This document summarizes Baidu's work on scaling deep learning using Spark. It discusses:
1) Baidu's goals of implementing Spark ML abstractions to train deep learning models with minimal code changes and leveraging Paddle's distributed training capabilities.
2) The system architecture which uses Spark and YARN for resource management and Paddle for distributed training.
3) Experiments showing Paddle can efficiently train large models like image recognition on ImageNet using many machines and GPUs, as well as sparse models.
- A brief introduction to Spark Core
- Introduction to Spark Streaming
- A Demo of Streaming by evaluation top hashtags being used
- Introduction to Spark MLlib
- A Demo of MLlib by building a simple movie recommendation engine
- A brief introduction to Spark Core
- Introduction to Spark Streaming
- A Demo of Streaming by evaluation top hashtags being used
- Introduction to Spark MLlib
- A Demo of MLlib by building a simple movie recommendation engine
Deep Learning to Production with MLflow & RedisAIDatabricks
Taking deep learning models to production and doing so reliably is one of the next frontiers of MLOps. With the advent of Redis modules and the availability of C APIs for the major deep learning frameworks, it is now possible to turn Redis into a reliable runtime for deep learning workloads, providing a simple solution for a model serving microservice. RedisAI is shipped with several cool features such as support for multiple frameworks, CPU and GPU backend, auto batching, DAGing, and soon will be with automatic monitoring abilities. In this talk, we'll explore some of these features of RedisAI and see how easy it is to integrate MLflow and RedisAI to build an efficient productionization pipeline.
For a Python driven Data Science team, DASK presents a very obvious logical next step for distributed analysis. However, today the de-facto standard choice for exact same purpose is Apache Spark. DASK is a pure Python framework, which does more of same i.e. it allows one to run the same Pandas or NumPy code either locally or on a cluster. Whereas, Apache Spark brings about a learning curve involving a new API and execution model although with a Python wrapper. Given the above statement, do we even need to compare and contrast to make a choice? Shouldn't DASK be the default choice? Well, that's what this session is about. It goes in detail explaining the various viewpoints and dimensions that need to be considered to pick one over other.
Willump: Optimizing Feature Computation in ML InferenceDatabricks
Systems for performing ML inference are increasingly important, but are far slower than they could be because they use techniques designed for conventional data serving workloads, neglecting the statistical nature of ML inference. As an alternative, this talk presents Willump, an optimizer for ML inference.
Building Deep Reinforcement Learning Applications on Apache Spark with Analyt...Databricks
Deep Reinforcement Learning (DRL) is a thriving area in the current AI battlefield. AlphaGO by DeepMind is a very successful application of DRL which has drawn the attention of the entire world. Besides playing games, DRL also has many practical use in industry, e.g. autonomous driving, chatbots, financial investment, inventory management, and even recommendation systems. Although DRL applications has something in common with supervised Computer Vision or Natural Language Processing tasks, they are unique in many ways.
For example, they have to interact (explore) with the environment to obtain training samples along the optimization, and the method to improve the model is usually different from common supervised applications. In this talk we will share our experience of building Deep Reinforcement Learning applications on BigDL/Spark. BigDL is a well-developed deep learning library on Spark which is handy for Big Data users, but it has been mostly used for supervised and unsupervised machine learning. We have made extensions particularly for DRL algorithms (e.g. DQN, PG, TRPO and PPO, etc.), implemented classical DRL algorithms, built applications with them and did performance tuning. We are happy to share what we have learnt during this process.
We hope our experience will help our audience learn how to build a RL application on their own for in their production business.
Choose Your Weapon: Comparing Spark on FPGAs vs GPUsDatabricks
Today, general-purpose CPU clusters are the most widely used environment for data analytics workloads. Recently, acceleration solutions employing field-programmable hardware have emerged providing cost, performance and power consumption advantages. Field programmable gate arrays (FPGAs) and graphics processing units (GPUs) are two leading technologies being applied. GPUs are well-known for high-performance dense-matrix, highly regular operations such as graphics processing and matrix manipulation. FPGAs are flexible in terms of programming architecture and are adept at providing performance for operations that contain conditionals and/or branches. These architectural differences have significant performance impacts, which manifest all the way up to the application layer. It is therefore critical that data scientists and engineers understand these impacts in order to inform decisions about if and how to accelerate.
