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What Is a Docker Container and How Is It Hosted?

What Is a Docker Container and How Is It Hosted?
Hostman Team
Technical writer
Infrastructure

Want to know what a Docker is? Need to know how to copy files from your host to a Docker container? Read this article to learn everything about Docker containers and Docker container hosting.

There are few technologies as popular as Docker. A lot of developers and administrators buzz about it. Docker is everywhere and we all must say that even if you’re not a DevOps engineer it is important to understand what Docker containers are, how these things work, and why they are so useful. Well, let’s figure it out.

What is a Docker container?

It is hard to answer the question "what is a Docker container" without explaining what containerization is. Containerization is an alternative way to create virtual machines. Instead of using hardware virtualization developers create software copies of computers where their applications are launched and run.

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Docker is an open-source piece of software that is used to develop, test, deploy and run web applications in a special virtual environment. Docker helps to reduce the number of hardware resources needed to maintain running applications in a stable and efficient manner. Also, it is one of the best tools for developers who want to launch pre-made software products quickly, upscale them or move them to other environments without worrying about the reliability of their project or any other issues.

Container is a product of containerization. It is in essence quite similar to a virtual machine but a lot smaller and dedicated to maintaining only one application rather than a fully-fledged OS with a large number of software tools.

This kind of virtualization works like this:

  • We put all the necessary components of the web application into isolated software environments (virtual blocks).

  • Every block contains everything we need to launch an app properly.

  • Every block can be initiated multiple times.

So, containers are small virtual machines that help developers relaunch an application many times on one computer, and maintain this squadron of apps ensuring reliability and high performance.

How does Docker work?

The main part of aDocker is the Docker Engine. It consists of a server, REST API, and a client. The first is responsible for initializing and controlling containers, the second works as an interpreter between a user and a Docker, and the third is used to pass CLI commands to the Docker Engineserver.

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This is the way it works:

  • The user sends a CLI command to the Docker server via the Docker client. For example, a command that creates a new container or pulls the image of an OS with pre-installed software tools that the user wants to use in his project.

  • Then the Docker server analyses the command and configuration data written in Dockerfile (a kind of document that consists of Docker settings) and performs the actionsthe user’s command dictates..

  • After the command is executed the Docker container is activated.

So the main idea is that the developer communicates with an app in a virtual environment using special software layers created by Docker.

Docker advantages

There are 6 main advantages of using Docker instead of standard virtual machines:

  1. Docker saves computer resources. Instead of emulating OS it uses core components of Linux distributive installed on a server and creates containers for applications like database, interface, etc.

  2. Docker saves your time. You don’t have to set up every virtual machine. Set up just one and Docker will use core elements of it to create containers with the application running inside.

  3. It protects you from malware. Docker containers are isolated from the core OS. So you can run any code inside the virtual block without worrying about malware corrupting your server.

  4. It’s a piece of cake to scale. The only thing you have todo to scale your project — add new containers. The scope of the project depends on the number of virtual blocks used and nothing else.

  5. Docker can launch apps on any compatible host. So you don’t have to spend time setting everything up before starting containerized software.

  6. Docker works smoother than most virtualization technologies. It uses "integrated layer system" technology as an efficient file system.

Why are Docker containers popular?

You’re probably wondering, why should you be using Docker containers in the first place? Well, the idea behind Docker is really simple. That’s why this technology have become so popular among developers,container and Docker Engine server administrators, testers, programmers, and many others well.

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It is often utilized in projects connected with large web services because of its easily scalable system where DevOps specialists can create new iterations of the app in only a few commands.

Also, administrators love Docker because of its monitoring system:it is easy to keep an eye on the whole system and individual components with containers.

How are Docker containers hosted?

Why should you host Docker containers?

The Docker ecosystem with containers and static files is an app, like classic websites or web applications. To make it accessible to users online you have to host it somewhere. On a remote PC that will maintain your service or application day and night .

