It’s been a year since Ben wrote about Nvidia support on Docker Desktop. At that time, it was necessary to take part in the Windows Insider program, use Beta CUDA drivers, and use a Docker Desktop tech preview build. Today, everything has changed:
- On the OS side, Windows 11 users can now enable their GPU without participating in the Windows Insider program. Windows 10 users still need to register.
- Nvidia CUDA drivers have been released.
- Last, the GPU support has been merged in Docker Desktop (in fact since version 3.1).
Nvidia used the term near-native to describe the performance to be expected.
Where to find the Docker images
Base Docker images are hosted at https://hub.docker.com/r/nvidia/cuda. The original project is located at https://gitlab.com/nvidia/container-images/cuda.
What they contain
The nvidia-smi utility allows users to query information on the accessible devices.
$ docker run -it --gpus=all --rm nvidia/cuda:11.4.2-base-ubuntu20.04 nvidia-smi
Tue Dec 7 13:25:19 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.00 Driver Version: 510.06 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... On | 00000000:01:00.0 Off | N/A |
| N/A 0C P0 13W / N/A | 132MiB / 4096MiB | N/A Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------
The dmon function of nvidia-smi allows monitoring the GPU parameters :
$ docker exec -ti $(docker ps -ql) bash
[email protected]:/src# nvidia-smi dmon
# gpu pwr gtemp mtemp sm mem enc dec mclk pclk
# Idx W C C % % % % MHz MHz
0 29 69 - - - 0 0 4996 1845
0 30 69 - - - 0 0 4995 1844The nbody utility is a CUDA sample that provides a benchmarking mode.
$ docker run -it --gpus=all --rm nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -benchmark
...
> 1 Devices used for simulation
GPU Device 0: "Turing" with compute capability 7.5
> Compute 7.5 CUDA device: [NVIDIA GeForce GTX 1650 Ti]
16384 bodies, total time for 10 iterations: 25.958 ms
= 103.410 billion interactions per second
= 2068.205 single-precision GFLOP/s at 20 flops per interactionQuick comparison to a CPU suggest a different order of magnitude of performance. GPU is 2000 times faster:
> Simulation with CPU
4096 bodies, total time for 10 iterations: 3221.642 ms
= 0.052 billion interactions per second
= 1.042 single-precision GFLOP/s at 20 flops per interactionWhat can you do with a paravirtualized GPU?
Run cryptographic tools
Using a GPU is of course useful when operations can be heavily parallelized. That’s the case for hash analysis. dizcza hosted its nvidia-docker based images of hashcat on Docker hub. This image magically works on Docker Desktop!
$ docker run -it --gpus=all --rm dizcza/docker-hashcat //bin/bash
[email protected]:~# hashcat -I
hashcat (v6.2.3) starting in backend information mode
clGetPlatformIDs(): CL_PLATFORM_NOT_FOUND_KHR
CUDA Info:
==========
CUDA.Version.: 11.6
Backend Device ID #1
Name...........: NVIDIA GeForce GTX 1650 Ti
Processor(s)...: 16
Clock..........: 1485
Memory.Total...: 4095 MB
Memory.Free....: 3325 MB
PCI.Addr.BDFe..: 0000:01:00.0From there it is possible to run hashcat benchmark
hashcat -b
...
Hashmode: 0 - MD5
Speed.#1.........: 11800.8 MH/s (90.34ms) @ Accel:64 Loops:1024 Thr:1024 Vec:1
Hashmode: 100 - SHA1
Speed.#1.........: 4021.7 MH/s (66.13ms) @ Accel:32 Loops:512 Thr:1024 Vec:1
Hashmode: 1400 - SHA2-256
Speed.#1.........: 1710.1 MH/s (77.89ms) @ Accel:8 Loops:1024 Thr:1024 Vec:1
...Draw fractals
The project at https://github.com/jameswmccarty/CUDA-Fractal-Flames uses CUDA for generating fractals. There are two steps to build and run on Linux. Let’s see if we can have it running on Docker Desktop. A simple Dockerfile with nothing fancy helps for that.
# syntax = docker/dockerfile:1.3-labs
FROM nvidia/cuda:11.4.2-base-ubuntu20.04
RUN apt -y update
RUN DEBIAN_FRONTEND=noninteractive apt -yq install git nano libtiff-dev cuda-toolkit-11-4
RUN git clone --depth 1 https://github.com/jameswmccarty/CUDA-Fractal-Flames /src
WORKDIR /src
RUN sed 's/4736/1024/' -i fractal_cuda.cu # Make the generated image smaller
RUN makeAnd then we can build and run:
$ docker build . -t cudafractal
$ docker run --gpus=all -ti --rm -v ${PWD}:/tmp/ cudafractal ./fractal -n 15 -c test.coeff -m -15 -M 15 -l -15 -L 15Note that the --gpus=all is only available to the run command. It’s not possible to add GPU intensive steps during the build.
Here’s an example image:
Machine learning
Well really, looking at GPU usage without looking at machine learning would be a miss. The tensorflow:latest-gpu image can take advantage of the GPU in Docker Desktop. I will simply point you to Anca’s blog earlier this year. She described a tensorflow example and deployed it in the cloud: https://www.docker.com/blog/deploy-gpu-accelerated-applications-on-amazon-ecs-with-docker-compose/
Conclusion: What are the benefits for developers?
At Docker, we want to provide a turn key solution for developers to execute their workflows seamlessly:
- With Docker Desktop, developers can run their code locally and deploy to the infrastructure of their choice.
- We provide support in the issue tracker https://github.com/docker/for-win
- See what’s coming up and recommend feature requests in the Docker public roadmap https://github.com/docker/roadmap
- Download the latest version of Docker Desktop now.
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