Table of Contents<\/strong><\/h2>\n\n\n- Getting Started with Docker for Machine Learning<\/a><\/li>\n
\n- Overview: Why the Need?<\/a><\/li>\n
- What Are Containers?<\/a><\/li>\n
- How Do Containers Differ from Virtual Machines?<\/a><\/li>\n
- Most Popular Container Images<\/a><\/li>\n
\n- TensorFlow<\/a><\/li>\n
- PyTorch<\/a><\/li>\n
- Python<\/a><\/li>\n
- NGINX<\/a><\/li>\n<\/ul>\n
- Install Docker<\/a><\/li>\n
- Install Docker Manually<\/a><\/li>\n
- Install Docker Desktop<\/a><\/li>\n
- Install NVIDIA Container Toolkit<\/a><\/li>\n<\/ul>\n
- Summary<\/a><\/li>\n
\n- Citation Information<\/a><\/li>\n<\/ul>\n<\/ul>\n<\/div>\n
\nGetting Started with Docker for Machine Learning<\/strong><\/a><\/h2>\nIn this tutorial, you will learn what Containers are, how they differ from Virtual Machines (VMs), and what Docker is. Finally, we will top it off by installing Docker on our local machine with simple and easy-to-follow steps.<\/p>\n
\n![\"\"](\"https:\/\/b2633864.smushcdn.com\/2633864\/wp-content\/uploads\/2023\/09\/docker-1-featured-1024x575.png?lossy=2&strip=1&webp=1\")
<\/a><\/figure>\n<\/div>\nThis lesson is the 1st of a 3-part series on Docker for Machine Learning<\/strong>:<\/p>\n\n- Getting Started with Docker for Machine Learning<\/em><\/strong><\/a> (this tutorial)<\/strong><\/li>\n
- Lesson 2<\/em><\/li>\n
- Lesson 3<\/em><\/li>\n<\/ol>\n
\nOverview: Why the Need?<\/strong><\/a><\/h3>\nEnvision yourself as an ML Engineer<\/strong> at one of the world\u2019s largest companies. You make a Machine Learning (ML) pipeline that does everything, from gathering and preparing data to making predictions. All the individual blocks are in the proper order, and you ensure everything works like a well-oiled machine.<\/p>\nAfter verifying everything, you hand over this pipeline to a colleague to get a review before your pipeline is deployed to perform real-time streaming predictions<\/em>. But, alas! The pipeline broke down when moved from your computer to your colleague\u2019s computer.<\/p>\nSwitching gears, imagine yourself being part of a high-tech research lab working with Machine Learning algorithms. You quickly iterate with several different things daily, each with additional requirements and dependencies. One day, you are working with TPUs to run JAX code. Next, you\u2019re trying to make your app available to the world as an API.<\/p>\n
In these cases, you\u2019d also encounter annoying times when your entire environment is polluted by one dirty install. Repairing such issues becomes time-consuming<\/strong>, especially in research iteration where time is crucial<\/strong>.<\/p>\n
\nWhat Are Containers?<\/strong><\/a><\/h3>\nIn the cases we just saw, the problem patterns stem from the tight coupling<\/em> between the software<\/strong> and hardware<\/strong> we develop.<\/p>\nThis can be a major impediment<\/strong> to developing software that \u201cjust works\u201d on all systems. When we look at this problem in the Machine Learning context, this can bring quick iteration to a halt.<\/p>\nWhat if we decouple<\/strong> the dependencies of the software we write from the hardware it works on? Could our software development environment be wrapped into a neat and simple package that can be shifted to any machine we want, irrespective of hardware or other dependency issues? <\/p>\nEnter the concept of Containers<\/strong>. Containers are lightweight packages<\/em><\/strong> that act as portable, sandboxed environments for use on any hardware that supports them. This would include code, dependencies, files, operating systems, runtime, and system settings.<\/p>\nLet\u2019s break that down. Containers are lightweight since they do not store Operating System (OS) images within them while using a snapshot<\/strong> instead. They virtualize the resources available to the OS for a specific process concerning CPU usage, memory, and storage. This allows your process to be portable and sandboxed from all other processes running on the host.