I have used and recommend [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) for exploration (at least for those with a docker-capable kernel as the base OS).
In Practical Deep Learning for CodersNVIDIA
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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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.
View mention counts and date ranges
| 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This project has been superseded by the NVIDIA Container Toolkit.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
17 sourcesNvidia Docker – Build and Run Docker Containers Leveraging Nvidia GPUs
2 points · Open discussionWhat about NVIDIA-docker?
https://github.com/NVIDIA/nvidia-docker
In Correcting Intel's Deep Learning Benchmark MistakesI'm really not up on how this all works, but isn't https://github.com/NVIDIA/nvidia-docker/blob/master/ubuntu-1... hardcoding driver versions in a different way?
In Steam in DockerAt NVIDIA we maintain this utility:
https://github.com/NVIDIA/nvidia-docker
It automatically discovers the devices and the right driver files on the host.
The main goal is compute (CUDA), but we also demonstrated how to run TF2 on Steam OS during our DockerCon 16 OpenForum presentation.
Nice job! :)
In Steam in DockerYou don't need to match the driver version between the host and the container. Actually, you shouldn't include any driver file inside the container.
All the user-level driver-files required for execution are mounted when the container is started using a volume. This way you can deploy the same container on any machine with NVIDIA drivers installed.
We have more details on our wiki:
https://github.com/NVIDIA/nvidia-docker/wiki/Internals
Concerning your last question: I don't have any information on this topic, but anyway it would not really impact nvidia-docker.
In An API for hosted deep learning models> "Using GPUs inside of containers is a challenge. There are driver issues, system dependencies, and configuration challenges. It’s a new space that’s not well-explored, yet. There’s not a lot of people out there trying to run multiple GPU jobs inside a Docker container.”
Er, Nvidia itself has an official Docker application which allows containers to interface with the host GPU, optimized for the deep learning use case: https://github.com/NVIDIA/nvidia-docker
Training models is one thing that can commoditized, like with this API, but building models and selecting features without breaking the rules of statistics is another story and is the true bottleneck for deep learning. That can't be automated as easily.
In An API for hosted deep learning modelsThat's exactly what we do, the image is indeed driverless and we mount the host driver files as a volume (provided by our volume plugin) when the container is launched.
This way, you can launch a CUDA 7.5 container on any machine with driver version 352+
We provide more details here:
https://github.com/NVIDIA/nvidia-docker/wiki/NVIDIA-driver
Hope that helps!
In Nvidia-Docker: Build and Run Docker Containers Leveraging Nvidia GPUsWe are only wrapping the Docker CLI, not forking the full code (that would be insane). The wrapper is provided for convenience since it should be enough for most users. If you know what you're doing, you don't need to rely on the wrapper, we explain how you can do that on our wiki:
https://github.com/NVIDIA/nvidia-docker/wiki/Internals
Yes, we have been discussing with a few people at Docker, I hope attending DockerCon and discussing with the team will help us move forward.
In Nvidia-Docker: Build and Run Docker Containers Leveraging Nvidia GPUsNvidia-Docker: Build and Run Docker Containers Leveraging Nvidia GPUs
63 points · Open discussionContainerized Nvidia Docker Image for GPU Applications
4 points · Open discussionThe CLI wrapper is provided for convenience since it should be enough for most users.
We recently added advanced documentation on our wiki, we explain how you can avoid relying on the nvidia-docker wrapper:
https://github.com/NVIDIA/nvidia-docker/wiki/Internals
This section should also have plenty of information for you if your goal is to integrate GPU support in your own container tool.
In Setting Up a Deep Learning Machine from ScratchNvidia has been making some pretty cool strides with https://github.com/NVIDIA/nvidia-docker, but I wish they would expose some images / docker run commands directly instead of wrapping it with their own (bespoke) Go tool. In its current form its utility strikes me as really limited since it can't be used with other tooling.
In Setting Up a Deep Learning Machine from ScratchThe dependency hell required to run a good deep learning machine is one of the reasons why using Docker/VM is not a bad idea. Even if you follow the instructions in the OP to the letter, you can still run into issues where a) an unexpected interaction with permissions/other package versions causes the build to fail and b) building all the packages can take an hour+ to do even on a good computer.
The Neural Doodle tool (https://github.com/alexjc/neural-doodle), which appeared on HN a couple months ago (https://news.ycombinator.com/item?id=11257566), is very difficult to set up without errors. Meanwhile, the included Docker container (for the CPU implementation) can get things running immediately after a 311MB download, even on Windows which otherwise gets fussy with machine learning libraries. (I haven't played with the GPU container yet,
Read the rest
though)
Nvidia also has an interesting implementation of Docker which allows containers to use the GPU on the host: https://github.com/NVIDIA/nvidia-docker
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