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Sharing Data Between the Docker Container and the Host
Docker
03.05.2024
Reading time: 6 min
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One of the reasons Docker is so popular is that it allows containerization without installing additional dependencies on your local machine. Docker uses virtualization technology that creates an isolated environment for running applications.
The Docker engine assigns host resources to an isolated application in a container. Once such an application is containerized, its virtualized data is isolated from the host, causing all application data to remain in the container.
However, the application often needs to share data between the Docker container and the host computer.
By default, all files created in a container are stored in it. When the container stops, data is lost. Docker has volumes that you can use to store data on the host system so that it is not lost even after the container is stopped.
In this article, we will show you how to organize data sharing so that your app can access the host data from a Docker container.
Prerequisites
To follow this guide, you will need:
-
A cloud server running a Linux-based OS (Ubuntu, Debian, CentOS etc) with Docker installed. You can deploy your Linux server on Hostman and even install Docker with one click.
-
A root user or a non-root user with sudo privileges.
Preparation
To demonstrate data sharing between the host and the container, we will use Nginx and HTML pages.
Let's pull the Nginx image from the Docker repository using the following command:
docker pull nginxYou can check whether the image was loaded successfully using the docker image ls command. The terminal should display a list of downloaded images:
In Docker, a volume is a mechanism for storing and managing data used inside containers. You can think of a volume as a separate file system that can be attached to one or more containers.
The main benefit of using volumes in Docker is that they allow you to persist data between container runs and even between deleting and creating new containers. Volumes can also be used to share data between containers, for example, when multiple containers need to access a common file system.
Docker provides many tools for working with volumes, including commands for creating, deleting, mounting, and unmounting volumes. It also allows you to specify which volume should be used in the container when it starts.
In Docker, to access the host from the container, you can use an existing directory in the host file system (bind mount) or a new volume entity. We will look at both methods.
Bind mount
By default, applications in Docker containers are stateless. This means the application does not leave any data after its operation. Therefore, Docker provides various methods for storing data. In this section, we will talk about bind mount.
Bind mount implements the following operating principle: the data file is stored on the host and opened inside the container. Because the data is stored on the host, it is not lost when the container shuts down.
We will use an HTML page as the data file.
To use bind mount, follow these steps:
Step 1. Create a directory with the HTML file:
mkdir -p /tmp/nginx/htmlStep 2. Build and launch the container with the Nginx image using the docker run command:
docker run -t -d -P -v /tmp/nginx/html:/usr/share/nginx/html --name nginxcont nginx:latestThe parameters to this command will launch a container named nginxcont with the Nginx image and pass the HTML directory to the container:
-
-twill allocate a pseudo-TTY for interactive interaction with the container; -
-dwill start the container in the background; -
-Pwill bind the container to a random port; -
-vallows us to mount the host folder into a container in Docker.
After executing the command, check the running containers:
docker container lsStep 3. Find out the IP address.
To find out the IP address of the Docker host, enter the command below:
hostname -ITo find the port number, check the PORTS column in the Step 2 or simply run docker container ls.
Go to your browser and enter the following URL:
http://<ip_address>:<port>Since we don't have an HTML file in the directory, the content should be something like this:
Step 4: Testing
In the /tmp/nginx/html directory, create a file index.html:
nano /tmp/nginx/html/index.htmlAnd add the following lines of code to it:
<!DOCTYPE html>
<html>
<meta charset="utf-8">
<head>
<title>Checking the functionality of bind mount</title>
</head>
<body>
<h1>This HTML file is taken from the host machine</h1>
<p>
If you see this page, then everything is working. The file was taken from the /tmp/nginx/html directory and transferred to the container via the console.
</p>
<p>
</p>
<p><em>Hostman Tutorials</em></p>
</body>
</html>
To save the file press Ctrl+X and then Y.
Once the file is created, refresh your browser tab and you will see the new content:
If you edit the file, you will see the new changes after refreshing the page.
Now you know how to share files from your host machine to a Docker container using bind mount.
If you see the error "localhost didn’t send any data", check:
-
the directory in which the container is running;
-
whether the container is running;
-
is the port was assigned to another application.
Volume
To use volumes for data sharing between a host and a container, follow these steps.
Step 1: Create a volume.
You can create a new volume with this command:
docker volume create simplevolWhere simplevol is the name of the new volume.
Now list all volumes:
docker volume lsWe will see:
Step 2: Start a Docker container.
