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.

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!