Effective infrastructure management is crucial for organizations using Docker Hub. Without a clear understanding of resource consumption, unexpected usage can emerge and skyrocket. This is particularly true if pulls and storage needs are not budgeted and forecasted correctly. By implementing proactive post controls and monitoring usage patterns, development teams can sustain their Docker Hub usage while keeping expenses under control.
To support these goals, we’ve introduced new Docker Hub Usage dashboards, offering organizations the ability to access and analyze their usage patterns for storage and pulls.
Docker Hub’s Usage dashboards put you in control, giving visibility into every pull and image your Docker systems request. Each pull and cache becomes a deliberate choice — not a random event — so you can make every byte count. With clear insights into what’s happening and why, you can design more efficient, optimized systems.
Reclaim control and manage technical resources by kicking bad habits
Figure 1: Docker Hub Usage dashboards.
The Docker Hub Usage dashboards (Figure 1) provide valuable insights, allowing teams to track peaks and valleys, detect high usage periods, and identify the images and repositories driving the most consumption. This visibility not only aids in managing usage but also strengthens continuous improvement efforts across your software supply chain, helping teams build applications more efficiently and sustainably.
This information helps development teams to stay on top of challenges, such as:
Redundant pulls and misconfigured repositories: Thesecan quickly and quietly drive up technical expenses while falling out of scope of the most relevant or critical use cases. Docker Hub’s Usage dashboards can help development teams identify patterns and optimize accordingly. They let you view usage trends across IPs and users as well, which helps with pinpointing high consumption areas and ensuring accountability in an organization when it comes to resource management.
Poor caching management: Repository insights and image tagging helps customers assess internal usage patterns, such as frequently accessed images, where there might be an opportunity to improve caching. With proper governance models, organizations can also establish policies and processes that reduce the variability of resource usage as a whole. This goal goes beyond keeping track of seasonality usage patterns to help you design more predictable usage patterns so you can budget accordingly.
Accidental automation: Accidental automated system activities can really hurt your usage. Let’s say you are using a CI/CD pipeline or automated scripts configured to pull images more often than they should. They may pull on every build instead of the actual version change, for example.
Usage dashboards can help you identify these inefficiencies by showing detailed pull data associated with automated tooling. This information can help your teams quickly identify and adjust misconfigured systems, fine-tune automations to only pull when needed, and ultimately focus on the most relevant use cases for your organization, avoiding accidental overuse of resources:
Figure 2: Details from the Usage dashboards.
Docker Hub’s Usage dashboards offer a comprehensive view of your usage data, including downloadable CSV reports that include metrics such as pull counts, repository names, IP addresses, and version checks (Figure 2). This granular approach allows your organization to gain valuable insights and trend data to help optimize your team’s workflows and inform policies.
Integrate robust operational principles into your development pipeline by leveraging these data-driven reports and maintain control over resource consumption and operational efficiency with Docker Hub.
Docker Desktop’s single sign-on (SSO) and sign-in enforcement (also called login enforcement) features work together to enhance security and ease of use. SSO allows users to log in with corporate credentials, whereas login enforcement ensures every user is authenticated, giving IT tighter control over compliance. In this post, we’ll define each of these features, explain their unique benefits, and show how using them together streamlines management and improves your Docker Desktop experience.
Before diving into the benefits of login alongside SSO, let’s clarify three related terms: login, single sign-on (SSO), and enforced login.
Login: Logging in connects users to Docker’s suite of tools, enabling access to personalized settings, team resources, and features like Docker Scout and Docker Build Cloud. By default, members of an organization can use Docker Desktop without signing in. Logging in can be done through SSO or by using Docker-specific credentials.
Single sign-on (SSO): SSO allows users to access Docker using their organization’s central authentication system, letting teams streamline access across multiple platforms with one set of credentials. SSO standardizes and secures login and supports automation around provisioning but does not automatically log in users unless enforced.