Launch a new container (nginxcont1 in our example) with an NGINX image:
docker run -t -d -P -v simplevol:/usr/share/nginx/html --name nginxcont1 nginx:latestThe volume on localhost is mapped to a directory inside the container from which NGINX takes the HTML.
To check the state of the container, run this command:
docker container lsNote the port number in the PORTS column too, we will need it in the next step.
Step 3: Find out the IP address.
To get the host's IP address, run:
ifconfigThe inet parameter will show your IP address. In our case, it is 172.19.0.1.
Now, go to http://<ip_address>:<port>, specifying your IP address and the port number from Step 2.
You should be greeted with the standard NGINX page.
Step 4. Testing.
Copy the index.html file from the Docker container to the host computer using the following command:
docker cp nginxcont1:/usr/share/nginx/html/index.html index.htmlOpen the file using an editor, update it as follows and save:
<!DOCTYPE html>
<html>
<meta charset="utf-8">
<head>
<title>Checking the performance of volumes</title>
</head>
<body>
<h1>This HTML code runs in a container with an NGINX image</h1>
<p>
If you see this page, everything is working. The index.html file is stored in the volume and shared with the container via the console.
</p>
<p>
</p>
<p><em>Hostman Tutorials</em></p>
</body>
</html>You can copy the edited file back from your computer to the Docker container by running this command:
docker cp index.html nginxcont1:/usr/share/nginx/htmlIf you refresh the previously opened tab, the content will update:
Conclusion
In this article, we looked at two ways to share data between the host and the container. If you use Docker volumes, you can also use them to share data between different environments running in the Docker environment.
Docker
03.05.2024
Reading time: 6 min
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Docker
Running Selenium with Chrome in Docker
Sometimes, it’s useful to work with Selenium in Python within a Docker container. This raises questions about the benefits of using such tools, version compatibility between ChromeDriver and Chromium, and the nuances of their implementation. In this article, we’ll cover key considerations and provide solutions to common issues.
Why Run Selenium in Docker?
Running Selenium in a container offers several advantages:
Portability: Easily transfer the environment between different machines, avoiding version conflicts and OS-specific dependencies.
Isolation: The Selenium container can be quickly replaced or updated without affecting other components on the server.
CI/CD Compatibility: Dockerized Selenium fits well into CI/CD pipelines — you can spin up a clean test environment from scratch each time your system needs testing.
Preparing an Ubuntu Server for Selenium with Docker
First, make sure Docker and Docker Compose are installed on the server:
docker --version && docker compose version
In some Docker Compose versions, the command is docker-compose instead of docker compose.
If the tools are installed, you’ll see output confirming their versions. If not, follow this guide.
Selenium in Docker Example
When deploying Selenium in Docker containers, consider the host architecture, functional requirements, and performance. Official selenium/standalone-* images are designed for AMD64 (x86_64) CPUs, while seleniarm/standalone-* images are adapted for ARM architectures (e.g., Apple silicon or ARM64 server CPUs).
First, create a docker-compose.yml file in your project root. It will contain two services:
version: "3"
services:
app:
build: .
restart: always
volumes:
- .:/app
depends_on:
- selenium
platform: linux/amd64
selenium:
image: selenium/standalone-chromium:latest # For AMD64
# image: seleniarm/standalone-chromium:latest # For ARM64
container_name: selenium-container
restart: unless-stopped
shm_size: 2g
ports:
- "4444:4444" # Selenium WebDriver API
- "7900:7900" # VNC Viewer
environment:
- SE_NODE_MAX_SESSIONS=1
- SE_NODE_OVERRIDE_MAX_SESSIONS=true
- SE_NODE_SESSION_TIMEOUT=300
- SE_NODE_GRID_URL=http://localhost:4444
- SE_NODE_DETECT_DRIVERS=false
You must choose the correct image for your system architecture by uncommenting the appropriate line.
The app service will run your main Python code. Let’s define a standard Dockerfile for this service:
# Use a minimal Python image
FROM python:3.11-slim
# Set working directory
WORKDIR /app
# Install Python dependencies
COPY requirements.txt /app/
RUN pip install --no-cache-dir -r requirements.txt
# Copy project files
COPY . /app/
# Set environment variables (Chromium is in a separate container)
ENV SELENIUM_REMOTE_URL="http://selenium:4444/wd/hub"
# Run Python script
CMD ["python", "main.py"]
This Dockerfile uses a base Python image and automatically installs the necessary dependencies.