This talk will characterize the architectural aspects of the two hardware types as applied to analytics, with the ultimate goal of informing the application programmer. Recently, both GPUs and FPGAs have been applied to Apache SparkSQL, via services on Amazon Web Services (AWS) cloud. These solutions’ goal is providing Spark users high performance and cost savings. We first characterize the key aspects of the two hardware platforms. Based on this characterization, we examine and contrast the sets and types of SparkSQL operations they accelerate well, how they accelerate them, and the implications for the user’s application. Finally, we present and analyze a performance comparison of the two AWS solutions (one FPGA-based, one GPU-based). The tests employ the TPC-DS (decision support) benchmark suite, a widely used performance test for data analytics.
Improving the Life of Data Scientists: Automating ML Lifecycle through MLflowDatabricks
The data science lifecycle consists of multiple iterative steps: data collection, data cleaning/exploration, feature engineering, model training, model deployment and scoring among others. The process is often tedious and error-prone and requires considerable human effort. Apart from these challenges, when it comes to leveraging ML in enterprise applications, especially in regulated environments, the level of scrutiny for data handling, model fairness, user privacy, and debuggability is very high. In this talk, we present the basic features of Flock, an end-to-end platform that facilitates adoption of ML in enterprise applications. We refer to this new class of applications as Enterprise Grade Machine Learning (EGML). Flock leverages MLflow to simplify and automate some of the steps involved in supporting EGML applications, allowing data scientists to spend most of their time on improving their ML models. Flock makes use of MLflow for model and experiment tracking but extends and complements it by providing automatic logging, deeper integration with relational databases that often store confidential data, model optimizations and support for the ONNX model format and the ONNX Runtime for inference. We will also present our ongoing work on automatically tracking lineage between data and ML models which is crucial in regulated environments. We will showcase Flock’s features through a demo using Microsoft’s Azure Data Studio and MLflow.
Simplify Distributed TensorFlow Training for Fast Image Categorization at Sta...Databricks
"In addition to the many data engineering initiatives at Starbucks, we are also working on many interesting data science initatives. The business scenarios involved in our deep learning initatives include (but are not limited to) planogram analysis (layout of our stores for efficient partner and customer flow) to predicting product pairings (e.g. purchase a caramel machiato and perhaps you would like caramel brownie) via the product components using graph convolutional networks.
For this session, we will be focusing on how we can run distributed Keras (TensorFlow backend) training to perform image analytics. This will be combined with MLflow to showcase the data science lifecycle and how Databricks + MLflow simplifies it. "
Using SparkML to Power a DSaaS (Data Science as a Service) with Kiran Muglurm...Databricks
Almost all organizations now have a need for data science and, as such, the main challenge after determining the algorithm is to scale it up and make it operational. Comcast uses several tools and technologies such as Python, R, SaS, H2O and so on. In this session, they’ll show how many common use cases use the common algorithms like Logistic Regression, Random Forest, Decision Trees, Clustering, NLP, etc.
Apache Spark has several machine learning algorithms built in and has excellent scalability. Hence, at Comcast, they built a platform to provide DSaaS on top of Spark with REST API as a means of controlling and submitting jobs, so as to abstract most users from the rigor of writing (repeating) code, instead focusing on the actual requirements.
Learn how they solved some of the problems of establishing feature vectors, choosing algorithms and then deploying models into production. They’ll also showcase their use of Scala, R and Python to implement models using language of choice yet deploying quickly into production on 500-node Spark clusters.
Zeus: Uber’s Highly Scalable and Distributed Shuffle as a ServiceDatabricks
Zeus is an efficient, highly scalable and distributed shuffle as a service which is powering all Data processing (Spark and Hive) at Uber. Uber runs one of the largest Spark and Hive clusters on top of YARN in industry which leads to many issues such as hardware failures (Burn out Disks), reliability and scalability challenges.
Deep Learning with DL4J on Apache Spark: Yeah it’s Cool, but are You Doing it...Databricks
DeepLearning4J (DL4J) is a powerful Open Source distributed framework that brings Deep Learning to the JVM (it can serve as a DIY tool for Java, Scala, Clojure and Kotlin programmers). It can be used on distributed GPUs and CPUs. It is integrated with Hadoop and Apache Spark. ND4J is a Open Source, distributed and GPU-enabled library that brings the intuitive scientific computing tools of the Python community to the JVM. Training neural network models using DL4J, ND4J and Spark is a powerful combination, but it presents some unexpected issues that can compromise performance and nullify the benefits of well written code and good model design. In this talk I will walk through some of those problems and will present some best practices to prevent them, coming from lessons learned when putting things in production.