Enforced login: This policy, configured by administrators, ensures users are logged in by requiring login credentials before accessing Docker Desktop and associated tools. With enforced login, teams gain consistent access to Docker’s productivity and security features, minimizing gaps in visibility and control.
With these definitions in mind, here’s why being logged in matters, how SSO simplifies login, and how login enforcement ensures your team gets the full benefit of Docker’s powerful development tools.
Why logging in matters for admins and compliance teams
Enforcing sign-in with corporate credentials ensures that all users accessing Docker Desktop are verified and utilizing the benefits of your Docker Business subscription while adding a layer of security to safeguard your software supply chain. This policy strengthens your organization’s security posture and enables Docker to provide detailed usage insights, helping compliance teams track engagement and adoption.
Enforced login will support cloud-based control over settings, allowing admins to manage application configurations across the organization more effectively. By requiring login, your organization benefits from greater transparency, control, and alignment with compliance standards.
When everyone in your organization signs in with proper credentials:
Access controls for shared resources become more reliable, allowing administrators to enforce policies and permissions consistently.
Developers stay connected to their workspaces and resources, minimizing disruptions.
Desktop Insights Dashboard provides admins actionable insights into usage, from feature adoption to image usage trends and login activity, helping administrators optimize team performance and security.
Teams gain full visibility and access to Docker Scout’s security insights, which only function with logged-in accounts.
Docker provides three options to help administrators enforce sign-in.
Registry key method (Windows Only): Integrates seamlessly with Windows, letting IT enforce login policies within familiar registry settings, saving time on configuration.
Plist or config profiles method (Mac): Provides an easy way for IT to manage access on macOS, ensuring policy consistency across Apple devices without additional tools.
Registry.json method (all platforms): Works across Windows, macOS, and Linux, allowing IT to enforce login on all platforms with a single, flexible configuration file, streamlining policy management for diverse environments.
Each method helps IT secure access, restrict to authorized users, and maintain compliance across all systems. You can enforce login without setting up SSO. Read the documentation to learn more about Docker’s sign-in enforcement methods.
Single sign-on (SSO)
Docker Desktop’s SSO capabilities allow organizations to streamline access by integrating with corporate identity providers, ensuring that only authorized team members can access Docker resources using their work credentials. This integration enhances security by eliminating the need for separate Docker-specific passwords, reducing the risk of unauthorized access to critical development tools. With SSO, admins can enforce consistent login policies across teams, simplify user management, and gain greater control over who accesses Docker Desktop. Additionally, SSO enables compliance teams to track access and usage better, aligning with organizational security standards and improving overall security posture.
Docker Desktop supports SSO integrations with a variety of idPs, including Okta, OneLogin, Auth0, and Microsoft Entra ID. By integrating with these IdPs, organizations can streamline user authentication, enhance security, and maintain centralized access control across their Docker environments.
Differences between SSO enforcement and SSO enablement
SSO and SCIM give your company more control over how users log in and attach themselves to your organization and Docker subscription but do not require your users to sign in to your organization when using Docker Desktop. Without sign-in enforcement, users can continue to utilize Docker Desktop without logging in or using their personal Docker IDs or subscriptions, preventing Docker from providing you with insights into their usage and control over the application.
SSO enforcement usually applies to identity management across multiple applications, enforcing a single, centralized login for a suite of apps or services. However, a registry key or other local login enforcement mechanism typically applies only to that specific application (e.g., Docker Desktop) and doesn’t control access across different services.
Better together: Sign-in enforcement and SSO
While SSO enables seamless access to Docker for those who choose to log in, enforcing login ensures that users fully benefit from Docker’s productivity and security features.
Docker’s SSO integration is designed to simplify enterprise user management, allowing teams to access Docker with their organization’s centralized credentials. This streamlines onboarding and minimizes password management overhead, enhancing security across the board. However, SSO alone doesn’t require users to log in — it simply makes it more convenient and secure. Without enforced login, users might bypass the sign-in process, missing out on Docker’s full benefits, particularly in areas of security and control.