<\/p>\nContainer runtimes are consistent, meaning they would work precisely the same whether you\u2019re on a Dell<\/em> laptop with an AMD<\/em> CPU, a top-notch MacBook Pro<\/em>, or an old Intel Lenovo ThinkPad<\/em> from 2015. <\/p>\nOne of the most popular methods of creating and working with containers is by using Docker<\/strong><\/a>. It is a platform that provides a batteries-included<\/em> experience for creating<\/strong>, deploying<\/strong>, and working<\/strong> with containers.<\/p>\nThroughout this tutorial, we will use Docker as the software for manipulating and working with containers. Some other alternatives to Docker include LXC (Linux Container Runtime)<\/a> and Podman<\/a>.<\/p>\nNote: <\/em><\/strong>Something of this sort would be useful to an ML Engineer such as yourself because it allows you to expose services from isolated runtimes and ensure horizontal scalability without any hassle.<\/em><\/p>\n
\nHow Do Containers Differ from Virtual Machines?<\/strong><\/a><\/h3>\nA question that may have popped up by now should be how containers differ from traditional Virtual Machines<\/a>. Let\u2019s see how they diverge<\/strong>.<\/p>\nThe most notable difference between a virtual machine (VM) and a container is the level of virtualization. This refers to the introduction of an abstraction layer over the hardware at a level where the resources are allocated and utilized separately from the original OS on the machine. This abstraction layer is also referred to commonly as a hypervisor<\/a>. They tend to have several functions (e.g., creating and deleting VMs, managing, and scheduling them). VMs work to virtualize everything up from the hardware layers on a machine with this layer on top.<\/p>\n\n![\"\"](\"https:\/\/lh3.googleusercontent.com\/lwilSiQt464Mi-p5AW9o1Y2kfclacni0pmbwxuJ7glhmXT7YCjUi--WNXEEUmm0aAPLZe2e4zPlH3GSe2_FLZU0uFESNg_2mgG_DGwMZfOAY1PObJDteStxE35LCoTRkCQHbXk9kz4ANgrEKqcHe57Y\")
<\/a>Difference between a Container and a Virtual Machine (source: (https:\/\/upload.wikimedia.org\/wikipedia\/commons\/0\/0a\/Docker-containerized-and-vm-transparent-bg.png<\/a>).<\/figcaption><\/figure>\n<\/div>\nOn the other hand, containers only virtualize up to the software layers of the container engine that handles all the containers present in a system. It does not go down to the hypervisor layer and instead hands over its own control to the original OS on the machine, which is controlled by the container runtime. The container runtime is like a hypervisor but made only on a software level for containers. It does not have direct access to the hardware of the machine.<\/p>\n
Virtual machines typically provide higher isolation and security by working directly with the hardware (depending on the type of hypervisor used). In contrast, Containers tend to simply isolate the specific process and filesystem while delegating the grunt work of allocating resources to the host OS itself via the container runtime.<\/p>\n
When working with different Operating Systems vis-\u00e0-vis Containers, they tend only to have a snapshot of an OS instead of the full OS image, making them significantly lighter than traditional VMs. But, this brings about a problem wherein we must ensure that the container\u2019s OS matches the same kernel as the original OS. That means the container\u2019s OS must have the same format for requesting machine resources as that of the host OS. For that reason, one would see that a Windows container cannot be used on Linux machines. In comparison, Linux containers may be created on Windows machines with the advent of WSL2<\/a>.<\/p>\n