Docker container hosts are not so different from any other hosts that DevOps specialists or developers work with. But it is really important to choose within the hosts the one that will give you the reliable, fully-fledged platform for your project.

What is a Docker host and how do we work with them? Generally, a Docker host is hardware that serves as a platform for the operating system and Docker containers;basically we are talking about the server. A computer that is placed somewhere in the host’s data center and works 24/7 to maintain your web application or website.

To work with containers (e.g. develop, test, deploy and run applications) you need a proper server. It can be a virtual private server or a dedicated server. Most of developers believe that dedicated is the best option. Some think that VPS is safer to use but the easiest way to host Docker containers is to use hosting platforms like Hostman. There’s an option in this platform that makes it possible to use GitHub (GitLab and Bitbucker are also supported) repository with your code and, in a few clicks, deploy it. All you have to do is:

  • connect your repository with the Dockerfile to a Hostman profile,

  • choose the option to deploy thea Docker container,

  • configure the host (server),

  • and wait till the rest is done almost automatically.

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Hostman will find all the necessary files and create an instance of your Docker service. In 3 simple steps, you’ll get ready for the work environment.

Hostman not only simplifies the process of deploying websites, applications, and complex IT infrastructures but grants users a secure and trustworthy server for projects of any type. Moreover, services like Hostman dramatically reduce costs of DevOps specialists because hosting will:

  • Set up a cloud server for you.

  • Install all dependencies.

  • Build the code.

  • Set up CDN for static files.

  • Install SSL certificate.

  • Monitor the app to relaunch it if it fails.

Essentially, Hostman becomes your DevOps engineer.

How are Docker containers connected to the host?

At the very beginning when we create a Docker image and are ready to launch the container for the first time, we use the command docker run. But it won’t work as expected because a developer has to forward ports so containers gain access to the host and vice versa.

It is necessary to connect Docker containers to the host because sometimes a lot of project files are saved on a local machine but are designed to be managed and accessible within containers.

So, to access the host from the Docker container, the developer has to use additional options while launching virtual machines. For example:

docker run -d -p 8099:8080 [name of the Docker container]

That will make it possible for the newly created virtual machine to interact with the local machine.

How to access the Docker container from your host?

It is quite easy.

  • First, open the directory where your Docker project is saved.

  • Then launch command docker container ls, so you can see all the containers that exist on your server.

  • And then launch command docker exec -it [name of the Docker container] /bin/bash.

That’s it. After accessing the docker container from the host as described you can manipulate your container as you wish.

How does file management work in Docker containers?

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Sometimes you need to move files between virtual machines and your local machine. For instance, to access logs. Or to bring some data to the local database. Let’s get into how to copy a file from host to Docker container and vice versa.

File management within Docker containers and Docker host is quite similar to what developers used to see in the Linux terminal. The commanders are almost the same for moving and copying files.

Let’s start with copying. It’ll give you an idea about everything else. For example, you have a task to copy a file from the Docker container to the host. You can do it using the command docker cp:

docker cp [options] [path to file in container] [path to host]
docker cp filename:/usr/local/apache2/conf/httpd.conf /home/fedora/dockers/httpd/

The command above copies the configuration file of Apache server from the Docker container to a local machine.

It works either way. If you need to copy a file from host to Docker container use the same command:

docker cp [options] [path to local file] [path to a container]

It’s dead simple. And it works, not only for files, but for directories.

How do you copy files from the Docker container to the host?

Usually, to move files from a docker container to a host (or the other way around) developers do the following:

  • Get access to the Docker container.

  • Copy files to host.

  • Remove files from the Docker container.

There are ways to use commands like RUN mv but they frequently fail to launch.

What is a Docker host IP?

By this term users and developers usually mean one of two different things.

The first one is the IP address that is used by people who use containerized applications. Of course, users access the Docker app using a domain name. But IP also exists and works the same way as it works with different websites and classic web applications. This is what’s also called Bridge address:the bridge between users and your application.