By coupling SSO with login enforcement, organizations strengthen their Registry Access Management (RAM), ensuring access is restricted to approved registries, boosting image compliance, and centralizing control. Encouraging login alongside SSO ensures teams enjoy a seamless experience while unlocking Docker’s complete suite of features.
Developers are increasingly expected to integrate AI capabilities into their applications but they also face many challenges. Namely, the steep learning curve, coupled with an overwhelming array of tools and frameworks, makes this process too tedious. Docker aims to bridge this gap with the Docker AI Catalog, a curated experience designed to simplify AI development and empower both developers and publishers.
Why Docker for AI?
Docker and container technology has been a key technology used by developers at the forefront of AI applications for the past few years. Now, Docker is doubling down on that effort with our AI Catalog. Developers using Docker’s suite of products are often responsible for building, deploying, and managing complex applications — and, now, they must also navigate generative AI (GenAI) technologies, such as large language models (LLMs), vector databases, and GPU support.
For developers, the AI Catalog simplifies the process of integrating AI into applications by providing trusted and ready-to-use content supported by comprehensive documentation. This approach removes the hassle of evaluating numerous tools and configurations, allowing developers to focus on building innovative AI applications.
Key benefits for development teams
The Docker AI Catalog is tailored to help users overcome common hurdles in the evolving AI application development landscape, such as:
Decision overload: The GenAI ecosystem is crowded with new tools and frameworks. The Docker AI Catalog simplifies the decision-making process by offering a curated list of trusted content and container images, so developers don’t have to wade through endless options.
Steep learning curve: With the rise of new technologies like LLMs and retrieval-augmented generation (RAG), the learning curve can be overwhelming. Docker provides an all-in-one resource to help developers quickly get up to speed.
Complex configurations preventing production readiness: Running AI applications often requires specialized hardware configurations, especially with GPUs. Docker’s AI stacks make this process more accessible, ensuring that developers can harness the full power of these resources without extensive setup.
The result? Shorter development cycles, improved productivity, and a more streamlined path to integrating AI into both new and existing applications.
Empowering publishers
For Docker verified publishers, the AI Catalog provides a platform to differentiate themselves in a crowded market. Independent software vendors (ISVs) and open source contributors can promote their content, gain insights into adoption, and improve visibility to a growing community of AI developers.
Key features for publishers include:
Increased discoverability: Publishers can highlight their AI content within a trusted ecosystem used by millions of developers worldwide.
Metrics and insights: Verified publishers gain valuable insights into the performance of their content, helping them optimize strategies and drive engagement.
Unified experience for AI application development
The AI Catalog is more than just a repository of AI tools. It’s a unified ecosystem designed to foster collaboration between developers and publishers, creating a path forward for more innovative approaches to building applications supported by AI capabilities. Developers get easy access to essential AI tools and content, while publishers gain the visibility and feedback they need to thrive in a competitive marketplace.
With Docker’s trusted platform, development teams can build AI applications confidently, knowing they have access to the most relevant and reliable tools available.
The road ahead: What’s next?
Docker will launch the AI Catalog in preview on November 12, 2024, alongside a joint webinar with MongoDB. This initiative will further Docker’s role as a leader in AI application development, ensuring that developers and publishers alike can take full advantage of the opportunities presented by AI tools.
Stay tuned for more updates and prepare to dive into a world of possibilities with the Docker AI Catalog. Whether you’re an AI developer seeking to streamline your workflows or a publisher looking to grow your audience, Docker has the tools and support you need to succeed.
Ready to simplify your AI development process? Explore the AI Catalog and get access to trusted content that will accelerate your development journey. Start building smarter, faster, and more efficiently.
For publishers, now is the perfect time to join the AI Catalog and gain visibility for your content. Become a trusted source in the AI development space and connect with millions of developers looking for the right tools to power their next breakthrough.
