Using Docker for GPU-accelerated Applications by Felix Abecassis and Jonathan Calmeis

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In addition of being used for visualization, the highly parallel architecture of GPUs also make them a natural fit for accelerating data-parallel and throughput oriented computations such as machine learning or numerical simulations. When GPUs applications are deployed inside data centers they suffer from the same packaging issues as CPU applications, aggravated by a strong need to get reproducible performance results. The Docker ecosystem is mostly CPU-centric and aims to be hardware-agnostic. This is not the case for GPUs applications since specialized hardware and a specific kernel device driver are now required. We will show how we reconciled those seemingly opposed requirements to enable containerization and execution of GPU applications with Docker.

Technology

Using Docker for GPU
accelerated applications
Felix Abecassis
Systems Software Engineer, NVIDIA
Systems Software Engineer, NVIDIA
Jonathan Calmels

GPU Computing
CUDA
Ecosystem
Applications
Agenda
NVIDIA Docker
Challenges
Our solution
Demos
GPU isolation
Machine Learning
Remote deployment

GPU Computing
nvidia.com/object/gpu-accelerated-computing.html

Heterogeneous Computing
CPU
Optimized for
Serial Tasks
GPU
Optimized for
Parallel Tasks

$CUDA C++ Programming // Vector sum in C void vector_add(int n, const float* a, const float* b, float* c) { for (int idx = 0; idx < n; ++idx) c[idx] = a[idx] + b[idx]; } // Vector sum in CUDA __global__ void vector_add(int n, const float* a, const float* b, float* c) { int idx = blockIdx.x * blockDim.x + threadIdx.x; if (idx < n) c[idx] = a[idx] + b[idx]; }$

Ecosystem
Libraries
Programming
Languages
Compiler
Directives
AmgX
cuBLAS
/
x86

Applications: Deep Learning
INTERNET &
CLOUD
Image Classification
Speech Recognition
Language Translation
Language Processing
Sentiment Analysis
Recommendation
MEDIA &
ENTERTAINMENT
Video Captioning
Video Search
Real Time Translation
AUTONOMOUS
MACHINES
Pedestrian Detection
Lane Tracking
Recognize Traffic Sign
SECURITY &
DEFENSE
Face Detection
Video Surveillance
Satellite Imagery
MEDICINE &
BIOLOGY
Cancer Cell Detection
Diabetic Grading
Drug Discovery

GPU-Accelerated Deep Learning
WATSON THEANO MATCONVNET
TENSORFLOW CNTK TORCH CAFFE
CHAINER

NVIDIA Docker
github.com/NVIDIA/nvidia-docker

Packaging driver files?
FROM ubuntu:14.04
RUN apt-get update &&
apt-get install --no-install-recommends -y gcc make libc-dev wget
RUN wget http://us.download.nvidia.com/XFree86/Linux-x86_64/361.42/NVIDIA-Linux-x86_64-361.42.run
RUN sh NVIDIA-Linux-x86_64-361.42.run --silent --no-kernel-module
Never install the driver in the Dockerfile, not portable!

GPU Applications Workflow
Container Based
Applications
GPU-Accelerated
data center
Research/Develop DeployTest/Package
Video
Transcoding
Image
Processing
Deep
Learning
HPC
Visualization

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Lukonde Mwila, Entelect - As the cloud-native approach to development and deployment becomes more prevalent, it's an exciting time for software engineers to be equipped on how to dockerize multi-container applications and deploy them to the cloud. In this talk, Lukonde Mwila, Software Engineer at Entelect, will cover the following topics: - Docker Compose - Containerizing an Nginx Server - Containerizing an React App - Containerizing an Node.JS App - Containerizing anMongoDB App - Runing Multi-Container App Locally - Creating a CI/CD Pipeline - Adding a build stage to test containers and push images to Docker Hub - Deploying Multi-Container App to AWS Elastic Beanstalk Lukonde will start by giving an overview of how Docker Compose works and how it makes it very easy and straightforward to startup multiple Docker containers at the same time and automatically connect them together with some form of networking. After that, Lukonde will take a hands on approach to containerize an Nginx server, a React app, a NodeJS app and a MongoDB instance to demonstrate the power of Docker Compose. He'll demonstrate usage of two Docker files for an application, one production grade and the other for local development and running of tests. Lastly, he'll demonstrate creating a CI/CD pipeline in AWS to build and test our Docker images before pushing them to Docker Hub or AWS ECR, and finally deploying our multi-container application AWS Elastic Beanstalk.

