I need to run a custom Docker container with GPU support using Vertex AI Pipelines, and I'm not seeing a clear way to do that. This requires several components:
Applications (pytorch in my case)
CUDA toolkit
CUDA driver
NVIDIA GPUs
I can use a NVIDIA Docker base image for #1 and #2, and a GCP accelerator for #4, but how do I install the CUDA driver in a Vertex AI pipeline? There's documentation on how to install NVIDIA drivers on GCE instances and GKE nodes, but nothing for Vertex AI.
One option could be to create a derivative container based on a GCP Deep Learning Container, but then I have to use a GCP container and don't have as much control over the environment.
It seems to me that you can use one of the pre-built GPU docker containers:
https://cloud.google.com/vertex-ai/docs/predictions/pre-built-containers
Related
I'm trying to run this deep learn algorithm https://github.com/albarji/neural-style-docker that's containerized in docker pushing the image on minikube, I understand that Docker isolate the container from the machine so you can't use your GPU, that's why you use nvidia-docker but I'm not sure if you can run nvdia-docker in minikube and I don't know how to make it work.
What I'm trying to do is to run the algorithm through the minikube node to prove that the algorithm can work with a kubernetes cluster.
I managed to make the algorithm work locally with docker following the documentation and I have installed NVIDIA drivers and working on Ubuntu 18.04
I am trying out tensorflow for deep learning, but for GPU support, tensorflow needs Nvidia GPU, but I have a Radeon 560. Is there any way of running tensorflow GPU in a Docker container? I couldn't wrap my head around Docker. Are there any other possibilities other than using Colab or Docker?
The official tensorflow-gpu docker images at this time only support NVIDIA gpus and host running the NVIDIA cuda-driver. In order to use an AMD GPU, you need to use a version of TensorFlow built with ROCm support. Google for "tensorflow ROCm" and you'll find some pages on how to do this. The best link is probably:
https://github.com/ROCmSoftwarePlatform/tensorflow-upstream
TensorFlow Official Supports a limited set of Operating System. If I use the tensorflow docker image on CentOS would that be a supported configuration?
I understand that I can get TensorFlow to work, I am cwondering about being an approved configuration
Thanks,
If you use Docker, you can install Ubuntu as base image. You should definitely try Nvidia Cloud GPU. It is a free docker from Nvidia to use tensorflow or any other framework from a container.
Initially Docker for Linux leveraged the namespace, cgroup primitives to provide the containerization solution on Linux platform. It used LXC and later on runC to jail docker processes. While they are extending the support for docker on Mac/Windows, seems that they are taking an inelegant workaround that beats the whole purpose of using containerization over virtualization.
Docker Toolbox used boot2docker Linux (based on a stripped down version of Tiny Core) to host docker containers. boot2docker runs on Oracle Virtualbox.
Docker for Mac runs Alpine Linux on OS X Yosemite's native virtualization called Hypervisor framework. The interfacing is realized through Hyperkit built on top of xhyve (an OS X port of bhyve).
Docker for Windows runs on Hyper-V virtualization framework on Windows 10.
The reason behind using docker (in general, containers) over traditional VMs is negligible overhead and near native performance. Conainers has to be lightweight to be useful.
How do containers compare to virtual machines?
They are complementary. VMs are best used to allocate chunks of
hardware resources. Containers operate at the process level, which
makes them very lightweight and perfect as a unit of software
delivery.
As both Docker for Mac/Windows rely on some virtualization technology behind the scene, is using docker on these platform still retain its relevance? Doesn't using virtualization to emulate containerization beat the whole purpose of switching to docker framework? Just as a side note, this article, too, supports my viewpoint.
As both Docker for Mac/Windows rely on some virtualization technology behind the scene, is using docker on these platform still retain its relevance?
Of course. Pending full native container support on those platform, you still benefit from the main advantages of docker: service discovery, orchestration (kubernetes/swarm) and monitoring.
Those services are easier to scale as container as they would be as individual VMs.
Doesn't using virtualization to emulate containerization beat the whole purpose of switching to docker framework?
No because without the docker framework, you would be left with one VM in which all your services would have to live, without the benefit of isolation and individual upgrade.
What is meant by Docker on bare metal?
what is the difference between running docker on a ubuntu system and docker running on bare metal?
Do we get all the features when running on bare metal?
how do I install Docker on bare metal?
Who runs the container?
Thanks!!
Docker uses the linux-kernel to do containerization. Without a linux kernel it won't run.
Docker is only a go binary, and to run that you need an operating system .
Docker is often referred to be as fast as running on bare metal. In this context that means not to be in a virtual environment but to have an operating system.
It's the same thing, bare metal just refers to the fact it is not running "in the cloud" or in a hypervisor or kvm. All the features are still the exact same. It is more referring to the setup of the server it is running on.