Resource-Efficient Deep Learning Model Selection on Apache SparkDatabricks
Deep neural networks (deep nets) are revolutionizing many machine learning (ML) applications. But there is a major bottleneck to broader adoption: the pain of model selection.
Data-Driven Transformation: Leveraging Big Data at Showtime with Apache SparkDatabricks
Interested in learning how Showtime is leveraging the power of Spark to transform a traditional premium cable network into a data-savvy analytical competitor? The growth in our over-the-top (OTT) streaming subscription business has led to an abundance of user-level data not previously available. To capitalize on this opportunity, we have been building and evolving our unified platform which allows data scientists and business analysts to tap into this rich behavioral data to support our business goals. We will share how our small team of data scientists is creating meaningful features which capture the nuanced relationships between users and content; productionizing machine learning models; and leveraging MLflow to optimize the runtime of our pipelines, track the accuracy of our models, and log the quality of our data over time. From data wrangling and exploration to machine learning and automation, we are augmenting our data supply chain by constantly rolling out new capabilities and analytical products to help the organization better understand our subscribers, our content, and our path forward to a data-driven future.
Authors: Josh McNutt, Keria Bermudez-Hernandez
MALT: Distributed Data-Parallelism for Existing ML Applications (Distributed ...asimkadav
Machine learning methods, such as SVM and neural net- works, often improve their accuracy by using models with more parameters trained on large numbers of examples. Building such models on a single machine is often impracti- cal because of the large amount of computation required.
We introduce MALT, a machine learning library that inte- grates with existing machine learning software and provides data parallel machine learning. MALT provides abstractions for fine-grained in-memory updates using one-sided RDMA, limiting data movement costs during incremental model up- dates. MALT allows machine learning developers to specify the dataflow and apply communication and representation optimizations. Through its general-purpose API, MALT can be used to provide data-parallelism to existing ML appli- cations written in C++ and Lua and based on SVM, ma- trix factorization and neural networks. In our results, we show MALT provides fault tolerance, network efficiency and speedup to these applications.
Deep Learning to Production with MLflow & RedisAIDatabricks
Taking deep learning models to production and doing so reliably is one of the next frontiers of MLOps. With the advent of Redis modules and the availability of C APIs for the major deep learning frameworks, it is now possible to turn Redis into a reliable runtime for deep learning workloads, providing a simple solution for a model serving microservice. RedisAI is shipped with several cool features such as support for multiple frameworks, CPU and GPU backend, auto batching, DAGing, and soon will be with automatic monitoring abilities. In this talk, we'll explore some of these features of RedisAI and see how easy it is to integrate MLflow and RedisAI to build an efficient productionization pipeline.
For a Python driven Data Science team, DASK presents a very obvious logical next step for distributed analysis. However, today the de-facto standard choice for exact same purpose is Apache Spark. DASK is a pure Python framework, which does more of same i.e. it allows one to run the same Pandas or NumPy code either locally or on a cluster. Whereas, Apache Spark brings about a learning curve involving a new API and execution model although with a Python wrapper. Given the above statement, do we even need to compare and contrast to make a choice? Shouldn't DASK be the default choice? Well, that's what this session is about. It goes in detail explaining the various viewpoints and dimensions that need to be considered to pick one over other.
Willump: Optimizing Feature Computation in ML InferenceDatabricks
Systems for performing ML inference are increasingly important, but are far slower than they could be because they use techniques designed for conventional data serving workloads, neglecting the statistical nature of ML inference. As an alternative, this talk presents Willump, an optimizer for ML inference.
Building Deep Reinforcement Learning Applications on Apache Spark with Analyt...Databricks
Deep Reinforcement Learning (DRL) is a thriving area in the current AI battlefield. AlphaGO by DeepMind is a very successful application of DRL which has drawn the attention of the entire world. Besides playing games, DRL also has many practical use in industry, e.g. autonomous driving, chatbots, financial investment, inventory management, and even recommendation systems. Although DRL applications has something in common with supervised Computer Vision or Natural Language Processing tasks, they are unique in many ways.