\nMost Popular Container Images<\/strong><\/a><\/h3>\nToday, popular software applications have container images available for direct use on any hardware that the user desires. Some notable examples include the following.<\/p>\n
\nTensorFlow<\/strong><\/a><\/h4>\nThe container runtime for TensorFlow<\/a> acts as a simple method to quickly experiment or iterate with TensorFlow in a throwaway fashion. These images also support interfacing with the GPU, meaning you can leverage it for training your Deep Learning networks written in TensorFlow. Note that, using a GPU typically refers to using NVIDIA CUDA-enabled GPUs (Alternative NVIDIA NGC Container Image here<\/a>).<\/p>\n\n![\"\"](\"https:\/\/lh4.googleusercontent.com\/dzlLMy4ELN3CyhtXJeBOwsL_fnq6tjriJzxmdLiglT_JZk6M5ea8B31ngKIugzFIekDDsTSQc87iN4z5MbJj9q5OY6Na5dZGtvJMAVsC0-Q6TJ8Elcwig4osCRidQ4-0QFuap9aFNMNcgiCsTKvE4QI\")
<\/a>Docker and TensorFlow (source: image created by the author)<\/figcaption><\/figure>\n<\/div>\n
\nPyTorch<\/strong><\/a><\/h4>\nThe container runtime for PyTorch<\/a> brings torch and torchvision to your runtime, along with all dependencies pre-installed. This allows you to begin your development directly and provides a platform for quick iteration. Similar to TensorFlow, this container image also provides a GPU-compatible version that uses Compute Unified Device Architecture (CUDA) to speed up Tensor processing. (Alternative NVIDIA NGC Container Image here<\/a>)<\/p>\n
\nPython<\/strong><\/a><\/h4>\nThe container runtime for Python<\/a> sets up a Debian Linux instance with Python pre-installed. This is suitable for making a variety of Python applications with other dependencies being added to it at the user\u2019s convenience. This container is commonly used for simpler tasks (e.g., developing web services, APIs, running tests, etc.). Depending on their host platform, one can choose between using Debian Linux, Alpine Linux, or Windows Server Core versions of this image.<\/p>\n
\nNGINX<\/strong><\/a><\/h4>\nThe container runtime for NGINX<\/a> sets up a Debian Linux instance with NGINX pre-installed. This allows you to use the popular reverse proxy server with load-balancers and HTTP caches without the hassle of setting it up across different machines. This image is also available in various styles (e.g., the standard Debian Linux base or with an Alpine Linux base).<\/p>\n
\nInstall Docker<\/strong><\/a><\/h3>\nWith this information on why Docker containers are so popular, it\u2019s time we install it on our local machine and start making our containers for Machine Learning.<\/p>\n
To later work with all the required components of Docker, we install Docker Desktop. It includes a standalone Desktop GUI (graphical user interface) application that simplifies your experience with Docker.<\/p>\n
If you are using Ubuntu, Debian, Fedora, Red Hat Enterprise Linux (RHEL), CentOS, or other common Linux Distributions, you can use the official convenience script from Docker by executing the command below.<\/p>\n
```bash\ncurl https:\/\/get.docker.com | sh \\\n && sudo systemctl --now enable docker\n```<\/pre>\nThis should set up Docker completely on your system! Skip over the next section to see how you can install Docker Desktop for a GUI-based interface.<\/p>\n
If you want to improve your Docker experience for later, you can follow some convenience-enhancing steps<\/a> for Linux systems. Note that these are optional and are geared toward superusers.<\/p>\n
\nInstall Docker Manually<\/strong><\/a><\/h3>\nNow, let\u2019s look at how to install Docker on a Linux system manually. For this example, we will look at a Fedora Linux setup with GNOME. Note, if you are using Ubuntu or any other Linux distribution, you can simply replace most of these commands with the distribution\u2019s respective package manager (\u201capt\u201d for Ubuntu, \u201cpacman\u201d for Arch Linux, \u201cbrew\u201d for Mac, etc.).<\/p>\n
\n- Ensure you have
