Accelerating Parallel Training of Neural Nets

Earlier this year, we reported a speech recognition system trained on a million hours of data, a feat possible through semi-supervised learning, in which training data is annotated by machines rather than by people.

These sorts of massive machine learning projects are becoming more common, and they require distributing the training process across multiple processors. Otherwise, training becomes too time consuming.

In theory, doubling the number of processors should halve the training time, but in practice, it doesn’t work that way: the need to synchronize distributed computations results in some inevitable communication overhead. Parallelization schemes always involve some trade-off between training efficiency and the accuracy of the resulting model.

In a paper we’re presenting at this year’s Interspeech, we describe a new approach to parallelizing the training of neural networks that combines two state-of-the-art methods and improves on both. One of the existing methods prioritizes model accuracy, and the other prioritizes training efficiency. In tests that involved training an acoustic model — a vital component of a speech recognition system — our hybrid method proved both more accurate than the accuracy-prioritizing method and more efficient than the efficiency-prioritizing method.

The accuracy-prioritizing method was proposed in 2015 by Amazon senior principal scientist Nikko Strom, who’s also a coauthor on the new paper. At the time, the method — known as synchronous GTC, for gradient threshold compression — was a breakthrough, enabling parallelization to as many as 32 processors with little loss in model accuracy. Our experiments indicate, however, that beyond 32 processors, communications overhead can make GTC much less efficient.

When we needed to increase the processor count to handle a million hours of data, we switched to a different parallelization method, called BMUF, for blockwise model update filtering. BMUF scales better than GTC, but at the price of accuracy. In experiments we report in our new paper, at 64 processors, BMUF triples the training rate achievable with GTC — but it also triples the loss in accuracy.

In our new work, we split the distributed processors into groups, and each group performs GTC within itself. Every so often, however, the groups share their latest models using BMUF, which re-synchronizes models across all processors.

BMUF-GTC.gif._CB436386414_ (1).gif
Our new method splits distributed processors into groups, and within each group, the processors use the highly accurate GTC method to synchronize their models. At regular intervals, designated representatives from all the groups share their models and update their own local models accordingly. Finally, each representative broadcasts its updated model to the rest of its group.
Animation by Nick Little

A neural network consists of thousands or even millions of very simple processors, often called neurons. Each neuron receives data from several other neurons, processes it, and passes the results to still other neurons. Connections between neurons have associated weights, which determine how big a role the outputs of one neuron play in the computations performed by the next.

During training, a network will process a small amount of data, known as a minibatch, evaluate the results, and update its weights accordingly. Then it will process the next minibatch, and so on.

The natural way to parallelize this procedure is to give each processor its own copy of the initial network weights and its own set of minibatches. After each minibatch, each processor will broadcast the updates it needs to make to its weights (technically, “gradients”) to all the other processors. A given processor simply combines all the updates it receives and applies them to its own, local copy of the network. Then it processes its next minibatch.

This method is equivalent to training a model on a single processor. But broadcasting a full set of weight updates after each minibatch is so bandwidth intensive that it eats up the time savings from parallelization. GTC modifies this procedure in a few crucial ways.

First, it exploits the fact that the weight updates that follow the processing of a single minibatch tend to make major modifications to only a few neural connections. GTC requires the establishment of a threshold — the T in GTC — below which weight updates will not be broadcast, saving bandwidth. (Weights that fall below the threshold are, however, stored locally, where they may still factor into later computations.)

The weight threshold, denoted by the Greek letter tau, is determined empirically and varies from application to application. In our experiments, the optimal setting of tau turned out to be 8.

Next, when broadcasting its weight updates, each processor sends only one of two values, tau or -tau. Those two values can be represented by a single bit of information, compressing (the C in GTC) the update message. (Like updates that fall below the threshold, the residuals of weights above the threshold are stored locally and factor into later computations.)

These two modifications do sacrifice some accuracy. That’s why, in our experiments, we evaluate relative increase in error rate. All three methods we compare — GTC, BMUF, and our GTC-BMUF hybrid — increase error rate. The question is how much, and what we gain in efficiency in exchange.