Securing Your Containerized Applications with NGINX

Docker, Inc.

Kevin Jones, NGNIX - NGINX is one of the most popular images on Docker Hub and has been at the forefront of the web since the early 2000's. In this talk we will discuss how and why NGINX's lightweight and powerful architecture makes it a very popular choice for securing containerized applications as a sidecar reverse proxy within containers. We will highlight important aspects of application security that NGINX can help with, such as TLS, HTTP, AuthN, AuthZ and traffic control.

How To Build and Run Node Apps with Docker and Compose

Docker, Inc.

Kathleen Juell, Digital Ocean - Containers are an essential part of today's microservice ecosystem, as they allow developers and operators to maintain standards of reliability and reproducibility in fast-paced deployment scenarios. And while there are best practices that extend across stacks in containerized environments, there are also things that make each stack distinct, starting with the application image itself. This talk will dive into some of these particularities, both at the image and service level, while also covering general best practices for building and running Node applications with database backends using Docker and Compose.

Hands-on Helm

Docker, Inc.

Distributed Deep Learning with Docker at Salesforce

Docker, Inc.

Jeff Hajewski, Salesforce - There is a wealth of information on building deep learning models with PyTorch or TensorFlow. Anyone interested in building a deep learning model is only a quick search away from a number of clear and well written tutorials that will take them from zero knowledge to having a working image classifier. But what happens when you need to deploy these models in a production setting? At Salesforce, we use TensorFlow models to help us provide customers with insights into their data, and we do this as close to real-time as possible. Designing these systems in a scalable manner requires overcoming a number of design challenges, but the core component is Docker. Docker enables us to design highly scalable systems by allowing us to focus on service interactions, rather than how our services will interact with the hardware. Docker is also at the core of our test infrastructure, allowing developers and data scientists to build and test the system in an end to end manner on their local machines. While some of this may sound complex, the core message is simplicity - Docker allows us to focus on the aspects of the system that matter, greatly simplifying our lives.

The First 10M Pulls: Building The Official Curl Image for Docker Hub

Docker, Inc.

James Fuller, webcomposite s.r.o. - Curl is the venerable (yet very modern) 'swiss army knife' command line tool and library for transferring data with URLs. Recently we (the Curl team) decided to build a release for Docker Hub. This talk will outline our current development workflow with respect to the docker image and provide insights on what it takes to build a docker image for mass public consumption. We are also keen to learn from users and other developers how we might improve and enhance the official curl docker image.

Monitoring in a Microservices World

Docker, Inc.

Fabian Stäber, Instana - In recent years, we saw a great paradigm shift in software engineering away from static monolithic applications towards dynamic distributed horizontally scalable architectures. Docker is one of the key technologies enabling this development. This shift poses a lot of new challenges for application monitoring, ranging from practical issues (need for automation) to technical challenges (Docker networking) to organizational topics (blurring line between software engineers and operations) to fundamental questions (define what is an application). In this talk we show how Docker changed the way we do monitoring, how modern application monitoring systems work, and what future developments we expect.

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Docker, Inc.

Clemente Biondo, Engineering Ingegneria Informatica - When the COVID 19 pandemic started, Engineering Ingegneria Informatica Group (1.25 billion euros of revenues, 65 offices around the world, 12.000 employees) was forced to put their digital transformation to the test in order to maintain operational continuity. In this session, Clemente Biondo, the Tech Lead of the Information Systems Department, will share how his company is reacting to this unforeseeable scenario and how Docker-driven digital transformation had paved the path for work to continue remotely. Clemente will discuss learnings moving from colocated teams, manual approaches, email based-business processes, and a monolithic application to a mature DevOps culture characterized by a distributed autonomous workforce and a continuous deployment process that deploys backward-compatible Docker containerized microservices into hybrid multi cloud datacenters an average of twice a day with zero-downtime. He will detail how they use Docker to unify dev, test and production environments, and as an efficient and automated mechanism for deploying applications. Lastly, Clemente shares how, in our darkest hour, he and others are working to shine their brightest light.