If you’ve ever been tangled in the complexities of setting up a WordPress environment, you’re not alone. WordPress powers more than 40% of all websites, making it the world’s most popular content management system (CMS). Its versatility is unmatched, but traditional local development setups like MAMP, WAMP, or XAMPP can lead to inconsistencies and the infamous “it works on my machine” problem.
As projects scale and teams grow, the need for a consistent, scalable, and efficient development environment becomes critical. That’s where Docker comes into play, revolutionizing how we develop and deploy WordPress sites. To make things even smoother, we’ll integrate Traefik, a modern reverse proxy that automatically obtains TLS certificates, ensuring that your site runs securely over HTTPS. Traefik is available as a Docker Official Image from Docker Hub.
In this comprehensive guide, I’ll show how to Dockerize your WordPress site using real-world examples. We’ll dive into creating Dockerfiles, containerizing existing WordPress instances — including migrating your data — and setting up Traefik for automatic TLS certificates. Whether you’re starting fresh or migrating an existing site, this tutorial has you covered.
Let’s dive in!
Why should you containerize your WordPress site?
Containerizing your WordPress site offers a multitude of benefits that can significantly enhance your development workflow and overall site performance.
Increased page load speed
Docker containers are lightweight and efficient. By packaging your application and its dependencies into containers, you reduce overhead and optimize resource usage. This can lead to faster page load times, improving user experience and SEO rankings.
Efficient collaboration and version control
With Docker, your entire environment is defined as code. This ensures that every team member works with the same setup, eliminating environment-related discrepancies. Version control systems like Git can track changes to your Dockerfiles and to wordpress-traefik-letsencrypt-compose.yml, making collaboration seamless.
Easy scalability
Scaling your WordPress site to handle increased traffic becomes straightforward with Docker and Traefik. You can spin up multiple Docker containers of your application, and Traefik will manage load balancing and routing, all while automatically handling TLS certificates.
Simplified environment setup
Setting up your development environment becomes as simple as running a few Docker commands. No more manual installations or configurations — everything your application needs is defined in your Docker configuration files.
Simplified updates and maintenance
Updating WordPress or its dependencies is a breeze. Update your Docker images, rebuild your containers, and you’re good to go. Traefik ensures that your routes and certificates are managed dynamically, reducing maintenance overhead.
Getting started with WordPress, Docker, and Traefik
Before we begin, let’s briefly discuss what Docker and Traefik are and how they’ll revolutionize your WordPress development workflow.
Docker is a cloud-native development platform that simplifies the entire software development lifecycle by enabling developers to build, share, test, and run applications in containers. It streamlines the developer experience while providing built-in security, collaboration tools, and scalable solutions to improve productivity across teams.
Traefik is a modern reverse proxy and load balancer designed for microservices. It integrates seamlessly with Docker and can automatically obtain and renew TLS certificates from Let’s Encrypt.
How long will this take?
Setting up this environment might take around 45-60 minutes, especially if you’re integrating Traefik for automatic TLS certificates and migrating an existing WordPress site.
Docker Desktop: If you don’t already have the latest version installed, download and install Docker Desktop.
A domain name: Required for Traefik to obtain TLS certificates from Let’s Encrypt.
Access to DNS settings: To point your domain to your server’s IP address.
Code editor: Your preferred code editor for editing configuration files.
Command-line interface (CLI): Access to a terminal or command prompt.
Existing WordPress data: If you’re containerizing an existing site, ensure you have backups of your WordPress files and MySQL database.
What’s the WordPress Docker Bitnami image?
To simplify the process, we’ll use the Bitnami WordPress image from Docker Hub, which comes pre-packaged with a secure, optimized environment for WordPress. This reduces configuration time and ensures your setup is up to date with the latest security patches.
Using the Bitnami WordPress image streamlines your setup process by:
Simplifying configuration: Bitnami images come with sensible defaults and configurations that work out of the box, reducing the time spent on setup.
