AI Conclave workshops Apache MXNet and Gluon talk

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Apach
e
11th December 2018
Soji Adeshina, Machine Learning Engineer, Amazon AI
Thomas Delteil, Machine Learning Scientist, Amazon AI
and Gluon

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MXNet Overview
Deep Learning framework
Computational Graph Automatic Differentiation Accelerator Offloading

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• GPUs have thousands of cores
• Can do matrix operations in parallel
• Ideal for Deep Learning: 100X faster than CPU
• NVIDIA GPUs with CUDA library commonly used
• Apache MXNet helps us manage
• Moving data to/from GPU(s)
• Doing GPU calculations
• CPU for data preprocessing, certain math functions
• Common Modality: Train on GPU, predict on CPU
MXNet Overview
GPU Acceleration

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MXNet Overview
Highlights
Scalable Debuggable
Optimized
librariesFlexible
7 frontend
languages Portable
Speech Bubble by Weltenraser, Scale by Ben Davis, Bug by Nociconist, Mobile by Rafael Garcia Motta, flexible by AdbA Icons from the Noun Project

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• FlexibleAPIs
• Bindings: Python, Scala, C, C++, R, Clojure
• Examples and tutorials
Ease of Use
• Optimized for CPU, GPU,ARM (and more)
• Highly scalable distributed training
• Quantization, Sparse, NCCL, and more…
Performance
• Train on cloud, predict on edge
• Model serving framework
• ONNX support
Portability
MXNet Overview
Detailed Highlights

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Quick History
Since 2015

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Originated from DMLC
Distributed Machine Learning Community
& many more…
XGBoost MinPy
MXNet

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Original Paper in 2015
https://arxiv.org/pdf/1512.01274.pdf

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Authors
Tianqi Chen UW Mu Li
Amazon AI
Yutian Li
Stanford
Min Lin
MILA
Naiyan Wang
TuSimple
Minjie Wang
NYU CS
Tianjun Xiao
Tesla
Bing Xu
Apple AI
Chiyuan Zhang
Google Brain
Zheng Zhang
MSR Asia

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Major Events
Amazon’s deep-learning framework
of choice
since November 2016.
Accepted into
Apache Incubator
in January 2017.

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Ecosystem & Community

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Multi-language Support
C++
C++
ClojureJuliaJavaR
ScalaPython
Frontend
Backend
While keeping high performance from efficient backend

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Ecosystem
MXNet
MXBoard
Model
Server
GluonCV
GluonNLP
ONNX Model Zoo
Keras
TVM

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• Tensor Representation with ndarray API
• Predefined Neural Network Layers
• Automatic Differentiation with autograd API
• Neural Network Training Algorithms and Optimizers with
gluon.Trainer
• Pretrained Neural Networks from gluon Model Zoo
Gluon
Imperative API for MXNet that’s flexible and easy-to-use

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Distributed training with MXNet
KVStore1
Worker1
KVStore2 KVStorem
Worker2 Workern
…
…
trainer = gluon.Trainer(net.collect_params(), 'sgd’,
{'learning_rate': hyperparameters['learning_rate’]},
kvstore=kvstore)
KVStore Modes
a. dist_sync
b. dist_async
c. dist_sync_device
d. dist_async_device

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Keeping Up to Date
Medium: https://medium.com/apache-mxnet

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Keeping Up to Date: Social
YouTube: /apachemxnet
Twitter: @apachemxnet
Reddit: r/mxnet

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Community
GitHub: https://github.com/apache/incubator-mxnet

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Community
Discuss Forum: https://discuss.mxnet.io/