With BMUF, each processor continually updates its own local copy of the neural model. After a fixed number of minibatches — say, 50 — it broadcasts its model, and all the processors update their models accordingly. This drastically cuts down on bandwidth consumption, but it decreases model accuracy.

By combining these approaches — GTC locally, BMUF globally — we get the best of both worlds. On the acoustic-modeling task we used for testing, our hybrid is slightly less efficient than BMUF at 32 cores, offering a 31-fold speedup relative to single-core processing; BMUF’s speedup is actually superlinear, at 36.6-fold. But our method reduces the error rate, while BMUF increases it by 3.5%. GTC, with a 26-fold speedup, increases error rate by 1.4%.

At 64 cores, GTC offers the best accuracy, with an error rate increase of 2.8%, versus 3.1% for our method and 8.9% for BMUF. But GTC’s efficiency actually falls, to 17-fold, versus 57-fold for BMUF and 42-fold for our method.

At 128 cores, our method is the undisputed champion, offering a 97-fold speedup and a 4.7% error rate increase, to 80-fold/9.6% for BMUF and 11-fold/15.6% for GTC.

About the Author
Pranav Ladkat is a research engineer in Alexa AI’s Machine Learning Platform Services group.

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The GSF (Global Specialty Fulfillment) organization leads the innovation of Amazon’s ultra-fast fulfillment initiatives. We are an Operations org that hires and manages associates for ultra-fast businesses such as online grocery delivery, sub-same day delivery etc. GSFTech sits within GSF with the mission to build world-class automated Science-Tech products that enable ultra-fast delivery speeds for Amazon customers and job market opportunities for Amazon associates. Our key vision is to transform the online experience. We’re growing in scale and volume, by orders of magnitude. We are a team of passionate tech builders who work endlessly to make life better for our associates through amazing, thoughtful, and creative new scheduling experiences. To succeed, we need senior technical leaders to forge a path into the future by building innovative, maintainable, and scalable systems.At Amazon, we are constantly inventing and re-inventing to be the most associate-centric company in the world. To get there, we need exceptionally talented, bright, and driven people. Amazon is one of the most recognizable brand names in the world and we distribute millions of products each year to our loyal customers.We are looking for a Senior Applied Scientist who will be the science lead for all key ML and forecasting initiatives, responsible for building models and prototypes for labor planning systems, and will require close collaboration with other scientists on the team that are developing state-of-the-art optimization algorithms to scale. This team plays a significant role in various stages of the innovation pipeline from identifying business needs, developing new algorithms, prototyping/simulation, to implementation by working closely with colleagues in engineering, product management, operations, retail and finance.As a Senior member of the scientist team, you will play an integral part on our Operations org with the following technical and leadership responsibilities:· Help the team define the forward looking Science roadmap and vision by helping to identify, disambiguate and seek out new opportunities· Interact with engineering, operations, science and business teams to develop an understanding and domain knowledge of processes, system structures, and business requirements· Apply domain knowledge and business judgment to identify opportunities and quantify the impact aligning research direction to business requirements and make the right judgment on research project prioritization· Develop scalable models to derive optimal or near-optimal solutions to existing and new scheduling challenges· Create prototypes and simulations to test devised solutions· Advocate technical solutions to business stakeholders, engineering teams, as well as executive-level decision makers· Work closely with engineers to integrate prototypes into production system· Create policy evaluation methods to track the actual performance of devised solutions in production systems, identify areas with potential for improvement and work with internal teams to improve the solution with new features· Mentor and supervise the work of junior scientists on the team for technical development and their career development and growth· Present business cases and document models, analyses, and their results in order to influence important decisions
CA, ON, Toronto
Job summaryAmazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day!Sponsored Products helps merchants, retail vendors, and brand owners succeed via native advertising that grows incremental sales of their products sold through Amazon. The Sponsored Products Ad Marketplace organization optimizes the systems and ad placements to match advertiser demand with publisher supply using a combination of machine learning, big data analytics, ultra-low latency high-volume engineering systems, and quantitative product focus. Our systems and algorithms operate on one of the world's largest product catalogs, matching shoppers with products - with a high relevance bar and strict latency constraints. Our goals are to help buyers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and to build a major, sustainable business that helps Amazon continuously innovate on behalf of all customers.As