Tutorial on Massively Scalable Production Grade Deep Learning with the Microsoft Cognitive Toolkit *

Full-day hands-on tutorial at CIKM 2017, Tuesday 7 November 2017

What is this tutorial about?

Train neural networks like the Microsoft product groups do! This hands-on tutorial introduces the Microsoft Cognitive Toolkit (formerly known as CNTK), a scalable open-source deep-learning toolkit for Windows and Linux that has been used for Skype, Cortana, Bing, Xbox, and much more. For example, Microsoft product groups achieved a major breakthrough when they used CNTK to learn to recognize conversational speech as well as a human can. In general, CNTK trains models as fast or faster than its competitors and scales up well on modern GPUs (see the performance comparison from HKBU and recent demos from NVidia at SC’16 and Cray at NIPS’16). CNTK is also compatible with popular algorithms (feed-forward, convolutional, and recurrent networks) and languages, including APIs for Python and C++.

This tutorial is split into three sections. In Part 1, we introduce how the toolkit has been used in different domains within and outside Microsoft. We also show how it is being used by customers both on-premises and on Azure cloud. In Part 2, we dive into the structure of deep learning models and elaborate on CNTK’s optimizations that make it scale well across multiple servers and GPUs. Part 3 is a hands-on session where you will use Jupyter notebooks to train several types of deep neural networks, taken from use cases in the vision, text, time series and speech domains. In particular, you will work with residual networks using fully connected CNN, which achieved record breaking effectiveness in recognizing objects in the ImageNet challenge; RNN/LSTM for sequence-to-sequence and reading comprehension of text documents (ResoNet); autoencoders for semi-supervised learning; Generative Adversarial Networks (GAN) for unsupervised learning; and reinforcement learning for enabling machines to optimally perform tasks such as playing games. Network bandwidth permitting, we will also explore scalable learning on Azure with simple introductory to very advanced end-to-end use cases. Time permitting, we will discuss integration of CNTK with Spark for scaling out.

Keywords. Deep learning, large-scale distributed machine learning, online learning, text (sequence to sequence, reasoning networks), computer vision (ResNet, GANs, video) and reinforcement learning, cloud based distributed computing, convolutional neural networks, LSTMs, Recurrence, GAN, autoencoders, Azure, Spark

Materials. The tutorial’s code and data will be made available through Jupyter iPython notebooks; CNTK itself can be downloaded from GitHub. The hands-on sessions require a laptop with Windows 7+ or Linux, and a CUDA-capable GPU is recommended.

Who should attend?

Current or future deep-learning practitioners and researchers looking for a tool that is easy to use yet efficient and scalable across multi-machine GPU clusters for real-world workloads. In Part 1, those new to deep learning will gain an understanding of what deep learning can achieve and its applications in the field. Part 2 requires familiarity with basic concepts from programming, linear algebra, and probability. Part 3 requires basic programming skills. If you are not familiar with Python, you will be able to run the code and gain experience. If you are, you will be able to explore more on your own.

What will I get out of this tutorial?

If your background and interest are primarily non-technical, e.g. if you are an executive or business-oriented practitioner, you will learn how deep learning is becoming pervasive in different areas including how we are using it at Microsoft, as well as our external partners. If you are technically minded, you will learn how to perform classification and regression using supervised, semi-supervised and unsupervised technique. If you are an advanced user, you will be exposed to different advanced algorithms and learn to code, modify and scale using state-of-the-art techniques.

You will leave this tutorial with working recipes for the different use cases, along with the experience of running their jobs in Azure (network bandwidth permitting). Time permitting, you will also learn how to distribute the workload by integrating with Spark.

Detailed Outline

Tutorial on Large Scale Distributed Data Science from Scratch with Apache Spark 2.0 & Deep Learning *

Full-day hands-on tutorial at CIKM 2017, Wednesday 8 November 2017

What is this tutorial about?

In the continuing big data revolution, Apache Spark’s open-source cluster computing framework has overtaken Hadoop MapReduce as the big data processing engine of choice. Spark maintains MapReduce’s linear scalability and fault tolerance, but offers two key advantages: Spark is much faster – as much as 100x faster for certain applications; and Spark is much easier to program, due to its inclusion of APIs for Python, Java, Scala, SQL and R, plus its user-friendly core data abstraction, the distributed data frame. In addition, Spark goes far beyond traditional batch applications to support a variety of compute-intensive tasks, including interactive queries, streaming data, machine learning, and graph processing.

This tutorial offers you an accessible introduction to large-scale distributed machine learning and data mining, and to Spark and its potential to revolutionize academic and commercial data science practices. The tutorial includes discussions of algorithm design, presentation of illustrative algorithms, relevant case studies, and practical advice and experience in writing Spark programs and running Spark clusters. Part I familiarizes you with fundamental Spark concepts, including Spark Core, functional programming a la MapReduce, RDDs/data frames/datasets, the Spark Shell, Spark Streaming and online learning, Spark SQL, MLlib, and more. Part 2 gives you hands-on algorithmic design and development experience with Spark, including building algorithms from scratch such as decision tree learning, association rule mining (aPriori), graph processing algorithms such as PageRank and shortest path, gradient descent algorithms such as support vector machines and matrix factorization, distributed parameter estimation, and deep learning. Your homegrown implementations will shed light on the internals of Spark’s MLlib libraries and on typical challenges in parallelizing machine learning algorithms. You will see examples of industrial applications and deployments of Spark.

Keywords. Distributed systems, HDFS, Spark, Hadoop, large-scale distributed machine learning, online learning, deep learning, Spark Streaming, mobile advertising

Materials. You will receive electronic handouts and a web-based iPython Jupyter notebook with example code and data. You will deploy Spark on your own multicore laptop to run and develop examples there. So that you can rapidly provision remote Spark clusters on the fly, we plan to work with Amazon Web Services to provide you with free access to Amazon's Elastic Compute Cloud (EC2) during the tutorial.

Who should attend?

Industry practitioners and researchers who wish to learn the best practices for large scale data science using next generation tools. No prior familiarity with Spark, distributed systems, how to distribute algorithms, or large-scale machine learning is required.

What will I get out of this tutorial?

• Gain an integrated view of the data processing pipeline as the tutorial highlights its components, including exploratory data analysis, feature extraction, supervised learning, and model evaluation.




• Introduced to the underlying statistical and algorithmic principles required to develop scalable machine learning pipelines, and gain hands-on experience in applying these principles using Apache Spark, a cluster computing system well suited for large-scale machine learning tasks.




• Learn how to implement scalable algorithms for fundamental statistical models (linear regression, logistic regression, matrix factorization, principal component analysis) while tackling key data science problems from various domains: mobile advertising, personalized recommendation, and consumer segmentation.




• Learn about data intensive industrial applications and deployments of Spark in fields such as mobile advertising. You will leave with an understanding of scalability challenges and the tradeoffs associated with distributed processing of large datasets.

Detailed Outline