gnome-terminal<\/code> installed (or the appropriate terminal as required). To do so, use the following command:<\/li>\n<\/ul>\n```bash \n>> sudo dnf install gnome-terminal\n```<\/pre>\n
\n- Set up the Docker package repositories<\/li>\n<\/ul>\n
```bash\n>> sudo dnf -y install dnf-plugins-core\n>> sudo dnf config-manager --add-repo https:\/\/download.docker.com\/linux\/fedora\/docker-ce.repo\n```<\/pre>\n
\n- Install Docker Engine, containerd, Docker build kit, Docker Compose, and related command line interfaces (CLIs)<\/li>\n<\/ul>\n
```bash\n>> sudo dnf install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin\n```<\/pre>\nOnce this process is complete, you will have a working installation of Docker on your system!<\/p>\n
\nInstall Docker Desktop<\/strong><\/a><\/h3>\nNow, let\u2019s look at how to install Docker Desktop on your system. Follow along!<\/p>\n
\n- Download the RPM (Red Hat Package Management system) file for Docker Desktop<\/a> (Note:<\/em><\/strong> This link may change in the future. To get the latest link, please look here<\/a>.)<\/li>\n
- Use the following command to install it:<\/li>\n<\/ul>\n
```bash\n>> sudo dnf install .\/docker-desktop-<version>-<arch>.rpm\n```<\/pre>\n
\n- Start Docker Desktop<\/li>\n<\/ul>\n
```bash\nsystemctl --user start docker-desktop\n```<\/pre>\nThe last command should launch a GUI window for Docker Desktop. You should now have a full installation of all the components required to work with Docker and a GUI application to handle it too!<\/p>\n
Installation of Docker Desktop can be performed for other Linux distributions or Windows by following the instructions given on their official website<\/a> as well.<\/p>\n
\nInstall NVIDIA Container Toolkit<\/strong><\/a><\/h3>\n
- \n
- Getting Started with Docker for Machine Learning<\/a><\/li>\n
- \n
- Overview: Why the Need?<\/a><\/li>\n
- What Are Containers?<\/a><\/li>\n
- How Do Containers Differ from Virtual Machines?<\/a><\/li>\n
- Most Popular Container Images<\/a><\/li>\n
- \n
- TensorFlow<\/a><\/li>\n
- PyTorch<\/a><\/li>\n
- Python<\/a><\/li>\n
- NGINX<\/a><\/li>\n<\/ul>\n
- Install Docker<\/a><\/li>\n
- Install Docker Manually<\/a><\/li>\n
- Install Docker Desktop<\/a><\/li>\n
- Install NVIDIA Container Toolkit<\/a><\/li>\n<\/ul>\n
- Summary<\/a><\/li>\n
- \n
- Citation Information<\/a><\/li>\n<\/ul>\n<\/ul>\n<\/div>\n
\nGetting Started with Docker for Machine Learning<\/strong><\/a><\/h2>\n
In this tutorial, you will learn what Containers are, how they differ from Virtual Machines (VMs), and what Docker is. Finally, we will top it off by installing Docker on our local machine with simple and easy-to-follow steps.<\/p>\n
\n<\/a><\/figure>\n<\/div>\nThis lesson is the 1st of a 3-part series on Docker for Machine Learning<\/strong>:<\/p>\n
- \n
- Getting Started with Docker for Machine Learning<\/em><\/strong><\/a> (this tutorial)<\/strong><\/li>\n
- Lesson 2<\/em><\/li>\n
- Lesson 3<\/em><\/li>\n<\/ol>\n
\nOverview: Why the Need?<\/strong><\/a><\/h3>\n
Envision yourself as an ML Engineer<\/strong> at one of the world\u2019s largest companies. You make a Machine Learning (ML) pipeline that does everything, from gathering and preparing data to making predictions. All the individual blocks are in the proper order, and you ensure everything works like a well-oiled machine.<\/p>\n
After verifying everything, you hand over this pipeline to a colleague to get a review before your pipeline is deployed to perform real-time streaming predictions<\/em>. But, alas! The pipeline broke down when moved from your computer to your colleague\u2019s computer.<\/p>\n
Switching gears, imagine yourself being part of a high-tech research lab working with Machine Learning algorithms. You quickly iterate with several different things daily, each with additional requirements and dependencies. One day, you are working with TPUs to run JAX code. Next, you\u2019re trying to make your app available to the world as an API.<\/p>\n