an Applied Scientist for the Sponsored Products Detail Page Allocation and Pricing team, you develop systems which make the final decision on which ads to show, where to place them on the page and how many ads to place. This also includes selection of various themes that would appear in detail pages. This is a challenging technical and business problem, which requires us to balance the interests of advertisers, shoppers, and Amazon. You'll develop a data-driven product strategy to define the right quantitative measures of shopper impact, using this to evaluate decisions and opportunities. You'll balance a portfolio of pragmatic and long-term investments to drive long term growth of the ads and retail businesses. You'll develop real-time algorithms to allocate billions of ads per day in advertising auctions.As an Applied Scientist on this team you will:Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production.Run A/B experiments, gather data, and perform statistical analysis.Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.Work closely with software engineers to assist in productionizing your ML models.Research new machine learning approaches.Why you love this opportunityAmazon is investing heavily in building a world-class advertising business. This team is responsible for defining and delivering a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate.Impact and Career GrowthYou will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven fundamentally from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.Team video https://youtu.be/zD_6Lzw8raE
US, WA, Seattle
Job summaryDo you want to join the Alexa Artificial Intelligence (AI) team - the science team behind Amazon’s intelligence voice assistance system? Do you want to utilize cutting-edge deep-learning and machine learning algorithms to delight millions of Alexa users around the world?If your answers to these questions are “yes”, then come join the Alexa AI team, which is in charge of improving Alexa user satisfaction through continuous closed-loop self-learning. The team owns the modules that reduce user perceived defects through automatic defect detection and label generation.Key job responsibilitiesYou will be expected to:· Analyze, understand, and model dialogue context based on large scale speech and dialogue data;· Create and innovate deep learning and/or NLP based algorithms for improving accuracy of Alexa's speech recognition and natural language understanding through contextual modeling;· Perform model/data analysis and monitor user-experienced based metrics through online A/B testing;· Research and implement novel deep learning and NLP algorithms and models.A day in the life· Work collaboratively with scientists and developers to design and implement automated, scalable NLP/ML/IR models for accessing and presenting information· Drive scalable solutions from the business, to prototyping, production testing and through engineering directly to production· Drive best practices on the team, deal with ambiguity and competing objectives, and mentor and guide junior members to achieve their career growth potential.About the teamThe Alexa AI team is in charge of improving Alexa user satisfaction through continuous closed-loop self-learning. The team owns the modules that reduce user perceived defects through automatic defect detection and label generation.You will be working alongside a team of experienced deep learning and NLP scientists and engineers to create deep neural network based contextual dialogue modeling on tasks such as speech translation, natural language understanding, etc.
GB, London
Job summaryCome build the future of entertainment with us. Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching?Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on.As part of the Prime Video Automated Excellence organization, the Automated Reasoning team applies deep and cutting-edge automated reasoning techniques to detect defects automatically in Prime Video’s core systems and device-level code. The tools we build are mission-critical to the software development and release cycle of many Prime Video engineering organizations, and will represent a huge step forward in the sophistication of our approach to automated software quality. Your work on this team will help us address a new dimension of scale our business faces as we deliver our applications on an ever-expanding set of client devices.Key job responsibilitiesYou will have the opportunity to apply your deep knowledge of automated reasoning techniques, such as static analysis, formal verification, symbolic execution, etc., to concrete problems our product and engineering teams face on a daily basis. You will collaborate with team members to design and deliver enterprise-scale systems that will be used by both internal and external customers. You will have the opportunity to analyse and verify code to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept. You will help set and continuously evolve a culture of innovation and curiosity that helps us find and solve our customers’ biggest problems.About the teamTo help a growing organization quickly deliver more features to Prime Video customers, Prime Video’s Automated Excellence organization is innovating on behalf of our global software development team consisting of thousands of engineers. We build services and utilities that make developer’s lives easier and more productive, and that help them deliver at higher levels of quality.
US, CA, Sunnyvale
