Re: Official Microsoft Toolkit KMS V2.6.4 Setup Free
Agathe Thies
Over the last few years there has been a dramatic rise in the use of software containers for simplifying deployment of data center applications at scale. Containers encapsulate an application along with its libraries and other dependencies to provide reproducible and reliable execution of applications and services without the overhead of a full virtual machine.
The NVIDIA Container Runtime for Docker, also known as nvidia-docker2 enables GPU-based applications that are portable across multiple machines, in a similar way to how Docker enables CPU-based applications to be deployed across multiple machines. It accomplishes this through the use of Docker containers.
A Docker container is a mechanism for bundling a Linux application with all of its libraries, data files, and environment variables so that the execution environment is always the same, on whatever Linux system it runs and between instances on the same host.
A Docker container is composed of layers. The layers are combined to create the container. You can think of layers as intermediate images that add some capability to the overall container. If you make a change to a layer through a DockerFile (see Building Containers), than Docker rebuilds that layer and all subsequent layers but not the layers that are not affected by the build. This reduces the time to create containers and also allows you to keep them modular.
One of the many benefits to using containers is that you can install your application, dependencies and environment variables one time into the container image; rather than on each system you run on. In addition, the key benefits to using containers also include:
For DGX systems, simply log into the system. See the Frameworks Support Matrix for the current list of DGX systems supported. For NGC consult the NGC documentation for details about your specific cloud provider. In general, you will start a cloud instance with your cloud provider using the NVIDIA Volta Deep Learning Image. After the instance has booted, log into the instance.
Typically, one of the first things you will want to do is get a list of all the Docker images that are currently available on the local computer. Issuing a docker pull command will download Docker images from the repository onto your local system. Issue the docker images command to list the images on the server. Your screen will look similar to the following:
In this example, there are a few Docker containers that have been pulled down to this system. Each image is listed along with its tag, the corresponding Image ID, also known as container version. There are two other columns that list when the container was created (approximately), and the approximate size of the image in GB. These columns have been cropped to improve readability.
NVIDIA has also developed a set of containers which includes software that is specific to NVIDIA DGX systems. These containers ensure the best performance for your applications and should provide the best single-GPU performance and multi-GPU scaling.
You can run a Docker container on any platform that is Docker compatible allowing you to move your application to wherever you need. The containers are platform-agnostic, and therefore, hardware agnostic as well. To get the best performance and to take full advantage of the tremendous performance of an NVIDIA GPU, specific kernel modules and user-level libraries are needed. NVIDIA GPUs introduce some complexity because they require kernel modules and user-level libraries to operate.
One approach to solving this complexity when using containers is to have the NVIDIA drivers installed in the container and have the character devices mapped corresponding to the NVIDIA GPUs such as /dev/nvidia0. For this to work, the drivers on the host (the system that is running the container), must match the version of the driver installed in the container. This approach drastically reduces the portability of the container.
There are times when you will need to connect to a running container. You can use the docker exec command to connect to a running container to run commands. You can use the bash command to start an interactive command line terminal or bash shell.
Building deep learning frameworks can be quite a bit of work and can be very time consuming. Moreover, these frameworks are being updated weekly, if not daily. On top of this, is the need to optimize and tune the frameworks for GPUs. NVIDIA has created a Docker repository, named nvcr.io, where deep learning frameworks are tuned, optimized, tested, and containerized for your use.
NVIDIA creates an updated set of Docker containers for the frameworks monthly. Included in the container is source (these are open-source frameworks), scripts for building the frameworks, Dockerfiles for creating containers based on these containers, markdown files that contain text about the specific container, and tools and scripts for pulling down datasets that can be used for testing or learning. Customers who purchase a DGX system have access to this repository for pushing containers (storing containers).
To get started with DGX systems, you need to create a system admin account for accessing nvcr.io. This account should be treated as an admin account so that users cannot access it. Once this account is created, the system admin can create accounts for projects that belong to the account. They can then give users access to these projects so that they can store or share any containers that they create.
This section of the document applies to Docker containers in general. You can use this general approach for your own Docker repository as well, but be cautious of the details. Using a DGX system you can either:
Any one of the three approaches are valid and will work, however, since the goal is to run the containers on a system which has GPUs, it's logical to assume that the applications will be using GPUs. Moreover, these containers are already tuned for GPUs. All of them also include the needed GPU libraries, configuration files, and tools to rebuild the container.
An existing container in nvcr.io should be used as a starting point. As an example, the TensorFlow 17.06 container will be used and Octave will be added to the container so that some post-processing of the results can be accomplished.
The command starts with docker build. The -t option creates a tag for this new container. Notice that the tag specifies the project in the nvcr.io repository where the container is to be stored. As an example, the project nvidian_sas was used along with the repository nvcr.io.
Projects can be created by your local administrator who controls access to nvcr.io, or they can give you permission to create them. This is where you can store your specific containers and even share them with your colleagues.
One of the fundamental aspects of using Docker is mounting file systems inside the Docker container. These file systems can contain input data for the frameworks or even code to run in the container. Docker containers have their own internal file system that is separate from file systems on the rest of the host.
After the last path, various options can be used in parenthesis (). In the above example, the second file system is mounted read-only (ro) inside the container. The various options for the -v option are discussed here.
The base command docker run --gpu all assumes that your system has Docker 19.03-CE and the NVIDIA runtime packages installed. See the section Enabling GPU Support For NGC Containers for the command to use for earlier versions of Docker.
You can access the NGC container registry by running a Docker command from your client computer. You are not limited to using your DGX platform to access the NGC container registry. You can use any Linux computer with Internet access on which Docker is installed. For more information about which platforms are supported, see
A Docker registry is the service that stores Docker images. The service can be on the internet, on the company intranet, or on a local machine. For example, nvcr.io is the location of the NGC container registry for Docker images.
Before you can pull a container from the NGC container registry, you must have Docker and nvidia-docker2 installed as explained in Preparing To Use NVIDIA Containers Getting Started Guide. You must also have access and logged into the NGC container registry as explained in NGC Getting Started Guide.
Notice that you need the Container ID of the image you want to stop. This can be found using the $ docker ps -a command. Another useful command or Docker option is to remove the image from the server. Removing or deleting the image saves space on the server. For example, issue the following command:
Figure 1. Docker containers encapsulate application dependencies to provide reproducible and reliable execution. The nvidia-docker utility mounts the user mode components of the NVIDIA driver and the GPUs into the Docker container at launch.
In the next sections, you will use these image names for running containers. Later in the document there is also a section on creating your own containers or customizing and extending existing containers.
Before you can run an NGC deep learning framework container, your Docker environment must support NVIDIA GPUs. To run a container, issue the appropriate command as explained in this chapter, specifying the registry, repository, and tags.
The method implemented in your system depends on the DGX OS version installed (for DGX systems), the specific NGC Cloud Image provided by a Cloud Service Provider, or the software that you have installed in preparation for running NGC containers on TITAN PCs, Quadro PCs, or vGPUs. Refer to the following table to assist in determining which method is implemented in your system.
If you build Docker images while nvidia is set as the default runtime, make sure the build scripts executed by the Dockerfile specify the GPU architectures that the container will need. Failure to do so may result in the container being optimized only for the GPU architecture on which it was built. Instructions for specifying the GPU architecture depend on the application and are beyond the scope of this document. Consult the specific application build process for guidance.
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