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

GPU Computing
CUDA
Ecosystem
Applications
Agenda
NVIDIA Docker
Challenges
Our solution
Demos
GPU isolation
Machine Learnin...

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

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Using Docker for GPU Accelerated Applications

Using Docker for GPU Accelerated Applications

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

Jun. 28, 2016

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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.

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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!