The second is the IP address of the Docker container itself which is used by different containers on one host to communicate with each other, stay connected, and work seamlessly managing common data (databases, logs, etc.). Put simply, as a result of interaction between these containers with different IPs, the Bridge IP is generated and makes it possible for users to access the app.

It is necessary to know the IP of certain Docker containers to make the interaction between them manageable.

How to get IP from a Docker container?

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There are three ways to get a Docker container’s IP address:

  1. The easiest way is to use the command docker inspect. Using the command line you must write something like docker inspect [name of your docker container]. If you don’t know the name of the container you need to inspect, you might want to use the command docker ps which will show you all the containers launched at the moment. Also, instead of a specific container, you can access the data from the whole network so you’ll see IPs for every container.

  2. Another method is docker exec. Using the command line you must write docker exec [name of the container] cat /etc/hosts. After that, you’ll see the result with local address and IP address at the last line of code.

  3. The third and final method — using docker exec but inside the Docker container. It is the same command we use for accessing containers but with a small addition. Using the command line you must write docker exec -it [name of the container]. After that, you can access the container’s data. To get the IP address just write ip -4 -o address and CL will return the IP of the container you’re in. Also, it is possible to get data from other containers. Just write ping [name of another docker] container. It’ll show the IP address of a pinged resource.

Summary

So, here we are. That’s the basics of Docker. Simple commands to launch it, move files around, etc. But the coolest thing is that these commands are all that you need because services like Hostman will automate the process of deploying and basic setup.

You just need to choose a configuration of a web server on the Hostman’s website and connect it with your GitHub, GitLab, or Bitbucket account. It will cost you $5.5/month and you can try all the functions for free during the 7-days trial period.