For example, they have to interact (explore) with the environment to obtain training samples along the optimization, and the method to improve the model is usually different from common supervised applications. In this talk we will share our experience of building Deep Reinforcement Learning applications on BigDL/Spark. BigDL is a well-developed deep learning library on Spark which is handy for Big Data users, but it has been mostly used for supervised and unsupervised machine learning. We have made extensions particularly for DRL algorithms (e.g. DQN, PG, TRPO and PPO, etc.), implemented classical DRL algorithms, built applications with them and did performance tuning. We are happy to share what we have learnt during this process.
We hope our experience will help our audience learn how to build a RL application on their own for in their production business.
Choose Your Weapon: Comparing Spark on FPGAs vs GPUsDatabricks
Today, general-purpose CPU clusters are the most widely used environment for data analytics workloads. Recently, acceleration solutions employing field-programmable hardware have emerged providing cost, performance and power consumption advantages. Field programmable gate arrays (FPGAs) and graphics processing units (GPUs) are two leading technologies being applied. GPUs are well-known for high-performance dense-matrix, highly regular operations such as graphics processing and matrix manipulation. FPGAs are flexible in terms of programming architecture and are adept at providing performance for operations that contain conditionals and/or branches. These architectural differences have significant performance impacts, which manifest all the way up to the application layer. It is therefore critical that data scientists and engineers understand these impacts in order to inform decisions about if and how to accelerate.
This talk will characterize the architectural aspects of the two hardware types as applied to analytics, with the ultimate goal of informing the application programmer. Recently, both GPUs and FPGAs have been applied to Apache SparkSQL, via services on Amazon Web Services (AWS) cloud. These solutions’ goal is providing Spark users high performance and cost savings. We first characterize the key aspects of the two hardware platforms. Based on this characterization, we examine and contrast the sets and types of SparkSQL operations they accelerate well, how they accelerate them, and the implications for the user’s application. Finally, we present and analyze a performance comparison of the two AWS solutions (one FPGA-based, one GPU-based). The tests employ the TPC-DS (decision support) benchmark suite, a widely used performance test for data analytics.
Improving the Life of Data Scientists: Automating ML Lifecycle through MLflowDatabricks
The data science lifecycle consists of multiple iterative steps: data collection, data cleaning/exploration, feature engineering, model training, model deployment and scoring among others. The process is often tedious and error-prone and requires considerable human effort. Apart from these challenges, when it comes to leveraging ML in enterprise applications, especially in regulated environments, the level of scrutiny for data handling, model fairness, user privacy, and debuggability is very high. In this talk, we present the basic features of Flock, an end-to-end platform that facilitates adoption of ML in enterprise applications. We refer to this new class of applications as Enterprise Grade Machine Learning (EGML). Flock leverages MLflow to simplify and automate some of the steps involved in supporting EGML applications, allowing data scientists to spend most of their time on improving their ML models. Flock makes use of MLflow for model and experiment tracking but extends and complements it by providing automatic logging, deeper integration with relational databases that often store confidential data, model optimizations and support for the ONNX model format and the ONNX Runtime for inference. We will also present our ongoing work on automatically tracking lineage between data and ML models which is crucial in regulated environments. We will showcase Flock’s features through a demo using Microsoft’s Azure Data Studio and MLflow.
Simplify Distributed TensorFlow Training for Fast Image Categorization at Sta...Databricks
"In addition to the many data engineering initiatives at Starbucks, we are also working on many interesting data science initatives. The business scenarios involved in our deep learning initatives include (but are not limited to) planogram analysis (layout of our stores for efficient partner and customer flow) to predicting product pairings (e.g. purchase a caramel machiato and perhaps you would like caramel brownie) via the product components using graph convolutional networks.
For this session, we will be focusing on how we can run distributed Keras (TensorFlow backend) training to perform image analytics. This will be combined with MLflow to showcase the data science lifecycle and how Databricks + MLflow simplifies it. "
Using SparkML to Power a DSaaS (Data Science as a Service) with Kiran Muglurm...Databricks
Almost all organizations now have a need for data science and, as such, the main challenge after determining the algorithm is to scale it up and make it operational. Comcast uses several tools and technologies such as Python, R, SaS, H2O and so on. In this session, they’ll show how many common use cases use the common algorithms like Logistic Regression, Random Forest, Decision Trees, Clustering, NLP, etc.
