It’s been a year since Ben wrote about Nvidia support on Docker Desktop. At that time, it was necessary to participate in the Windows Insider Program, use the Beta CUDA drivers, and use the Docker Desktop Technology Preview build. Today everything has changed:
- On the operating system 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 released.
- Finally, GPU support is integrated into Docker Desktop (in fact since version 3.1).
Nvidia used the term Native to describe the expected performance.
Where to find Docker images
Base Docker images hosted at https://hub.docker.com/r/nvidia/cuda. The original project is located at https://gitlab.com/nvidia/container-images/cuda.
what it contains
the nvidia-smi The tool allows users to query information about 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 The nvidia-smi function allows you to monitor the parameters of the GPU:
$ docker exec -ti $(docker ps -ql) bash
root@7d3f4cbdeabb:/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 interactionA quick CPU comparison indicates a different order of magnitude in performance. GPU 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 semi-virtual GPU?
Turn on the encryption tools
Using a GPU is of course useful when operations can be greatly balanced. This is the case for segmentation analysis. dizcza She hosted her nvidia-docker-based images for the hashcat on the Docker hub. this picture magically Works on Docker Desktop!
$ docker run -it --gpus=all --rm dizcza/docker-hashcat //bin/bash
root@a6752716788d:~# 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 the hashcat standard can be run
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 to generate fractals. There are two steps to build and run on Linux. Let’s see if we can run it on Docker Desktop. A simple Dockerfile without anything fancy helps with 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 15We note that --gpus=all Available only for run ordering. Cannot add GPU intensive steps during a file build.
Here is an example image:
machine learning
Well really, looking at GPU usage without looking at machine learning would be a waste. the tensorflow:latest-gpu The image can take advantage of the GPU in Docker Desktop. I will simply direct you to the Anca blog earlier this year. I described a tensorflow example and posted it to 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 turnkey solution for developers to seamlessly implement their workflows:
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