Job summaryThe Amazon Alexa app is a companion to Alexa devices for setup, remote control, and enhanced features. The Alexa app understands a customer’s habits, preferences and delivers a personalized experience to help them manage their day by providing relevant information as customers want it. We believe voice is the most natural user interface for interacting with technology across many domains; we are inventing the future. As voice-enabled technology becomes increasingly advanced, consumers are demanding more from what their voice products can do. We’re looking for Scientists who are passionate about innovating on behalf of customers, demonstrate a high degree of product ownership, and want to have fun while they make history.As an Senior Data Scientist, you will help build a production scaled personalized recommendation and lead the team to build Machine Learning (ML) and Deep Learning (DL) models to help derive business value and new insights through the adoption of Artificial Intelligence (AI).Key job responsibilitiesThe successful candidate will be responsible for distilling user data insights for ML science applications and influence business decision with data-driven approach to increase Alexa mobile engagement and growth. A successful candidate will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term solutions.· Define the long-term development, science and business strategies for the team.· Expertise in the areas of data science, machine learning and statistics.· Translate business needs into advanced analytics and machine learning models and provide strong algorithm and coding execution and delivery of Machine Learning & Artificial Intelligence.· Work closely with the engineers to architect and develop the best technical design and approach.· Being able to dive a ML / DL project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization.· Analyze, extract, normalize, and label relevant data.· Work with Engineers to help our customers operationalize models after they are built.A day in the life· Design and review mobile experiments for growth and engagement· Build statistical models and generate data insights to understand mobile growth and retention· Feature engineering to improve ML model performance.· Analyze, extract, normalize, and label relevant data.· Work with Engineers to deploy applications to production· Work with product manager to convert business problems to science problems and define the solutions.About the teamAlexa Mobile Intelligence team is motivated to make Alexa mobile app being the best intelligent assistant and providing personalized relevant features and content by understanding customers' habits, preferences, hence will reach high growth and retention for the app.
DE, BE, Berlin
Are you excited about building Robotics AI technology that works seamlessly with and around people? The Robotics AI team at Amazon is building high-performance, real-time robotic systems that can perceive, learn and act intelligently alongside humans, at Amazon scale.To this end, we are seeking an experienced Applied Science Manager who is interested in leading a team of Applied Scientist to bring Computer Vision innovations to Fulfillment Centers. We work on machine learning, planning, control, simulation and computer vision applied to robotics, particularly to manipulation and item understanding. We are expanding our Berlin team to meet the huge application demand in Amazon.Key responsibilities:· Lead a team of Computer Vision experts and oversee research and development projects at various stages ranging from initial exploration to deployment into production systems.· Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment.· Collaborate with software engineering teams to integrate successful experiments into large scale, highly complex production services.· Report results in a scientifically rigorous way.· Collaborate closely with stakeholders on developing systems from prototyping to production level.
US, WA, Seattle
Come join a team to work in the intersection of Machine Learning, Observability, Cloud, Big Data and Open Source!The AWS CloudWatch Predictions team produces Anomaly Detection solution that gives customers actionable visibility into the health of their applications and services by leveraging machine learning technologies. Our service continuously analyzes system and application metrics, detects and surfaces anomalies without requiring user intervention, enables AWS customers across the world to monitor and act on the dynamic nature of system and application behaviors.We are looking for applied scientists to help us lend meaning to vast amounts of time series data and delight our customers by finding the reasonable answers to the right problems. If you enjoyed your studies of Time Series Modeling and Predicting, Anomaly Detection Algorithms, Data Smoothing, Outlier Filtering, and innovating new features that can make a huge impact on the customer experience excites you, then we've got a good home for you here.You'll have a ground floor opportunity to work on cutting edge ways for online time series forecasting and anomaly detection, and improve the the use of advanced Machine Learning on massive scale datasets. You'll join a team of veteran service engineers who are focused on highly stable, low operational burden, continuously deployed software and help lead a new quantitative effort. It'll be an opportunity to not just change how the world understands their compute resources, but the opportunity to build world class distributed system software engineering skills as well.