Now let’s add the driver initialization script to main.py:
import time # Used to create a delay for checking browser functionality
import os
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.chrome.options import Options
# WebDriver settings
chrome_options = Options()
chrome_options.add_argument("--no-sandbox")
chrome_options.add_argument("--disable-dev-shm-usage")
chrome_options.add_argument("--disable-gpu")
chrome_options.add_argument("--disable-webrtc")
chrome_options.add_argument("--hide-scrollbars")
chrome_options.add_argument("--disable-notifications")
chrome_options.add_argument("--start-maximized")
SELENIUM_REMOTE_URL = os.getenv("SELENIUM_REMOTE_URL", "http://selenium:4444/wd/hub")
driver = webdriver.Remote(
command_executor=SELENIUM_REMOTE_URL,
options=chrome_options
)
# Open a test page
driver.get("https://www.timeweb.cloud")
time.sleep(9999)
# Shut down WebDriver
driver.quit()
In the requirements.txt file, list standard dependencies, including Selenium:
attrs==25.1.0
certifi==2025.1.31
h11==0.14.0
idna==3.10
outcome==1.3.0.post0
PySocks==1.7.1
selenium==4.28.1
sniffio==1.3.1
sortedcontainers==2.4.0
trio==0.28.0
trio-websocket==0.11.1
typing_extensions==4.12.2
urllib3==2.3.0
websocket-client==1.8.0
wsproto==1.2.0
Now you can launch the containers:
docker compose up -d
Expected output: Docker will build and launch the containers.
To verify everything is running correctly:
docker compose ps
You should see two running containers which means everything was loaded successfully. You can now integrate a script in main.py to interact with any site.
Debugging Selenium in Docker with VNC
In official Selenium Docker images (like seleniarm/standalone-chromium, selenium/standalone-chrome, etc.), direct access to the Chrome DevTools Protocol is usually overridden by Selenium Grid. It generates a new port for each session and proxies it via WebSocket.
Arguments like --remote-debugging-port=9229 are ignored or overwritten by Selenium, making direct browser port access impossible from outside the container.
Instead, these Docker images offer built-in VNC (Virtual Network Computing), similar to TeamViewer or AnyDesk, but working differently.
VNC requires headless mode to be disabled, since it transmits the actual screen content — and if the screen is blank, there will be nothing to see.
You can connect to the VNC web interface at:
http://<server_ip>:7900
When connecting, you'll be asked for a password. To generate one, connect to the selenium-container via terminal:
docker exec -it selenium-container bash
Then enter:
x11vnc -storepasswd
You’ll be prompted to enter and confirm a password interactively.
Enter the created password into the VNC web interface, and you’ll gain access to the browser controlled by Selenium inside Docker.
From there, you can open DevTools to inspect DOM elements or debug network requests.
Conclusion
Running Selenium in Docker containers simplifies environment portability and reduces the risk of version conflicts between tools. It also allows visual debugging of tests via VNC, if needed. Just make sure to choose the correct image for your system architecture and disable headless mode when a graphical interface is required. This provides a more flexible and convenient infrastructure for testing and accelerates Selenium integration into CI/CD pipelines.
19 June 2025 · 5 min to read
Docker
Building Docker Images and Deploying Applications
Containerizing applications offers a convenient and flexible way to quickly deploy software, including web servers, databases, monitoring systems, and others. Containers are also widely used in microservices architectures. Docker is ideal for these purposes, as it greatly simplifies working with containerized apps. Introduced in 2013, Docker has seen continuous support and usage ever since.
In this tutorial, you’ll learn how to create Docker images for three different applications written in different programming languages and how to run Docker containers from these images.
Prerequisites
To work with the Docker platform, you’ll need:
A VPS or virtual machine with any Linux distribution preinstalled. In this tutorial, we use Ubuntu 22.04.
Docker installed. You can find the Docker installation guide for Ubuntu 22.04 in our tutorials. Alternatively, you can use a prebuilt cloud server image with Docker — just select it in the “Marketplace” tab when creating a server.
What Is a Docker Image?
At the core of Docker’s concept is the image. A Docker image is a template—an executable file—you can use to start a Docker container. It contains everything needed to launch a ready-to-run application: source code, configuration files, third-party software, utilities, and libraries.
Docker image architecture is layer-based. Each layer represents an action performed during the image build process, such as creating files and directories or installing software. Docker uses the OverlayFS file system, which merges multiple mount points into one, resulting in a unified directory structure.
You can move Docker images between systems and use them in multiple locations, much like .exe executables in Windows systems.
Creating Custom Docker Images
Let’s walk through how to create Docker images for Flask, Node.js, and Go applications.
