Using GPU from a docker container?

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Regan’s answer is great, but it’s a bit out of date, since the correct way to do this is avoid the lxc execution context as Docker has dropped LXC as the default execution context as of docker 0.9.

Instead it’s better to tell docker about the nvidia devices via the –device flag, and just use the native execution context rather than lxc.

Environment

These instructions were tested on the following environment:

  • Ubuntu 14.04
  • CUDA 6.5
  • AWS GPU instance.

Install nvidia driver and cuda on your host

See CUDA 6.5 on AWS GPU Instance Running Ubuntu 14.04 to get your host machine setup.

Install Docker

$ sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-keys 36A1D7869245C8950F966E92D8576A8BA88D21E9
$ sudo sh -c "echo deb https://get.docker.com/ubuntu docker main > /etc/apt/sources.list.d/docker.list"
$ sudo apt-get update && sudo apt-get install lxc-docker

Find your nvidia devices

ls -la /dev | grep nvidia

crw-rw-rw-  1 root root    195,   0 Oct 25 19:37 nvidia0 
crw-rw-rw-  1 root root    195, 255 Oct 25 19:37 nvidiactl
crw-rw-rw-  1 root root    251,   0 Oct 25 19:37 nvidia-uvm

Run Docker container with nvidia driver pre-installed

I’ve created a docker image that has the cuda drivers pre-installed. The dockerfile is available on dockerhub if you want to know how this image was built.

You’ll want to customize this command to match your nvidia devices. Here’s what worked for me:

 $ sudo docker run -ti --device /dev/nvidia0:/dev/nvidia0 --device /dev/nvidiactl:/dev/nvidiactl --device /dev/nvidia-uvm:/dev/nvidia-uvm tleyden5iwx/ubuntu-cuda /bin/bash

Verify CUDA is correctly installed

This should be run from inside the docker container you just launched.

Install CUDA samples:

$ cd /opt/nvidia_installers
$ ./cuda-samples-linux-6.5.14-18745345.run -noprompt -cudaprefix=/usr/local/cuda-6.5/

Build deviceQuery sample:

$ cd /usr/local/cuda/samples/1_Utilities/deviceQuery
$ make
$ ./deviceQuery

If everything worked, you should see the following output:

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 6.5, CUDA Runtime Version = 6.5, NumDevs =    1, Device0 = GRID K520
Result = PASS

More Related Contents:

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  3. Cuda atomics change flag
  4. GPU Emulator for CUDA programming without the hardware [closed]
  5. How is CUDA memory managed?
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  8. Passing Host Function as a function pointer in __global__ OR __device__ function in CUDA
  9. How to verify CuDNN installation?
  10. What is the purpose of using multiple “arch” flags in Nvidia’s NVCC compiler?
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  12. What is a bank conflict? (Doing Cuda/OpenCL programming)
  13. nvidia-smi Volatile GPU-Utilization explanation?
  14. How do I use Nvidia Multi-process Service (MPS) to run multiple non-MPI CUDA applications?
  15. Any particular function to initialize GPU other than the first cudaMalloc call?
  16. Copy an object to device?
  17. CUDA: How to use -arch and -code and SM vs COMPUTE
  18. In CUDA, what is memory coalescing, and how is it achieved?
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  21. cudaMemset() – does it set bytes or integers?
  22. CUBLAS: Incorrect inversion for matrix with zero pivot
  23. Using maximum shared memory in Cuda
  24. Cuda Mutex, why deadlock?
  25. Inconsistency of IDs between ‘nvidia-smi -L’ and cuDeviceGetName()
  26. CUDA allocation alignment is 256 bytes – seriously?
  27. printf() in my CUDA kernel doesn’t result produce any output
  28. Multiply Rectangular Matrices in CUDA
  29. Which Compute Capability is supported by which CUDA versions?
  30. “invalid configuration argument ” error for the call of CUDA kernel?

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