"Because NVIDIA" is blatantly false.
CUDA support for docker containers is provided through the open source Nvidia-Docker project maintained by Nvidia[1]. If anything this is a great argument for NVIDIAs usage of open source.
Searching that project's issues shows that Nvidia-Docker support on MacOS is blocked by the VM used by Docker for Mac(xhyve) not supporting PCI passthrough, which is required for any containers to use host GPU resources.[2]
xhyve has an issue for PCI passthrough, updated a few months ago, which notes that the APIs provided by Apple through DriverKit are insufficient for this use case[3]
So your comment should really say "Because Apple"
[1] https://github.com/NVIDIA/nvidia-docker
[2] https://github.com/NVIDIA/nvidia-docker/issues/101#issuecomm...
[3] https://github.com/machyve/xhyve/issues/108#issuecomment-616...
In Nvidia to Acquire Arm for $40BNVIDIA
nvidia-docker
Systems & Infrastructure2015–2022
NVIDIA / nvidia-docker · Archived
nvidia-docker
Build and run Docker containers leveraging NVIDIA GPUs
27Distinct people
36Mentions
0Explicit recommenders
17.6KGitHub stars
What to know before exploring
What it is
Build and run Docker containers leveraging NVIDIA GPUs
GitHub
Where it is used
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What people are saying
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This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 2:04:58 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
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HN mentions by year36 dated mentions
Observed mentions: 2015-11-05 23:30:22 UTC to 2022-09-10 06:11:57 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
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| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2015 | 2015-01-01 00:00:00 UTC | 2016-01-01 00:00:00 UTC | 4 | 4 | 0 |
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 17 | 3 | 14 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 6 | 1 | 5 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 3 | 0 | 3 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 1 | 1 | 0 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 4 | 0 | 4 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 1 | 0 | 1 |
From the project README
An excerpt from the repository, fetched Nov 13, 2026.
Read the project’s introduction
DEPRECATION NOTICE
This project has been superseded by the NVIDIA Container Toolkit.
The tooling provided by this repository has been deprecated and the repository archived.
The nvidia-docker wrapper is no longer supported, and the NVIDIA Container Toolkit has been extended
to allow users to configure Docker to use the NVIDIA Container Runtime.
For further instructions, see the NVIDIA Container Toolkit documentation
and specifically the install guide.
Issues and Contributing
Checkout the Contributing document!
For questions, feature requests, or bugs, open an issue against the nvidia-container-toolkit repository.
Read the full README on GitHubWhat Hacker News says
4 sourcesOn the contrary, thanks for having included Docker in that list. It's the obvious answer to so many problems (developing, running and deploying apps, replicating deterministic Python environments, not installing linux dependencies required by Python packages directly on your machine, and so on).
BTW, to comment one of the point you made in the article, it's not that hard to run CUDA inside a container. It's less straightforward but quite well documented. You basically need nvidia-docker [1] on the host and start your containers with the runtime 'nvidia'. docker-compose still doesn't support it officially but there are workarounds. [2] I'm running it on ~50 instances in production and automated all the setup with ansible successfully.
[1] https://github.com/NVIDIA/nvidia-docker
[2] https://github.com/docker/compose/issues/6691
In Overview of Python dependency management toolsHopefully, this will enable Windows users will be able to run docker containers that use GPUs the way Linux users have been able to for years now with nvidia’s container toolkit[1].
[1]: https://github.com/NVIDIA/nvidia-docker
In DirectX is coming to the Windows Subsystem for LinuxYou can play with the code[0] for this really easy if you've got a Nvidia GPU with nvidia-docker[1] installed:
docker run --gpus all -it anibali/pytorch:cuda-10.1 bash
# copy images to container with `docker cp`
pip install stylegan2_pytorch
stylegan2_pytorch --data ./images
[0] https://github.com/lucidrains/stylegan2-pytorch
[1] https://github.com/NVIDIA/nvidia-docker
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