Creating a Docker Image for a Flask Application
To create images, a Dockerfile is used. Dockerfile is a plain text file without an extension that defines the steps to build a container image. You can find more details about Dockerfile instructions in the official documentation.
We’ll create a Docker image with a web application built with Flask and run the container. The application will show a basic HTML page that displays the current date.
1. Install Required Packages
Install the pip package manager and python3-venv for managing virtual environments:
apt -y install python3-pip python3-venv
2. Create the Project Directory
mkdir dockerfile-flask && cd dockerfile-flask
3. Create and Activate a Virtual Environment
python -m venv env
source env/bin/activate
After activation, you'll see (env) in your prompt, indicating the virtual environment is active. Packages installed via pip will now only affect this environment.
4. Install Flask and Dependencies
pip install flask
pip install MarkupSafe==2.1.5
5. Create the Flask Application
Create a file named app.py that will store the source code of our application:
from flask import Flask
import datetime
app = Flask(__name__)
@app.route('/')
def display_current_date():
current_date = datetime.datetime.now().date()
return f"Current date is: {current_date}"
if __name__ == '__main__':
app.run(debug=True)
6. Run and Test the Application
flask run --host=0.0.0.0 --port=80
In your browser, visit your server’s IP address (port 80 doesn’t need to be specified as it’s the default one). You should see today’s date.
7. Freeze Dependencies
Now, we need to save all the dependencies (just the flask package in our case) to a requirements.txt file, which stores all packages used in the project and installed via pip.
pip freeze > requirements.txt
Your project structure should now look like this:
dockerfile-flask/
├── app.py
├── env/
├── requirements.txt
Now we can proceed to creating a Docker image.
8. Create the Dockerfile
Create a file named Dockerfile with the following contents:
FROM python:3.8-slim-buster
WORKDIR /app
COPY requirements.txt requirements.txt
RUN pip3 install -r requirements.txt
COPY . .
CMD [ "python3", "-m", "flask", "run", "--host=0.0.0.0", "--port=80" ]
Explanation:
FROM python:3.8-slim-buster: Use Python 3.8 base image on a lightweight Debian Buster base.
WORKDIR /app: Set the working directory inside the container (similar to the mkdir command in Linux systems)
COPY requirements.txt requirements.txt: Copy the dependency list into the image.
RUN pip3 install -r requirements.txt: The RUN directive runs the commands in the image. In this case, it’s used to install dependencies.
COPY . .: Copy all project files into the container.
CMD [...]: CMD defines the commands and app parameters to be used when the container starts.
9. Use a .dockerignore File
Create a .dockerignore file to exclude unnecessary directories. It helps to decrease the image size.
In our case, we have two directories that we don’t need to launch the app. Add them to the .dockerignore file:
env
__pycache__
10. Build the Docker Image
When building the image, we need to use a tag that would work as an identifier for the image. We’ll use the flask-app:01 tag.
docker build -t flask-app:01 .
The dot at the end means the Dockerfile is located in the same directory where we run the command.
Check the created image:
docker images
11. Run the Docker Container
docker run -d -p 80:80 flask-app:01
-d: Run the container in the background.
-p: Forward host port 80 to container port 80.
Check running containers:
docker ps
The STATUS column should show “Up”.
Open your browser and navigate to your server's IP address to view the app.
Creating a Docker Image for a Node.js Application
Our simple Node.js app will display the message: “This app was created using Node.js!”
Make sure you have Node.js installed on your system.
1. Create the Project Directory
mkdir dockerfile-nodejs && cd dockerfile-nodejs
2. Initialize the Project
npm init --yes
3. Install Express
npm install express --save
4. Create the Application File
Create app.js with the following code:
const express = require("express");
const app = express();
app.get("/", function(req, res) {
return res.send("This app was created using Node.js!");
});
app.listen(3000, '0.0.0.0', function(){
console.log('Listening on port 3000');
});
5. Test the Application
node app.js
Open http://<your-server-ip>:3000 in a browser to verify it works.
6. Create the Dockerfile
FROM node:20
WORKDIR /app
COPY package.json /app
RUN npm install
COPY . /app
CMD ["node", "app.js"]
7. Add .dockerignore
Create .dockerignore and the following line:
**/node_modules/
8. Build the Image
docker build -t nodejs-app:01 .
9. Start the Container from Image
docker run -d -p 80:3000 nodejs-app:01
Visit http://<your-server-ip> in your browser. The app should be running.
Creating a Docker Image for a Go Application
This Go application will display: “Hello from GO!”