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Characteristic Description Significance for ML VRAM Volume Memory for storing models and data Large models require 24-80 GB CUDA Cores / Tensor Cores Blocks for parallel computing Accelerate training, especially FP16 Framework Support Compatibility with PyTorch, TensorFlow, JAX Simplifies development Power Consumption Consumed power (W) Affects expenses and cooling Price/Performance Balance of cost and speed Optimizes budget Video Memory Volume (VRAM) VRAM determines how much data and model parameters can be stored on the GPU. For simple tasks such as image classification, 8-12 GB is sufficient. However, for large models, including LLMs or generative networks, 24-141 GB is required (like the Tesla H200). Lack of VRAM leads to out-of-memory errors, which can stop training. Case: A fintech startup uses Tesla A6000 with 48 GB VRAM for transaction analysis, accelerating processing by 40%. Recommendation: Beginners need 12-16 GB, but for corporate tasks choose 40+ GB. Number of CUDA Cores and FP16/FP32 Performance CUDA cores (for NVIDIA) or Stream Processors (for AMD) provide parallel computing. More cores mean higher speed. For example, Tesla H200 with approximately 14,592 cores outperforms RTX 3060 with approximately 3,584 cores. Tensor Cores accelerate low-precision operations (FP16/FP32), which is critical for modern models. Case: An automotive company trains autonomous driving models on Tesla H100, reducing test time by 50%. For business, this means development savings. Library and Framework Support (TensorFlow, PyTorch) A graphics card for AI must support popular frameworks: TensorFlow, PyTorch, JAX. NVIDIA leads thanks to CUDA, but AMD with ROCm is gradually catching up. Without compatibility, developers spend time on optimization, which slows down projects. Case: A marketing team uses PyTorch on Tesla A100 for A/B testing advertising campaigns, quickly adapting models to customer data. Power Consumption and Cooling Modern GPUs consume 200-700W, requiring powerful power supplies and cooling systems. In 2025, this is relevant for servers and data centers. Overheating can lead to failures, which is unacceptable for business. Case: A logistics company uses water cooling for a GPU cluster, ensuring stable operation of forecasting models. Price and Price-Performance Ratio The balance of price and performance is critical for return on investment (ROI) and long-term efficiency of business projects. For example, Tesla A6000, offering 48 GB VRAM and high performance for approximately $5,000, pays for itself within a year in projects with large models, such as financial data processing or training complex neural networks. However, choosing the optimal graphics card for neural networks depends not only on the initial cost, but also on operating expenses, including power consumption and the need for additional equipment, such as powerful power supplies and cooling systems. For small businesses or beginning developers, a graphics card for machine learning, such as RTX 3060 for $350-500, can be a reasonable start. It provides basic performance for educational tasks, but its limited 12 GB VRAM and approximately 3,584 CUDA cores won't handle large projects without significant time costs. On the other hand, for companies working with generative models or big data analysis, investing in Tesla H100 for $20,000 and more (depending on configuration) is justified by high training speed and scalability, which reduces overall costs in the long term. It's important to consider not only the price of the graphics card itself, but also additional factors, such as driver availability, compatibility with existing infrastructure, and maintenance costs. For example, for corporate solutions where high reliability is required, Tesla A6000 may be more profitable compared to cheaper alternatives, such as A5000 ($2,500-3,000), if we consider reduced risks of failures and the need for frequent equipment replacement. Thus, the price-performance ratio requires careful analysis in the context of specific business goals, including product time-to-market and potential benefits from accelerating ML processes. Best Graphics Cards for AI in 2025 The GPU market in 2025 offers the best solutions for different budgets and tasks. Optimal Solutions for Beginners (under $1,000) For students and small businesses, the best NVIDIA graphic card for AI would be RTX 4060 Ti (16 GB, approximately $500). This graphics card will handle educational tasks excellently, such as data classification or small neural networks. RTX 4060 Ti provides high performance with 16 GB VRAM and Tensor Cores support. Alternative: AMD RX 6800 (16 GB, approximately $500) with ROCm for more complex projects. Case: A student trains a text analysis model on RTX 4060 Ti. Mid-Range: Balance of Power and Price NVIDIA A5000 (24 GB, approximately $3,000) is a universal choice for medium models and research. It's suitable for tasks like data analysis or content generation. Alternative: AMD Radeon Pro W6800 (32 GB, approximately $2,500) is a powerful competitor with increased VRAM and improved ROCm support, ideal for medium projects. Case: A media company uses A5000 for generative networks, accelerating video production by 35%. Professional Graphics Cards for Advanced Tasks Tesla A6000 (48 GB, approximately $5,000), Tesla H100 (80 GB, approximately $30,000), and Tesla H200 (141 GB, approximately $35,000) are great for large models and corporate tasks. Alternative: AMD MI300X (64 GB, approximately $20,000) is suitable for supercomputers, but inferior in ecosystem. Case: An AI startup trains a multimodal model on Tesla H200, reducing development time by 60%. NVIDIA vs AMD for AI NVIDIA remains the leader in ML, but AMD is actively catching up. The choice depends on budget, tasks, and ecosystem. Here's