flx42_

6 ms·

1.
by flx42_ 6y ago
Thanks, I have reported it internally and it is now fixed.
2.
by flx42_ 8y 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? [1] https://github.com/NVIDIA/nvidia-docker
3.
by flx42_ 9y 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.
4.
by flx42_ 9y 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)
5.
by flx42_ 9y ago
It allows you to run GPU-accelerated applications (like machine learning, HPC, video/image processing...) inside a Docker container.
6.
by flx42_ 10y ago
No performance impact as long as your I/O is done in volumes, to avoid going through AUFS.
7.
by flx42_ 10y 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
8.
by flx42_ 10y ago
One of your section is named "Install Nvidia Toolkit 7.5", this is probably what confused parent @hughperkins.
9.
by flx42_ 10y 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
10.
by flx42_ 10y 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 t
11.
by flx42_ 10y 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 conta
12.
by flx42_ 10y 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 c
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by flx42_ 10y 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.
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by flx42_ 10y 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!
15.
by flx42_ 10y 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
16.
by flx42_ 10y ago
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 DockerHub. Our base cuDNN image is now used by TensorFlow, Ca
17.
by flx42_ 10y ago
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 what you're doing, you don't need to rely on the
18.
by flx42_ 10y ago
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 the nvidia-docker wrapper: https://github.com&#x2
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by flx42_ 11y ago
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. There is also an easier way of downloading cuDNN v2 (there is
20.
by flx42_ 11y ago
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...
21.
Nvidia-docker: build and run Docker containers using GPUs(github.com)
9 points by flx42_ 11y ago|0 comments
22.
by flx42_ 12y ago
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 functions from libstagefright you are actually using IO