Apache Spark has several machine learning algorithms built in and has excellent scalability. Hence, at Comcast, they built a platform to provide DSaaS on top of Spark with REST API as a means of controlling and submitting jobs, so as to abstract most users from the rigor of writing (repeating) code, instead focusing on the actual requirements.
Learn how they solved some of the problems of establishing feature vectors, choosing algorithms and then deploying models into production. They’ll also showcase their use of Scala, R and Python to implement models using language of choice yet deploying quickly into production on 500-node Spark clusters.
Zeus: Uber’s Highly Scalable and Distributed Shuffle as a ServiceDatabricks
Zeus is an efficient, highly scalable and distributed shuffle as a service which is powering all Data processing (Spark and Hive) at Uber. Uber runs one of the largest Spark and Hive clusters on top of YARN in industry which leads to many issues such as hardware failures (Burn out Disks), reliability and scalability challenges.
Deep Learning with DL4J on Apache Spark: Yeah it’s Cool, but are You Doing it...Databricks
DeepLearning4J (DL4J) is a powerful Open Source distributed framework that brings Deep Learning to the JVM (it can serve as a DIY tool for Java, Scala, Clojure and Kotlin programmers). It can be used on distributed GPUs and CPUs. It is integrated with Hadoop and Apache Spark. ND4J is a Open Source, distributed and GPU-enabled library that brings the intuitive scientific computing tools of the Python community to the JVM. Training neural network models using DL4J, ND4J and Spark is a powerful combination, but it presents some unexpected issues that can compromise performance and nullify the benefits of well written code and good model design. In this talk I will walk through some of those problems and will present some best practices to prevent them, coming from lessons learned when putting things in production.
Resource-Efficient Deep Learning Model Selection on Apache SparkDatabricks
Deep neural networks (deep nets) are revolutionizing many machine learning (ML) applications. But there is a major bottleneck to broader adoption: the pain of model selection.
Data-Driven Transformation: Leveraging Big Data at Showtime with Apache SparkDatabricks
Interested in learning how Showtime is leveraging the power of Spark to transform a traditional premium cable network into a data-savvy analytical competitor? The growth in our over-the-top (OTT) streaming subscription business has led to an abundance of user-level data not previously available. To capitalize on this opportunity, we have been building and evolving our unified platform which allows data scientists and business analysts to tap into this rich behavioral data to support our business goals. We will share how our small team of data scientists is creating meaningful features which capture the nuanced relationships between users and content; productionizing machine learning models; and leveraging MLflow to optimize the runtime of our pipelines, track the accuracy of our models, and log the quality of our data over time. From data wrangling and exploration to machine learning and automation, we are augmenting our data supply chain by constantly rolling out new capabilities and analytical products to help the organization better understand our subscribers, our content, and our path forward to a data-driven future.
Authors: Josh McNutt, Keria Bermudez-Hernandez
MALT: Distributed Data-Parallelism for Existing ML Applications (Distributed ...asimkadav
Machine learning methods, such as SVM and neural net- works, often improve their accuracy by using models with more parameters trained on large numbers of examples. Building such models on a single machine is often impracti- cal because of the large amount of computation required.
We introduce MALT, a machine learning library that inte- grates with existing machine learning software and provides data parallel machine learning. MALT provides abstractions for fine-grained in-memory updates using one-sided RDMA, limiting data movement costs during incremental model up- dates. MALT allows machine learning developers to specify the dataflow and apply communication and representation optimizations. Through its general-purpose API, MALT can be used to provide data-parallelism to existing ML appli- cations written in C++ and Lua and based on SVM, ma- trix factorization and neural networks. In our results, we show MALT provides fault tolerance, network efficiency and speedup to these applications.
F. Petroni and L. Querzoni:
"GASGD: Stochastic Gradient Descent for Distributed Asynchronous Matrix Completion via Graph Partitioning."
In: Proceedings of the 8th ACM Conference on Recommender Systems (RecSys), 2014.
Abstract: "Matrix completion latent factors models are known to be an effective method to build recommender systems. Currently,
stochastic gradient descent (SGD) is considered one of the best latent factor-based algorithm for matrix completion. In this paper we discuss GASGD, a distributed asynchronous variant of SGD for large-scale matrix completion, that (i) leverages data partitioning schemes based on graph partitioning techniques, (ii) exploits specific characteristics of the input data and (iii) introduces an explicit parameter to tune synchronization frequency among the computing nodes. We empirically show how, thanks to these features, GASGD achieves a fast convergence rate incurring in smaller communication cost with respect to current asynchronous distributed SGD implementations."