a comparison: Parameter NVIDIA AMD Ecosystem CUDA, wide support ROCm, limited VRAM 12-141 GB 16-64 GB Price More expensive Cheaper Tensor Cores Yes No Community Large Developing Why NVIDIA is the Choice of Most Developers NVIDIA dominates thanks to a wide range of advantages that make it preferred for developers and businesses worldwide: CUDA: This platform has become the de facto standard for ML, providing perfect compatibility with frameworks such as PyTorch, TensorFlow, and JAX. Libraries optimized for CUDA allow accelerating development and reducing costs for code adaptation. Tensor Cores: Specialized blocks that accelerate low-precision operations (FP16/FP32) provide a significant advantage when training modern neural networks, especially in tasks requiring high performance, such as generative AI. Energy Efficiency: The new Hopper architecture demonstrates outstanding performance-to-power consumption ratio, which reduces operating costs for data centers and companies striving for sustainable development. Community Support: A huge ecosystem of developers, documentation, and ready-made solutions simplifies the implementation of NVIDIA GPUs in projects, reducing time for training and debugging. Case: A retail company uses Tesla A100 for demand forecasting, reducing costs by 25% and improving forecast accuracy thanks to broad tool support and platform stability. AMD GPU Capabilities in 2025 AMD offers an alternative that attracts attention thanks to competitive characteristics and affordable cost: ROCm: The platform is actively developing, providing improved support for PyTorch and TensorFlow. In 2025, ROCm becomes more stable, although it still lags behind CUDA in speed and universality. Price: AMD GPUs, such as MI300X (approximately $20,000), are the best budget GPUs for AI, as they are significantly cheaper than NVIDIA counterparts. It makes them attractive for universities, research centers, and companies with limited budgets. Energy Efficiency: New AMD architectures demonstrate improvements in energy consumption, making them competitive in the long term. HPC Support: AMD cards are successfully used in high-performance computing, such as climate modeling, which expands their application beyond traditional ML. Case: A university uses MI300X for research, saving 30% of budget and supporting complex simulations thanks to high memory density. However, the limited ROCm ecosystem and smaller developer community may slow adoption and require additional optimization efforts. Local GPU vs Cloud Solutions Parameter Local GPU Cloud Control Full Limited Initial Costs High Low Scalability Limited High When to Use Local Hardware Local GPUs are suitable for permanent tasks where autonomy and full control over equipment are important. For example, the R&D department of a large company can use Tesla A6000 for long-term research, paying for itself within a year thanks to stable performance. Local graphics cards are especially useful if the business plans intensive daily GPU use, as this eliminates additional rental costs and allows optimizing infrastructure for specific needs. Case: A game development company trains models on local A6000s, avoiding cloud dependency. Additionally, local solutions allow configuring cooling and power consumption for specific conditions, which is important for data centers and server rooms with limited resources. However, this requires significant initial investments and regular maintenance, which may not be justified for small projects or periodic tasks. Pros and Cons of Cloud Solutions Cloud solutions for GPU usage are becoming a popular choice thanks to their flexibility and accessibility, especially for businesses seeking to optimize machine learning costs. Let's examine the key advantages and limitations to consider when choosing this approach. Pros: Scalability: You can add GPUs as tasks grow, which is ideal for companies with variable workloads. This allows quick adaptation to new projects without needing to purchase new equipment. Flexibility: Paying only for actual usage reduces financial risks, especially for startups or companies testing new AI solutions. For example, you can rent Tesla A100 for experiments without spending $20,000 on purchase. Access to Top GPUs: Cloud providers give access to cutting-edge models that aren't available for purchase in small volumes or require complex installation. Updates and Support: Cloud providers regularly update equipment and drivers, relieving businesses of the need to independently monitor technical condition. Cons: Internet Dependency: Stable connection is critical, and any interruptions can stop model training, which is unacceptable for projects with tight deadlines. Long-term Costs: With intensive use, rental can cost more than purchasing local GPU. Case: A startup tests models on a cloud server with Tesla H100, saving $30,000 on GPU purchase and quickly adapting to project changes. However, for long-term tasks, they plan to transition to local A6000s to reduce costs. Conclusion Choosing a graphics card for neural networks and ML in 2025 depends on your tasks. Beginners should choose NVIDIA RTX 4060 Ti, which will handle educational projects and basic models. For the mid-segment, A5000 is a good solution, especially if you work with generative models and more complex tasks. For business and large research, Tesla A6000 remains the optimal choice, providing high video memory volume and performance. NVIDIA provides the best graphic cards for AI and maintains leadership thanks to the CUDA ecosystem and specialized Tensor Cores. However, AMD is gradually strengthening its position, offering ROCm support and more affordable solutions, making the GPU market for ML and AI increasingly competitive.
30 September 2025 · 12 min to read

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