Make sure you have Go installed in your system.
1. Create the Project Directory
mkdir dockerfile-go && cd dockerfile-go
2. Initialize the Go Module
go mod init go-test-app
3. Create the Application File
Create main.go with this code of our application:
package main
import "fmt"
func main() {
fmt.Println("Hello from GO!")
}
Verify it works:
go run .
4. Create the Dockerfile
FROM golang:1.23-alpine
WORKDIR /app
COPY go.mod ./
RUN go mod download
COPY *.go ./
RUN go build -o /go-test
CMD [ "/go-test" ]
COPY go.mod ./: Adds dependencies file.
RUN go mod download: Installs dependencies.
COPY *.go ./: Adds source code.
RUN go build -o /go-test: Compiles the binary.
5. Build the Image
docker build -t go:01 .
6. Run the Container
docker run go:01
You should see the output: Hello from GO!
Conclusion
In this guide, we walked through building custom Docker images for three applications written in different programming languages. Docker allows you to package any application and deploy it with ease.
18 June 2025 · 7 min to read
Docker
How to Install Docker on MacOS
Docker is a platform that makes it easier to create, deploy, and operate applications in containers. Containers enable developers to bundle an application's dependencies, including as libraries, frameworks, and runtime environments, and ship it as a single package. This ensures that the program runs reliably and consistently, independent of the environment in which it is deployed. If you have troubles with that, here's our instruction how to deploy server with Docker.
Docker allows you to automate the deployment of software inside lightweight, portable containers. These containers may operate on any system with Docker installed, making it simple to deploy apps across several settings, such as a developer's laptop, a testing server, or a production environment on the cloud. Docker also includes tools for managing and orchestrating containers at scale, making it simpler to deploy, scale, and manage complex applications in production environments.
Below are the requirements to prepare for the installation of docker on MacOS:
A supported version of MacOS. Docker Desktop is compatible with the latest macOS versions. This includes the current macOS release as well as the two previous releases. As new major versions of macOS become widely available, Docker stops supporting the oldest version and instead supports the most recent version (along with the prior two).
RAM: minimum of 4 GB. This is to optimize Docker performance especially when operating multiple containers.
In installing docker, you can either install it interactively or manually or via the command line interface. Here’s the guide on how to do the installation with both methods.
Manual Installation
Download the installer from the official docker website using the following links:
Apple Silicon processor
Intel chip processor
Install Docker Desktop by double-clicking the Docker.dmg:
And drag and drop it to the Application folder.
By default, the Docker Desktop is installed at /Applications/Docker.
Wait for the copying to finish.
Double-click the Docker from the Applications folder to proceed with the installation.
Click Accept to continue in the Docker Subscription Service Agreement page.
From the installation window, choose either:
Use recommended settings (Requires password)
Use advanced settings
Click Finish.
Verify if installation is successful. A Docker icon should appear on the menu bar when the Docker Desktop is installed and running. A notification will appear stating that Docker is running.
Install using Command Line Interface
Once Docker.dmg is downloaded from the official docker website, login as a super user / root in a terminal to install Docker Desktop in the Application folder. Execute the below commands respectively.
hdiutil attach Docker.dmg
/Volumes/Docker/Docker.app/Contents/MacOS/install
hdiutil detach /Volumes/Docker
If running as a normal user, execute the command with sudo:
sudo hdiutil attach Docker.dmg
sudo /Volumes/Docker/Docker.app/Contents/MacOS/install
sudo hdiutil detach /Volumes/Docker
Installation might take some time to complete since the system may do various security checks while installing Docker on Mac.
Troubleshooting
Some of the common issues that the user might encounter during the installation of MacOS are:
Users may not check the MacOs version of their machine. Take note of the system requirements when installing Docker on MacOS to avoid installation failure and unexpected behavior (like docker image becomes corrupted).
Errors during the installation process may occur such as failed downloads, incomplete installation, etc. Go back and check the system and hardware compatibility of the machine.
Conflict with existing software. This requires troubleshooting and investigating system logs. Usually, this can be solved by removing the problematic software.
Permission and security issues. When installing Docker on MacOS, ensure that the machine has all the required permission to access system resources, like directory, network, etc.
Conclusion
To summarize, installing Docker on MacOS provides various opportunities for both developers and system administrators. Docker technology provides resources with an enhanced development workflow, an efficient procedure for delivering apps, and consistent system environments. Embrace containerization to broaden your development horizons.
Check what Hostman VPS Servers can give you.
30 April 2025 · 4 min to read
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