Alex Smola, Professor in the Machine Learning Department, Carnegie Mellon Uni...MLconf
Fast, Cheap and Deep – Scaling Machine Learning: Distributed high throughput machine learning is both a challenge and a key enabling technology. Using a Parameter Server template we are able to distribute algorithms efficiently over multiple GPUs and in the cloud. This allows us to design very fast recommender systems, factorization machines, classifiers, and deep networks. This degree of scalability allows us to tackle computationally expensive problems efficiently, yielding excellent results e.g. in visual question answering.
My talk from SICS Data Science Day, describing FlinkML, the Machine Learning library for Apache Flink.
I talk about our approach to large-scale machine learning and how we utilize state-of-the-art algorithms to ensure FlinkML is a truly scalable library.
You can watch a video of the talk here: https://youtu.be/k29qoCm4c_k
Slides from Strata+Hadoop Singapore 2016 presenting how Deep Learning can be scaled both vertically and horizontally, when to use CPUs and when to use GPUs.
Rajat Monga, Engineering Director, TensorFlow, Google at MLconf 2016MLconf
Machine Learning with TensorFlow: TensorFlow has enabled cutting-edge machine learning research at the top AI labs in the world. At the same time it has made the technology accessible to a large audience leading to some amazing uses. TensorFlow is used for classification, recommendation, text parsing, sentiment analysis and more. This talk will go over the design that makes it fast, flexible, and easy to use, and describe how we continue to make it better.
Spark and Deep Learning frameworks with distributed workloadsS N
The increasing complexity of learning algorithms and deep neural networks, combined with size of data and parameters, has made it challenging to exploit existing large-scale data processing pipelines for training and inference.
Approaches are outlined for preprocessing, training, inference, and deployment across datasets that leverage Spark, its extended ecosystem of libraries, and deep learning frameworks.
Keras Tutorial For Beginners | Creating Deep Learning Models Using Keras In P...Edureka!
** AI & Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka Tutorial on "Keras Tutorial" (Deep Learning Blog Series: https://goo.gl/4zxMfU) provides you a quick and insightful tutorial on the working of Keras along with an interesting use-case! We will be checking out the following topics:
Agenda:
What is Keras?
Who makes Keras?
Who uses Keras?
What Makes Keras special?
Working principle of Keras
Keras Models
Understanding Execution
Implementing a Neural Network
Use-Case with Keras
Coding in Colaboratory
Session in a minute
Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
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알리바바 클라우드 PAI (machine learning Platform for AI)Alibaba Cloud Korea
대규모 고성능 분산 컴퓨팅을 기반으로 구축된 알리바바 클라우드의 머신러닝 플랫폼 PAI에 대해 알아보세요. PAI는 고객이 대규모 데이터 마이닝 및 모델링을 쉽게 구현할 수 있도록 지원합니다.
중국 최초의 머신 러닝 플랫폼인 알리바바 클라우드 PAI는 AI 프로그램을 설계하기 위해 제작된 것으로, 여러 고객의 현실적인 문제를 해결하는 데 효과적인 도구입니다.
알리바바 클라우드 PAI의 주요 기능은 다음과 같습니다:
• 다양하고 혁신적인 알고리즘:PAI에는 데이터 전처리, 신경망, 회귀, 분류, 예측, 평가, 통계 분석, 기능 공학 및 딥러닝 아키텍처를 다루는 100가지 이상의 알고리즘이 설계되어 있습니다.
• 딥러닝 아키텍처: PAI에는 전체 컴퓨팅 아키텍처가 다양한 딥러닝 프레임워크에 맞게 최적화되어 있습니다. 또한 이는 API(Application Program Interface)를 배포하는 원클릭 기능을 지원해, 모델링과 서비스 통합 문제를 해결합니다.
• 대규모 컴퓨팅 파워: 알리바바 클라우드의 대형 컴퓨팅 엔진인 PAI는 Apsara에 의해 구동되며, 페타바이트급 컴퓨팅 업무를 매일 처리할 수 있는 초대규모 분산 컴퓨팅 기능을 제공합니다.
• 사용자 친화적 인터페이스: PAI의 데이터 시각화 기능을 통해 개발자는 드래그 앤 드롭 기능으로 구성요소를 작업 흐름에 편리하고 신속하게 투입할 수 있습니다. 모델 구축 및 디버깅 효율성을 향상시키는데 도움을 드립니다.