In these cases, you\u2019d also encounter annoying times when your entire environment is polluted by one dirty install. Repairing such issues becomes time-consuming<\/strong>, especially in research iteration where time is crucial<\/strong>.<\/p>\n
\nWhat Are Containers?<\/strong><\/a><\/h3>\n
In the cases we just saw, the problem patterns stem from the tight coupling<\/em> between the software<\/strong> and hardware<\/strong> we develop.<\/p>\n
This can be a major impediment<\/strong> to developing software that \u201cjust works\u201d on all systems. When we look at this problem in the Machine Learning context, this can bring quick iteration to a halt.<\/p>\n
What if we decouple<\/strong> the dependencies of the software we write from the hardware it works on? Could our software development environment be wrapped into a neat and simple package that can be shifted to any machine we want, irrespective of hardware or other dependency issues? <\/p>\n
Enter the concept of Containers<\/strong>. Containers are lightweight packages<\/em><\/strong> that act as portable, sandboxed environments for use on any hardware that supports them. This would include code, dependencies, files, operating systems, runtime, and system settings.<\/p>\n
Let\u2019s break that down. Containers are lightweight since they do not store Operating System (OS) images within them while using a snapshot<\/strong> instead. They virtualize the resources available to the OS for a specific process concerning CPU usage, memory, and storage. This allows your process to be portable and sandboxed from all other processes running on the host.<\/p>\n
Container runtimes are consistent, meaning they would work precisely the same whether you\u2019re on a Dell<\/em> laptop with an AMD<\/em> CPU, a top-notch MacBook Pro<\/em>, or an old Intel Lenovo ThinkPad<\/em> from 2015. <\/p>\n
One of the most popular methods of creating and working with containers is by using Docker<\/strong><\/a>. It is a platform that provides a batteries-included<\/em> experience for creating<\/strong>, deploying<\/strong>, and working<\/strong> with containers.<\/p>\n
Throughout this tutorial, we will use Docker as the software for manipulating and working with containers. Some other alternatives to Docker include LXC (Linux Container Runtime)<\/a> and Podman<\/a>.<\/p>\n
Note: <\/em><\/strong>Something of this sort would be useful to an ML Engineer such as yourself because it allows you to expose services from isolated runtimes and ensure horizontal scalability without any hassle.<\/em><\/p>\n
\nHow Do Containers Differ from Virtual Machines?<\/strong><\/a><\/h3>\n
A question that may have popped up by now should be how containers differ from traditional Virtual Machines<\/a>. Let\u2019s see how they diverge<\/strong>.<\/p>\n
The most notable difference between a virtual machine (VM) and a container is the level of virtualization. This refers to the introduction of an abstraction layer over the hardware at a level where the resources are allocated and utilized separately from the original OS on the machine. This abstraction layer is also referred to commonly as a hypervisor<\/a>. They tend to have several functions (e.g., creating and deleting VMs, managing, and scheduling them). VMs work to virtualize everything up from the hardware layers on a machine with this layer on top.<\/p>\n
\nDifference between a Container and a Virtual Machine (source: (https:\/\/upload.wikimedia.org\/wikipedia\/commons\/0\/0a\/Docker-containerized-and-vm-transparent-bg.png<\/a>).<\/figcaption><\/figure>\n<\/div>\n On the other hand, containers only virtualize up to the software layers of the container engine that handles all the containers present in a system. It does not go down to the hypervisor layer and instead hands over its own control to the original OS on the machine, which is controlled by the container runtime. The container runtime is like a hypervisor but made only on a software level for containers. It does not have direct access to the hardware of the machine.<\/p>\n
