, we’ll cover core Deep Learning Concepts such as: Types of Learning, a) Supervised Learning b) Unsupervised Learning c) Active Learning d) Reinforcement Learning Supervised Learning types – classification, regression, Image classification Types of Neural Networks – Feed forward Networks, CNNs, RNNs, GANs * Apache MXNet(Incubating) Deep Learning Framework. MXNet concepts ie., NDArray, Symbolic APIs and Module APIs. MXNet Gluon APIs * Distributed Inference using Apache MXNet and Apache Spark on Amazon EMR. In this section, I will cover some of the use-cases of Distributed Inference, the challenges associated with running distributed Inference.

1. Naveen Swamy
Distributed Deep Learning Inference
using Apache MXNet* and Apache Spark
Amazon AI
*

2. Outline
• Review of Deep Learning
• Apache MXNet Framework
• Distributed Inference using MXNet and Spark

3. Input layer
(Raw pixels)
Output
(object identity)
3rd hidden layer
(object parts)
2nd hidden layer
(corners & contours)
1st hidden layer
(edges)
• Originally inspired by our biological
neural systems.
• A System that learns important
features from experience.
• Layers of Neurons learning concepts.
• Deep learning != deep understanding
Deep Learning
Credit: Ian Goodfellow etal., Deep Learning Book
CAR PERSON DOG

4. Algorithmic Advances
(Faster Learning)
Abundance of Data
(Deeper Networks)
High Performance Compute
GPUs
(Faster Experiments)
Bigger and Better Models = Better AI Products

5. Why does Deep Learning matter?
Autonomous
Vehicles
Personal Assistants
Solve Intelligence ???
Health care

6. Deep Learning & AI, Limitations
Artificial Intelligence
DL Limitations:
• Requires lots of data and
compute power.
• Cannot detect Inherent bias in
data - Transparency.
• Uninterpretable Results.
Machine Learning
Deep
Learning

7. Deep Learning Training
forward
dog
dog
?
error
labels
data
backward
• Pass data through the network – forward pass
• Define an objective – Loss function
• Send the error back – backward pass
Model: Output of Training a neural network
X2 h2 w6 = 0.5
y = 1.0
y` = 0.9
loss = y – y`
l = 0.1
y
X1 h1
w2 = 0.5
w1 = 0.5
w3 = 0.5
w5 = 0.4
w
4
=
0.5
0.1
0.1
backward pass
forward pass

8. Deep Learning Inference
• Real time Inference: Tasks that require immediate result.
• Batch Inference: Tasks where you need to run on a large data sets.
o Pre-computations are necessary - Recommender Systems.
o Backfilling with state-of-the art models.
o Testing new models on historic data.
model
forward
dog

9. Types of Learning
• Supervised Learning – Uses labeled training data learning to
associate input data to output.
Example: Image classification, Speech Recognition, Machine translation
• Unsupervised Learning - Learns patterns from Unlabeled data.
Example: Clustering, Association discovery.
• Active Learning – Semi-supervised, human in the middle..
• Reinforcement Learning – learn from environment, using rewards and
feedback.

10. Outline
• Apache MXNet Framework
• Distributed Inference using MXNet and Spark

11. Why MXNet

12. MXNet – NDArray & Symbol
• NDArray– Imperative Tensor Operations that work on both CPU and
GPUs.
• Symbol APIs – similar to NDArray but adopts declarative programming
for optimization.
Computation GraphSymbolic Program

13. MXNet - Module
High level APIs to work with Symbol
1) Create Graph
2) Bind
3) Pass data

14. Outline
• Distributed Inference using MXNet and Spark

15. Distributed Inference
Challenges
• Similar to large scale data
processing systems
High Performance DL framework
Distributed Cluster
Resource Management
Job Management
Efficient Partition of Data
Deep Learning Setup
Apache Spark:
• Multiple Cluster Managers
• Works well with MXNet.
• Integrates with Hadoop & big data tools.

16. MXNet + Spark for Inference.
• ImageNet trained ResNet-18 classifier.
• For demo, CIFAR-10 test dataset with 10K Images.
• PySpark on Amazon EMR, MXNet is also available in Scala.
• Inference on CPUs, can be extended to use GPUs.

17. Distributed Inference Pipeline
download
S3 keys
on driver
create RDD
and
partition
fetch batch
of images
on executor
decode to
numpy array
run
prediction
collect
predictions
initialize model only once
mapPartitions

18. MXNet + Spark for Inference.
Onthedriver

19. Ontheexecutor

20. Summary
• Overview of Deep Learning
o How Deep Learning works and Why Deep Learning is a big deal.
o Phases of Deep Learning
o Types of Learning
• Apache MXNet – Efficient deep learning library
o NDArray/Symbol/Module
• Apache MXNet and Spark for distributed Inference.

21. What’s Next ?
• Released simplified Scala Inference APIs (v1.2.0)
oAvailable on Maven : org.apache.mxnet
• Working on Java APIs for Inference.
• Dataframe support is under consideration.
• MXNet community is fast evolving, join hands to democratize
AI.

22. Resources/References
• https://github.com/apache/incubator-mxnet
• Blog- Distributed Inference using MXNet and Spark
• Distributed Inference code sample on GitHub
• Apache MXNet Gluon Tutorials
• Apache MXNet – Flexible and efficient deep learning.
• The Deep Learning Book
• MXNet – Using pre-trained models
• Amazon Elastic MapReduce

23. Thank You
nswamy@apache.org