Predicting Space Weather with Docker

Docker, Inc.

Chris Lauer, NOAA Space Weather Prediction Center - This is the story of how adopting a containerized workflow changed the way our small software team works at NOAA’s Space Weather Prediction Center. Our old architecture, a big ball of mud shared-database integration, just wasn’t cutting it - it was killing our agility. Over the past two years, our small team has adopted a microservice style architecture, using Docker with docker-compose and environment files as our deployment strategy for all new development. We’ve discovered the joys of using containers for identical dev, staging, and production environments. We work closely with scientists: much of the code we’re running has complicated and conflicting library dependencies. Docker captures these beautifully - we’ve even had some success teaching our scientists to use it! I’ll share what we’ve learned, some of the persistent challenges we face, and one place we really got it wrong. This talk builds off of a popular hallway track from DockerCon 2019.

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Docker, Inc.

Brian Christner, 56k + Docker Captain - In this session, we will unlock the full potential of using Microsoft Visual Studio Code (VS Code) and Docker Desktop to turn you into a Docker Power User. When we expand and utilize the VS Code Docker plugin, we can take our projects and Docker skills to the next level. In addition to using VS Code, we streamline our Docker Desktop development workflow with less context switching and built-in shortcuts. You will learn how to bootstrap new projects, quickly write Dockerfiles utilizing templates, build, run, and interact with containers all from VS Code.

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Monolithic to Microservices + Docker = SDLC on Steroids!

Docker, Inc.

Ashish Sharma, SS&C Eze - SS&C Eze provides various products in the stock market domain. We spent the last couple of years building Eclipse which is an investment suite born in cloud. The journey so far has been very interesting. The very first version of the product were a bunch of monolithic windows services and deployed using Octopus tool. We successfully managed to bring all the monolithic problem to the cloud and created a nightmare for ourselves. We then started applying microservices architecture principles and started breaking the monolithic into small services. Very soon we realized that we need a better packaging/deployment tool. Docker looked like a magical solution to our problem. Since its adoption, It has not only solved the deployment problem for us but has made a deep impact on different aspects of SDLC. It allowed us to use heterogeneous technology stacks, simplified development environment setup, simplified our testing strategy, improved our speed of delivery, and made our developers more productive. In this talk I would like to share our experience of using Docker and its positive impact on our SDLC.

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Docker, Inc.

Ara Pulido, Datadog - Container technologies, although not new, have increased their popularity in the past few years, with container orchestrators allowing companies around the world to adopt these technologies to help them ship and scale microservices with precision and velocity. Kubernetes is currently the most popular container orchestration platform, and while many organizations are migrating their workloads to it, Kubernetes is still relatively immature. New corner cases, errors, and quirks are regularly discovered as users push the boundaries of size and scale. When Datadog adopted Kubernetes we discovered some of these boundaries the hard way, and we continuously challenge and modify our infrastructure decisions in order to fit our use case. Join me in this talk for our story on what we learned while we scaled our Kubernetes clusters, the contributions to Kubernetes we made along the way, and how you can apply those learnings when growing your Kubernetes clusters from a handful to hundreds or thousands of nodes.

Labels, Labels, Labels

Docker, Inc.

Andy Clemenko, StackRox - One underutilized, and amazing, thing about the docker image scheme is labels. Labels are a built in way to document all aspects about the image itself. Think about all the information that the tags inside your clothing carry. If you care to look you can find out everything about the garment. All that information can be very valuable. Now think about how we can leverage labels to carry similar information. We can even use the labels to contain Docker Compose or even Kubernetes Yaml. We can even include labels into the CI/CD process making things more secure and smoother. Come find out some fun techniques on how to leverage labels to do some fun and amazing things.

Using Docker Hub at Scale to Support Micro Focus' Delivery and Deployment Model

Docker, Inc.

