flx42_
Joined 12 years ago
Karma
68
Submissions
22
Member since
Apr 18
Recent submissions
Comment · 6 years ago
Thanks, I have reported it internally and it is now fixed.
Comment · 8 years ago
nvidia-docker[1] maintainer here.
Curious to know, are you using docker today? If yes, is there anything missing to satisfy your security requirements?
Comment · 9 years ago
Just wanted to chime in on TensorRT, it's a well supported product and it's different than gpu-rest-engine. This GitHub repo is simply an example of how to use TensorRT in a specific situation.
Comment · 9 years ago
We document how this on our wiki:
https://github.com/NVIDIA/nvidia-docker/wiki/Internals
> The added benefit of this is that you can use different versions of the drivers side-by-side (in my understanding).
No, you can only have one driver version, the one that correspond to the loaded kernel modules. Installing the driver inside a Docker image makes it non-portable.
Comment · 9 years ago
It allows you to run GPU-accelerated applications (like machine learning, HPC, video/image processing...) inside a Docker container.
Comment · 10 years ago
No performance impact as long as your I/O is done in volumes, to avoid going through AUFS.
Comment · 10 years ago
If using Docker is an option, the official Dockerfile works well, you just need to modify the FROM line to "nvidia/cuda:8.0-cudnn5-devel-ubuntu16.04". Or "nvidia/cuda:8.0-cudnn5-devel-ubuntu14.04", depending on which version of Ubuntu you want.
https://github.com/tensorflow/tensorflow/blob/master/tensorf...
Comment · 10 years ago
One of your section is named "Install Nvidia Toolkit 7.5", this is probably what confused parent @hughperkins.
Comment · 10 years ago
No, this is the CUDA toolkit, it doesn't depend on the driver version. You can compile CUDA code without having a GPU (which is the case during a "docker build").
Edit: in other words, your Docker image doesn't depend on a specific driver version and can be ran on any machine with sufficient drivers. Driver files are mounted as a volume when starting the container.
Comment · 10 years ago
At 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! :)
Comment · 10 years ago
You 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.
Comment · 10 years ago
One container can use multiple GPUs on the same machine without problems.
For distributed training (which Caffe doesn't actually support, not the official version), you would have to run one container per instance, but this is more a configuration problem at the framework level, than a Docker or nvidia-docker problem.
Comment · 10 years ago
Well yes, you do need to have the driver installed on the host OS :)
You can run multiple containers on the same GPU with nvidia-docker, it's exactly the same as running multiple processes (without Docker) on the same GPU.
Comment · 10 years ago
Author of nvidia-docker here. You can definitely have multiple containers on each GPU if you want. If you find a bug or if you think the documentation was not great, please file a bug!
Comment · 10 years ago
That'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!