High Performance Deep learning with Apache SparkRui Liu
Depp learning system is deployed as a service. And, Spark data pipeline is seamlessly connected with this service. Apache Arrow format is used for shared memory data transfer from Spark data pipeline to deep learning services.
Low Latency Polyglot Model Scoring using Apache ApexApache Apex
Data science is fast becoming a complementary approach and process to solve business challenges today. The explosion of frameworks to help data scientists build models bears a testimony to this. However when a model needs to be turned into a production version in very low latency and enterprise grade environments, there are a very few choices with each one having their own strengths and weaknesses. Adding to this is the current disconnect between a data scientists world which is all about modelling and an engineers world which is about SLAs and service guarantees. A framework like Apache Apex can complement each of these roles and provide constructs for both these worlds. This would help enterprises to drastically cut down the cost of model deployment to production environments.
Deep Learning on Apache® Spark™: Workflows and Best PracticesDatabricks
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark.
Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including:
* optimizing cluster setup;
* configuring the cluster;
* ingesting data; and
* monitoring long-running jobs.
We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters.
Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.
Deep Learning on Apache® Spark™: Workflows and Best PracticesJen Aman
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark.
Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including:
* optimizing cluster setup;
* configuring the cluster;
* ingesting data; and
* monitoring long-running jobs.
We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters.
Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.
Deep Learning on Apache® Spark™ : Workflows and Best PracticesJen Aman
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark.
Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including:
* optimizing cluster setup;
* configuring the cluster;
* ingesting data; and
* monitoring long-running jobs.
We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters.
Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.
New developers and teams are now polyglot :
- they use multiple programming languages (Java, Javascript, Ruby, ...)
- they use multiple persistence store (RDBMS, NoSQL, Hadoop)
In this talk you will learn about the benefits if being polyglot: use the good language or framework for the good cause, select the good persistence for specific constraints.
This presentation will show how developer could mix the Java platform with other technologies such as NodeJS and AngularJS to build application in a more productive way. This is also the opportunity to talk about the new Command Query Responsibility Segregation (CQRS) pattern to allow developers to be more effective and deliver the proper application to the user quicker.
This presentation was delivered during Devfest Nantes 2014
AWS re:Invent 2016: Bringing Deep Learning to the Cloud with Amazon EC2 (CMP314)Amazon Web Services
Algorithmia is a startup with a mission to make state of the art machine learning discoverable by everyone&emdash;they offer the largest algorithm marketplace in the world, with over 2500 algorithms supporting tens of thousands of application developers. Algorithma is the first company to make deep learning, one of the most conceptually difficult areas of computing, accessible to any company via microservices. In this session, you learn how this startup has selected and optimized Amazon EC2 instances for various algorithms (including the latest generation of GPU optimized instances), to create a flexible and scalable platform. They also share their architecture and best practices for getting any computationally-intensive application started quickly.
Build, Scale, and Deploy Deep Learning Pipelines Using Apache SparkDatabricks
Deep learning has shown tremendous successes, yet it often requires a lot of effort to leverage its power. Existing deep learning frameworks require writing a lot of code to run a model, let alone in a distributed manner. Deep Learning Pipelines is an Apache Spark Package library that makes practical deep learning simple based on the Spark MLlib Pipelines API. Leveraging Spark, Deep Learning Pipelines scales out many compute-intensive deep learning tasks. In this talk, we discuss the philosophy behind Deep Learning Pipelines, as well as the main tools it provides, how they fit into the deep learning ecosystem, and how they demonstrate Spark's role in deep learning.
Big Data Adavnced Analytics on Microsoft AzureMark Tabladillo
This presentation provides a survey of the advanced analytics strengths of Microsoft Azure from an enterprise perspective (with these organizations being the bulk of big data users) based on the Team Data Science Process. The talk also covers the range of analytics and advanced analytics solutions available for developers using data science and artificial intelligence from Microsoft Azure.
Deep Learning and Streaming in Apache Spark 2.x with Matei ZahariaJen Aman
2017 continues to be an exciting year for Apache Spark. I will talk about new updates in two major areas in the Spark community this year: stream processing with Structured Streaming, and deep learning with high-level libraries such as Deep Learning Pipelines and TensorFlowOnSpark. In both areas, the community is making powerful new functionality available in the same high-level APIs used in the rest of the Spark ecosystem (e.g., DataFrames and ML Pipelines), and improving both the scalability and ease of use of stream processing and machine learning.