Patrick Deloulay, Micro Focus - Micro Focus started their digital transformation 3 years ago, moving the entire portfolio into hundreds of container images. Leveraging Docker Hub as our primary registry service, we will cover how we ended up building a simple but secure push/pull model to publish and deliver our premium assets to our customers and partners to both meet the high agility of our DevOps teams while greatly simplifying the deployment of our applications.

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From Fortran on the Desktop to Kubernetes in the Cloud: A Windows Migration S...

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Elton Stoneman, Docker Captain + Container Consultant and Trainer How do you provide a SaaS offering when your product is a 10-year old Fortran app, currently built to run on Windows 10? With Docker and Kubernetes of course - and you can do it in a week (... to prototype level at least). In this session I'll walk through the processes and practicalities of taking an older Windows app, making it run in containers with Kubernetes, and then building a simple API wrapper to host the whole stack as a cloud-based SaaS product. There's a lot of technology here from a real world case study, and I'll focus on: - running Windows apps in Docker containers - building a .NET Core API which can run in Linux or Windows containers - running the stack in Kubernetes with Docker Desktop locally and AKS in the cloud - configuring AKS workloads in Azure to burst out to Azure Container Instances And there's a core theme to this session: Docker and Kubernetes are complex technologies, but they're the key to modern development. If you invest time learning them, they make projects like this simple, portable, fast and fun.

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Using Docker for GPU-accelerated Applications by Felix Abecassis and Jonathan Calmeis

1. Using Docker for GPU accelerated applications Felix Abecassis Systems Software Engineer, NVIDIA Systems Software Engineer, NVIDIA Jonathan Calmels

2. GPU Computing CUDA Ecosystem Applications Agenda NVIDIA Docker Challenges Our solution Demos GPU isolation Machine Learning Remote deployment

3. GPU Computing nvidia.com/object/gpu-accelerated-computing.html

4. Heterogeneous Computing CPU Optimized for Serial Tasks GPU Optimized for Parallel Tasks

5. CUDA C++ Programming // Vector sum in C void vector_add(int n, const float* a, const float* b, float* c) { for (int idx = 0; idx < n; ++idx) c[idx] = a[idx] + b[idx]; } // Vector sum in CUDA __global__ void vector_add(int n, const float* a, const float* b, float* c) { int idx = blockIdx.x * blockDim.x + threadIdx.x; if (idx < n) c[idx] = a[idx] + b[idx]; }

6. Ecosystem Libraries Programming Languages Compiler Directives AmgX cuBLAS / x86

7. NVIDIA SDKs

8. Applications: Deep Learning INTERNET & CLOUD Image Classification Speech Recognition Language Translation Language Processing Sentiment Analysis Recommendation MEDIA & ENTERTAINMENT Video Captioning Video Search Real Time Translation AUTONOMOUS MACHINES Pedestrian Detection Lane Tracking Recognize Traffic Sign SECURITY & DEFENSE Face Detection Video Surveillance Satellite Imagery MEDICINE & BIOLOGY Cancer Cell Detection Diabetic Grading Drug Discovery

9. GPU-Accelerated Deep Learning WATSON THEANO MATCONVNET TENSORFLOW CNTK TORCH CAFFE CHAINER

10. NVIDIA Docker github.com/NVIDIA/nvidia-docker

11. Challenges: a typical cluster

12. Packaging driver files? FROM ubuntu:14.04 RUN apt-get update && apt-get install --no-install-recommends -y gcc make libc-dev wget RUN wget http://us.download.nvidia.com/XFree86/Linux-x86_64/361.42/NVIDIA-Linux-x86_64-361.42.run RUN sh NVIDIA-Linux-x86_64-361.42.run --silent --no-kernel-module Never install the driver in the Dockerfile, not portable!

13. Bringing GPU support to Docker

14. Internals

15. DockerHub images

16. GPU Applications Workflow Container Based Applications GPU-Accelerated data center Research/Develop DeployTest/Package Video Transcoding Image Processing Deep Learning HPC Visualization

17. Demos Interactive, ask questions!

18. Thank you!

Using Docker for GPU-accelerated Applications by Felix Abecassis and Jonathan Calmeis

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