Virtual machines typically provide higher isolation and security by working directly with the hardware (depending on the type of hypervisor used). In contrast, Containers tend to simply isolate the specific process and filesystem while delegating the grunt work of allocating resources to the host OS itself via the container runtime.<\/p>\n
When working with different Operating Systems vis-\u00e0-vis Containers, they tend only to have a snapshot of an OS instead of the full OS image, making them significantly lighter than traditional VMs. But, this brings about a problem wherein we must ensure that the container\u2019s OS matches the same kernel as the original OS. That means the container\u2019s OS must have the same format for requesting machine resources as that of the host OS. For that reason, one would see that a Windows container cannot be used on Linux machines. In comparison, Linux containers may be created on Windows machines with the advent of WSL2<\/a>.<\/p>\n
\nMost Popular Container Images<\/strong><\/a><\/h3>\n
Today, popular software applications have container images available for direct use on any hardware that the user desires. Some notable examples include the following.<\/p>\n
\nTensorFlow<\/strong><\/a><\/h4>\n
The container runtime for TensorFlow<\/a> acts as a simple method to quickly experiment or iterate with TensorFlow in a throwaway fashion. These images also support interfacing with the GPU, meaning you can leverage it for training your Deep Learning networks written in TensorFlow. Note that, using a GPU typically refers to using NVIDIA CUDA-enabled GPUs (Alternative NVIDIA NGC Container Image here<\/a>).<\/p>\n
\nDocker and TensorFlow (source: image created by the author)<\/figcaption><\/figure>\n<\/div>\n
\nPyTorch<\/strong><\/a><\/h4>\n
The container runtime for PyTorch<\/a> brings torch and torchvision to your runtime, along with all dependencies pre-installed. This allows you to begin your development directly and provides a platform for quick iteration. Similar to TensorFlow, this container image also provides a GPU-compatible version that uses Compute Unified Device Architecture (CUDA) to speed up Tensor processing. (Alternative NVIDIA NGC Container Image here<\/a>)<\/p>\n
\nPython<\/strong><\/a><\/h4>\n
The container runtime for Python<\/a> sets up a Debian Linux instance with Python pre-installed. This is suitable for making a variety of Python applications with other dependencies being added to it at the user\u2019s convenience. This container is commonly used for simpler tasks (e.g., developing web services, APIs, running tests, etc.). Depending on their host platform, one can choose between using Debian Linux, Alpine Linux, or Windows Server Core versions of this image.<\/p>\n
\nNGINX<\/strong><\/a><\/h4>\n
The container runtime for NGINX<\/a> sets up a Debian Linux instance with NGINX pre-installed. This allows you to use the popular reverse proxy server with load-balancers and HTTP caches without the hassle of setting it up across different machines. This image is also available in various styles (e.g., the standard Debian Linux base or with an Alpine Linux base).<\/p>\n
\nInstall Docker<\/strong><\/a><\/h3>\n
With this information on why Docker containers are so popular, it\u2019s time we install it on our local machine and start making our containers for Machine Learning.<\/p>\n
To later work with all the required components of Docker, we install Docker Desktop. It includes a standalone Desktop GUI (graphical user interface) application that simplifies your experience with Docker.<\/p>\n