Snorkel: Dark Data and Machine Learning with Christopher RéJen Aman
Building applications that can read and analyze a wide variety of data may change the way we do science and make business decisions. However, building such applications is challenging: real world data is expressed in natural language, images, or other “dark” data formats which are fraught with imprecision and ambiguity and so are difficult for machines to understand. This talk will describe Snorkel, whose goal is to make routine Dark Data and other prediction tasks dramatically easier. At its core, Snorkel focuses on a key bottleneck in the development of machine learning systems: the lack of large training datasets. In Snorkel, a user implicitly creates large training sets by writing simple programs that label data, instead of performing manual feature engineering or tedious hand-labeling of individual data items. We’ll provide a set of tutorials that will allow folks to write Snorkel applications that use Spark.
Snorkel is open source on github and available from Snorkel.Stanford.edu.
RISELab:Enabling Intelligent Real-Time DecisionsJen Aman
Spark Summit East Keynote by Ion Stoica
A long-standing grand challenge in computing is to enable machines to act autonomously and intelligently: to rapidly and repeatedly take appropriate actions based on information in the world around them. To address this challenge, at UC Berkeley we are starting a new five year effort that focuses on the development of data-intensive systems that provide Real-Time Intelligence with Secure Execution (RISE). Following in the footsteps of AMPLab, RISELab is an interdisciplinary effort bringing together researchers across AI, robotics, security, and data systems. In this talk I’ll present our research vision and then discuss some of the applications that will be enabled by RISE technologies.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. Background
• Spark
– Batch/Streaming processing, Spark SQL, MLLib
• Deep learning has many use cases in Baidu and showed
significant improvement in quality
– Image retrieval ranking
– Ads CTR prediction
– Machine translation
– Speech recognition
• Goal: able to use distributed deep learning in Spark
3. Typical Training Data in Baidu
• Image Recognition: 100 millions
• OCR: 100 millions
• Speech: 10 billions
• CTR: 100 billions
• Grows every year since 2012
5. Paddle
• Parallel Asynchronous Distributed Deep Learning Library
– Support distributed parameter servers to do
synchronous/asynchronous parameter update
– Support Multi GPU / CPU training
– Support sparse model
– Easy to understand API for user to add new layers
– Support rich features for deep learning use cases,
especially for NLP
6. Deep learning options comparison
Caffe Tensor Flow Torch Paddle
Distributed Training Yes Yes No Yes
Communication
Cost
Medium High N/A Medium to Low
Easy to customize
and coding
Yes More Learning
Curve
More Learning
Curve
Yes
Sparse Model
Support
No Yes Yes Yes
Area of Focus Vision All All All
Integration with
Spark
Yes No No Yes
7. High Level Goals
• Implement Spark ML abstractions to let user train
deep learning models with minimal code change
• Leverage paddle’s native training and parameter
server mechanisms to be scheduled in spark deep
learning jobs
• Handle multi-tenancy and heterogeneity
• Parallelize hyper parameter selection
• Batch and Streaming learning
16. Design decisions
• Spark ML Compatible API
– Compatible with Spark is more important than
implemented under Spark
• Code level configuration
– Easy and flexible
– Manual is prone to error
18. Sharded Parameter Server
• One parameter an one
trainer co-locate in a
machine.
• Parameters are shared,
but not replicated.
• All-to-all communication.
• Our environments
• 4 GPUs per machine.
• 4-10 machines.
• all machines in one
switch
• reliable data center.
19. GPU Ring Synchronization
• Each parameter only needs
to go through slow
connection two times.
• One for reduce.
• Another for scatter.
20. ImageNet Scale Experiments
0
10
20
30
40
1 2 3 4 5
Time(s)
Number of machines
Time per 100 batches
TCP RDMA
• AlexNet on ImageNet
• batch size = 64
• 1 Machine has 4 K10
GPUs.
22. Sparse Training Experiment
0
75
150
225
1 2 4 8 16
Time(s)
Number of nodes
Time per 100 batches
Non Sparse Sparse
• 1451594 dimensional
sparse feature.
• Embedded to 128d, 96d,
96d, and 128d.
• Using a ranking cost on
the top.
• batch size = 128.