Developing with Docker for the Arm Architecture
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This virtual meetup introduces the concepts and best practices of using Docker containers for software development for the Arm architecture across a variety of hardware systems. Using Docker Desktop on Windows or Mac, Amazon Web Services (AWS) A1 instances, and embedded Linux, we will demonstrate the latest Docker features to build, share, and run multi-architecture images with transparent support for Arm.
![14 © 2019 Arm Limited
MNIST: the 'Hello World!’ of ML
http://yann.lecun.com/exdb/mnist/
Dataset format
• There are four files:
• Training set
contains 60.000
examples
• Testing set contains
10.000 examples
• Pixels represented
by values from 0
(white) to 255
(black), organized
row-wise.
ModelExample Dataset Neural network
New input Neural Network Model
TrainingInference
Prediction
”2”[96.0% confidence]](https://image.slidesharecdn.com/armdockernov5meetup-191218053242/85/Developing-with-Docker-for-the-Arm-Architecture-14-320.jpg)
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Developing with Docker for the Arm Architecture
- 1. © 2019 Arm Limited Developing with Docker for the Arm Architecture Jason Andrews [email protected] November 5, 2019
- 2. 2 © 2019 Arm Limited Docker Desktop - Any Device, Any Architecture 2 Build and Run both x86 and Arm images using Docker Desktop on x86 Mac and Windows Developer Machines
- 3. 3 © 2019 Arm Limited Windows 10 Docker Desktop Hyper-V arm/v6 x86 macOS HyperKit QEMUDocker Engine arm/v7 arm64 Managed by Docker Desktop Application Code x86_64 Docker Desktop Architecture
- 4. 4 © 2019 Arm Limited Docker Edge/IoT Developer Kits Embedded developers become cloud native in minutes • Application templates take care of local & cloud provisioning • Device setup and app runtime uses Secure Engine • App deployment trigger Desktop & Pipelines; scan & sign with DTR; deploy to devices 2. Inner Loop - Emulation environment 1. Embedded developer loop 3. Native build on remote server 4. CI/CD pipeline Docker Desktop Enterprise Docker Trusted Registry
- 5. 1 2 3 Meetup outline Developing with Docker Arm C/C++ examples Application templates
- 6. 6 © 2019 Arm Limited Brief History of SW Development & Deployment Embedded engineer perspectiveDevelopDeploy 1950s 1990s 2010s Punch a lot of holes in a lot of cards Set ROM/Flash in custom assembly line Pull app from cloud, run Cloud-based Embedded software development practices are stuck in the 1990s. Cross-compile, copy, ssh/scp, run Punch holes in cards Future embedded development
- 7. 7 © 2019 Arm Limited Alternatives for embedded software development Docker improves on cross-compilation and target-compilation Embedded software development options Docker Cross-compile Target Build Complexity Consistent environment Different environment leads to more porting struggles (especially around library dependencies) Same environment At scale deployment Simple, must setup docker on each target Medium, must reconfigure each target environment Generally slower compile time due to target performance Build time Medium…host PC with QEMU, Good using remote Arm build farm Quickest, host PC speeds Very slow, must reconfigure environment and app on each
- 8. 8 © 2019 Arm Limited Docker: Where is it tomorrow? Develop on Arm, for Arm Docker enables an easy transition to Arm servers which can be used for software development AWS A1 Simply develop and deploy Manage multi-architecture IoT network development and deployment with Docker, from factories to hotels Rich IoT • This • That • The other thing Easily compile and deploy Traditional Arm compilation requires 20+ steps and many 3rd-party packages, requiring expert-level knowledge Edge ML • This • That • The other thing Increase big-data performance Docker simplifies deployment of ‘computational storage’ applications, drastically increasing processing Smart SSD
- 9. 9 © 2019 Arm Limited Amazon EC2 A1 instances Powered by Arm-based processors Up to 45% lowercostforscale-outworkloads1 Powered byAWSGraviton Processorsthatfeature 64-bit Arm Neoverse coresand custom silicon designed byAWS2 Amazon Linux2, Red HatEnterprise Linux7.6, Ubuntu 16.04 LTS & 18.04 LTS, and SUSELinuxEnterprise Serverfor Arm 15 available now 3 Targetworkloadsinclude Web servers, containerized microservices, caching fleets, distributed data stores, as well asdevelopment/testenvironments 4
- 10. 10 © 2019 Arm Limited Raspberry Pi 4 Arm development board to run Docker Board information A 1.5GHz quad-core 64-bit ARM Cortex-A72 CPU 1GB, 2GB, or 4GB of LPDDR4 SDRAM Full-throughput Gigabit Ethernet Dual-band 802.11ac wireless networking Bluetooth 5.0 and USB 3.0 Dual monitor support, at resolutions up to 4K Complete compatibility with earlier Raspberry Pi products The picture can't be displayed.
- 11. 11 © 2019 Arm Limited Meetup goals Targeting Linux application development with C/C++ • Multi-architecture images using docker buildx • Local development on Windows and Mac • Using remote build and run on AWS A1 and Raspberry Pi • Docker templates to automate developer setup
- 12. 12 © 2019 Arm Limited Meetup applications Two primary exercises • PART 1: warm up application using a hello world C program • Docker buildx for multi-architecture images • Docker Hub • Easy application migration across laptop, AWS A1, and Raspberry Pi • PART 2: Arm NN machine learning SDK application • More realistic software development on Arm using complex SDK • Remote build with docker buildx • Multiple docker images for base tools, Arm NN SDK, application development, and deployment
- 13. © 2019 Arm Limited Hello World example Jump to demo
- 14. 14 © 2019 Arm Limited MNIST: the 'Hello World!’ of ML http://yann.lecun.com/exdb/mnist/ Dataset format • There are four files: • Training set contains 60.000 examples • Testing set contains 10.000 examples • Pixels represented by values from 0 (white) to 255 (black), organized row-wise. ModelExample Dataset Neural network New input Neural Network Model TrainingInference Prediction ”2”[96.0% confidence]
- 15. 15 © 2019 Arm Limited Example Application with Arm NN and Arm Compute Library MNIST application using Arm NN and Compute Library 1. C++ application to classify MNIST digits • Using framework parsers provided by Arm NN 2. Arm NN to parse TensorFlow model • The inference engine calls the Arm NN API • Alternatively, directly calls the Compute Library 3. Compute Library running on Cortex-A 1 2 3 Compute Library Application TensorFlow Arm NN TF Parser Cortex-A CPUs 2 1 3 https://github.com/ARM-software/armnn
- 16. © 2019 Arm Limited Machine Learning example Jump to demo
- 17. 17 © 2019 Arm Limited Application templates Available in Docker Desktop Enterprise • Generate project scaffolding • Jumpstart new developers • Customized templates for each application
- 18. © 2019 Arm Limited Docker templates examples Jump to demo
- 19. 19 © 2019 Arm Limited #ArmAIoTDevSummit#ArmAIoTDevSummitwww.arm.com/aiot-dev-summit Get $75 off with code ARMMBED
- 20. 20 © 2019 Arm Limited Final notes Links to more information • Reach out if you are interested in doing more with Docker tools, we’ll setup time to discuss with you and your team • Trial licenses for Docker Desktop Enterprise are available to try application templates • Contact Marc Meunier: [email protected] • Additional articles and GitHub links: https://is.gd/Docker4Arm • Arm TechCon 2019 workshop instructions: http://is.gd/techcon