If you are using Ubuntu, Debian, Fedora, Red Hat Enterprise Linux (RHEL), CentOS, or other common Linux Distributions, you can use the official convenience script from Docker by executing the command below.<\/p>\n
```bash\ncurl https:\/\/get.docker.com | sh \\\n && sudo systemctl --now enable docker\n```<\/pre>\n
This should set up Docker completely on your system! Skip over the next section to see how you can install Docker Desktop for a GUI-based interface.<\/p>\n
If you want to improve your Docker experience for later, you can follow some convenience-enhancing steps<\/a> for Linux systems. Note that these are optional and are geared toward superusers.<\/p>\n
\nInstall Docker Manually<\/strong><\/a><\/h3>\n
Now, let\u2019s look at how to install Docker on a Linux system manually. For this example, we will look at a Fedora Linux setup with GNOME. Note, if you are using Ubuntu or any other Linux distribution, you can simply replace most of these commands with the distribution\u2019s respective package manager (\u201capt\u201d for Ubuntu, \u201cpacman\u201d for Arch Linux, \u201cbrew\u201d for Mac, etc.).<\/p>\n
- \n
- Ensure you have
gnome-terminal<\/code> installed (or the appropriate terminal as required). To do so, use the following command:<\/li>\n<\/ul>\n```bash \n>> sudo dnf install gnome-terminal\n```<\/pre>\n
- \n
- Set up the Docker package repositories<\/li>\n<\/ul>\n
```bash\n>> sudo dnf -y install dnf-plugins-core\n>> sudo dnf config-manager --add-repo https:\/\/download.docker.com\/linux\/fedora\/docker-ce.repo\n```<\/pre>\n
- \n
- Install Docker Engine, containerd, Docker build kit, Docker Compose, and related command line interfaces (CLIs)<\/li>\n<\/ul>\n
```bash\n>> sudo dnf install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin\n```<\/pre>\n
Once this process is complete, you will have a working installation of Docker on your system!<\/p>\n
\nInstall Docker Desktop<\/strong><\/a><\/h3>\n
Now, let\u2019s look at how to install Docker Desktop on your system. Follow along!<\/p>\n
- \n
- Download the RPM (Red Hat Package Management system) file for Docker Desktop<\/a> (Note:<\/em><\/strong> This link may change in the future. To get the latest link, please look here<\/a>.)<\/li>\n
- Use the following command to install it:<\/li>\n<\/ul>\n
```bash\n>> sudo dnf install .\/docker-desktop-<version>-<arch>.rpm\n```<\/pre>\n
- \n
- Start Docker Desktop<\/li>\n<\/ul>\n
```bash\nsystemctl --user start docker-desktop\n```<\/pre>\n
The last command should launch a GUI window for Docker Desktop. You should now have a full installation of all the components required to work with Docker and a GUI application to handle it too!<\/p>\n
Installation of Docker Desktop can be performed for other Linux distributions or Windows by following the instructions given on their official website<\/a> as well.<\/p>\n
\nInstall NVIDIA Container Toolkit<\/strong><\/a><\/h3>\n
- Use the following command to install it:<\/li>\n<\/ul>\n
- Download the RPM (Red Hat Package Management system) file for Docker Desktop<\/a> (Note:<\/em><\/strong> This link may change in the future. To get the latest link, please look here<\/a>.)<\/li>\n
- Install Docker Engine, containerd, Docker build kit, Docker Compose, and related command line interfaces (CLIs)<\/li>\n<\/ul>\n
- Set up the Docker package repositories<\/li>\n<\/ul>\n
- Lesson 2<\/em><\/li>\n
- Getting Started with Docker for Machine Learning<\/em><\/strong><\/a> (this tutorial)<\/strong><\/li>\n
- PyTorch<\/a><\/li>\n
- What Are Containers?<\/a><\/li>\n
- Overview: Why the Need?<\/a><\/li>\n
![\"\"](\"https:\/\/lh4.googleusercontent.com\/rsH5fPcW7CerGvW68-liXjh7KneKRQ_hbNiyPZ7Z72V_JJ-rc5r4rB2a2imPpIEkic2kvCdm23f1IavCwbZFeeGccDYnu8Jq8EHSn4LZ3Otk-EkLgfdNqrEWSxGvjaHvLT-u1xYZnDWPzUoMtIdG2T0\")
<\/a>