Viewing profile — flx42_

Thanks, I have reported it internally and it is now fixed.

nvidia-docker[1] maintainer here. Curious to know, are you using docker today? If yes, is there anything missing to satisfy your security requirements? [1] https://github.com/NVIDI…

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 specif…

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-s…

It allows you to run GPU-accelerated applications (like machine learning, HPC, video/image processing...) inside a Docker container.

No performance impact as long as your I/O is done in volumes, to avoid going through AUFS.

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-de…

One of your section is named "Install Nvidia Toolkit 7.5", this is probably what confused parent @hughperkins.

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 wo…

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 …

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…

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 …

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 p…

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 …

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, y…

Yes, running containerized machine learning workflows is our primary use case of nvidia-docker internally. That's why we provide pre-built images for cuDNN and DIGITS on the Docker…

We 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…

The 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 …

Why not use the Tensorflow Docker images? Or if you think they are too old, you can rebuild them manually, it will still be easier than installing all the dependencies manually. Th…

I don't understand why you need to do that, tensorflow is already dockerized for GPUs, using the nvidia-docker images: https://github.com/tensorflow/tensorflow/tree/master/tensorf.…

It depends on your SoC, but most of the time your application won't be able to access HW codecs and then you have no choice but using the mediaserver. I think that if you pull OMX …