I am working on a applied math project that utilizes Metal API for acceleration, I want to know if I could use containerd to encapsulate Metal API calls in macOS. Currently, all my apple machines are x86. I also have some linux machines and win machines. I plan to use K8s to orgranize all the machines, hence it would be great if macOS allows me to use Metal API inside containerd.
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I'm part of a team developing a machine learning application.
currently we're using Vagrant with a Docker provider as a uniform dev environment.
We want to utilize the GPUs on our computers when we play around during development, and I found that Nvidia released nvidia-docker to enable that for a simple docker container.
How can I use nvidia-docker as a provider for Vagrant?
If it's not possible, is there any equivalent solution?
It is important for us to develop on top of the same docker image that we deploy since we depend on multiple interacting opensource libraries, and we want to manage them in one place
(no dependencies breaking when deploying)
Is there a way to run a Docker container in Windows IoT Core? I have seen it can be used in Azure, Windows Server and desktop W10 but there is no evidence about Windows IoT Core and I am not sure if some of the already existing installations of docker-engine is compatible with IoT Core or it is just not possible.
Last Friday, Azure IoT Edge v2 launched in Public Preview yesterday with out-of-box support for native Windows containers! There is even a how-to for deploying on Windows IoT Core with a compatible x64-based board*.
First party modules like Azure Functions, Azure Stream Analytics, Modbus and a cool developer experience in VS Code for authoring custom modules all work great with Windows containers on both Windows 10 and IoT Core.
*Note: Windows containers require x64-based processor support, they won’t work on ARM32-based devices like Raspberry Pi.
As of IoT Core version 16299, released on 17 October, this feature is in preview.
https://developer.microsoft.com/en-us/windows/iot/docs/whatsnew
You can run Nano Server Core containers on 64-bit Windows 10 IoT core. It is likely to be amd64 only at this point.
The short answer is, no. This is because Windows 10 IoT Core is an OS that supports a set of features that overlap with Windows 10 desktop, but there is no version of Docker that runs on that currently. Off the top of my head, there would be a few concerns with creating such a version. First, the implementation of Docker would have to be runnable (use features that the OS supports), and second, the features utilized in the container would need to be virtualized by Docker in form that are supported in Windows 10 IoT Core. Third, the hardware running Windows 10 IoT Core (and Docker and its container) would have to support all these functions. Maybe some do and some don't. The problem might be whether or not the hardware such as a Raspberry Pi or Minnowboard supports virtualization in a way that this would be a practical scenario.
On linux, when I wish to develop a driver(character device or block device),I'll need to make sure I develop upon corresponding kernel version. Each linux distribution like centos6.8 has its kernel version like 2.6.32
But seems on docker, all images share the same, new kernel version of moby4.9.2. So it seems that only develop normal applications on docker is valid, but not programs like drivers or any management applications that should visit /dev or other system level resources.
So is docker linux designed to develop system program? If there's no way to specify which kernel version and device structure should be used, seems docker is only for normal user applications?
My question is: how to develop programs like drivers on docker and apply to production linux distributions?
There was a project thrown my way recently that involves the orchestration of several (Linux capable) embedded devices, deploying software to them, and allowing for the applications to be updated when the code base updates in a git repo.
The initial thought was to make a standard image for each device, and I set out, attempting to install docker on an UDOO Quad and an Intel Edison to start, but without any success up to this point.
My thinking is that it seems to be a good idea to install Docker on embedded devices--but if that's the case, surely it would have been ported by now. The only group out there that seems to be making these efforts is Resin.io.
Is there something I'm missing, or is there a clear reason why Docker doesn't make sense on embedded devices? If there isn't a reason, and it does make sense to run Docker on embedded systems, is there something I've overlooked out there: are there any sources of discussion on porting, or how-to's that cover this?
I have considered running docker on embedded devices (a mips system), but didn't go that way. There are some problems with it, in my humble view:
Docker is implemented in Golang. There is currently no available tool chain for mips to compile go. You will need to create the tool chain yourself using gcc-go.
The size of docker is larger than lxc. In a desktop computer this is not a problem, but the embedded device has limited flash storage.
Docker uses some quite up-to-date feature of linux kernel. Sometimes the kernel version on embedded devices are not so new and back-port is needed to make it work.
The docker image has to be built on the same architecture as the run time environment. It means that if you want to run a docker container on Raspberry Pi, the docker image has to be built on an ARM-architecture system. QEMU can be used to build docker image in the cloud, but it doesn't support all CPU architectures used in embedded system. (for example, it currently doesn't support MIPS)
In the end, lxc was chosen for the specific task of running a container on embedded device. It has limited features compared to docker, but currently it suits the requirement of the project.
As of year 2019, I would like to update this answer since I did port docker to embedded system with ARM cpu. With the price of flash usage, memory usage, by using docker you will have container management, image management, and many ready to run images from docker hub. So the decision is a balance between cost and features.
Here is an update for 2018:
You can work with Docker on embedded devices such as Raspberry Pi and Orange Pi quite easily now because of advancements in the development of Raspbian and Armbian operating system images. Specifically, both types of devices and their respective OS images now support kernels that are of sufficiently high enough versions to install Docker without any problems (at least version 3.10, though both now offer 4.x+ versions).
Your desire for faster rates of change can definitely be realized by using embedded Docker. I can say from experience that I have tested and regularly run the approach you describe. Basically, you start with a base operating system image such as Raspbian or Armbian, tweak that operating system enough that it's secure and has Docker installed, and then you use Docker for handling development iteration and application updates.
As an aside, if you are interested in running Docker on embedded Linux devices, then I recommend you check out a free, open-source, MIT-licensed command line tool I wrote to help developers work with embedded Docker on multiple devices at once: https://github.com/ForwardLoopLLC/floopcli .
Even if you are not interested in the tool itself, the documentation for the tool describes several patterns for working with Dockerized applications across multiple devices in multiple languages: https://docs.forward-loop.com/floopcli/master/index.html . The materials there should serve as a starting point for porting applications to Docker and then deploying them on embedded devices. The documentation also addresses some embedded device subtleties, such as differences between ARMv6 and ARMv7. Hopefully this helps you get started!
There is a great article on LinkedIn describing his experience with that
https://www.linkedin.com/pulse/whale-jar-when-running-docker-embedded-linux-good-thing-fletcher#pulse-comments-urn:li:article:7736487387895237975
Often embedded systems have a very slow rate of change. Docker works well on a minimum build then layering on top. If you want to sacrifice the overhead of running docker on a minimum embedded system for docker's ability to have a build system and steady rate of change then you could explorer it.
Is it possible to deploy an OpenCV application to windows azure?
Open CV is comes into client application category accessible through user interface and also can be used for backend processing. Windows Azure Cloud services is used for web application so Open CV does not fit in the application model. For backend processing you may think to use cloud service as worker but that need lots of work on your part and defeat the purpose.
For the sake of completeness and possibility, you sure can get a Windows Azure Virtual Machine, along with Windows OS and deploy OpenCV application there. Once ready you can Remote Desktop to the VM and use it. You may pay monthly cost to use the VM but you sure can do it. But I am sure that is not your objective either.
Yes, I'll say its possible to install OpenCV applications to Azure.
Check the following Deep Learning VM
It comes with pre-installed software. Most of the machine learning libraries along with the OpenCV project are pre-installed
You can also use APIs to host your models on the Windows Azure