Enhancing security: The images are regularly updated to include the latest security patches, minimizing vulnerabilities.
Ensuring consistency: With a standardized environment, you avoid the “it works on my machine” problem and ensure consistency across development, staging, and production.
Including additional tools: Bitnami often includes helpful tools and scripts for backups, restores, and other maintenance tasks.
By choosing the Bitnami WordPress image, you can leverage a tested and optimized environment, reducing the risk of configuration errors and allowing you to focus more on developing your website.
Key features of Bitnami WordPress Docker image:
Optimized for production: Configured with performance and security in mind.
Regular updates: Maintained to include the latest WordPress version and dependencies.
Ease of use: Designed to be easy to deploy and integrate with other services, such as databases and reverse proxies.
Comprehensive documentation: Offers guides and support to help you get started quickly.
This indicates that we’re using the Bitnami WordPress image, version 6.3.1. The Bitnami image aligns well with our goals for a secure, efficient, and easy-to-manage WordPress environment, especially when integrating with Traefik for automatic TLS certificates.
By leveraging the Bitnami WordPress Docker image, you’re choosing a robust and reliable foundation for your WordPress projects. This approach allows you to focus on building great websites without worrying about the underlying infrastructure.
How to Dockerize an existing WordPress site with Traefik
Let’s walk through dockerizing your WordPress site using practical examples, including your .env and wordpress-traefik-letsencrypt-compose.yml configurations. We’ll also cover how to incorporate your existing data into the Docker containers.
Step 1: Preparing your environment variables
First, create a .env file in the same directory as your wordpress-traefik-letsencrypt-compose.yml file. This file will store all your environment variables.
Example .env file:
# Traefik Variables
TRAEFIK_IMAGE_TAG=traefik:2.9
TRAEFIK_LOG_LEVEL=WARN
TRAEFIK_ACME_EMAIL=your-email@example.com
TRAEFIK_HOSTNAME=traefik.yourdomain.com
# Basic Authentication for Traefik Dashboard
# Username: traefikadmin
# Passwords must be encoded using BCrypt https://hostingcanada.org/htpasswd-generator/
TRAEFIK_BASIC_AUTH=traefikadmin:$$2y$$10$$EXAMPLEENCRYPTEDPASSWORD
# WordPress Variables
WORDPRESS_MARIADB_IMAGE_TAG=mariadb:11.4
WORDPRESS_IMAGE_TAG=bitnami/wordpress:6.6.2
WORDPRESS_DB_NAME=wordpressdb
WORDPRESS_DB_USER=wordpressdbuser
WORDPRESS_DB_PASSWORD=your-db-password
WORDPRESS_DB_ADMIN_PASSWORD=your-db-admin-password
WORDPRESS_TABLE_PREFIX=wpapp_
WORDPRESS_BLOG_NAME=Your Blog Name
WORDPRESS_ADMIN_NAME=AdminFirstName
WORDPRESS_ADMIN_LASTNAME=AdminLastName
WORDPRESS_ADMIN_USERNAME=admin
WORDPRESS_ADMIN_PASSWORD=your-admin-password
WORDPRESS_ADMIN_EMAIL=admin@yourdomain.com
WORDPRESS_HOSTNAME=wordpress.yourdomain.com
WORDPRESS_SMTP_ADDRESS=smtp.your-email-provider.com
WORDPRESS_SMTP_PORT=587
WORDPRESS_SMTP_USER_NAME=your-smtp-username
WORDPRESS_SMTP_PASSWORD=your-smtp-password
Notes:
Replace placeholder values (e.g., your-email@example.com, your-db-password) with your actual credentials.
Do not commit this file to version control if it contains sensitive information.
Use a password encryption tool to generate the encrypted password for TRAEFIK_BASIC_AUTH. For example, you can use the htpasswd generator.
Step 2: Creating the Docker Compose file
Create a wordpress-traefik-letsencrypt-compose.yml file that defines your services, networks, and volumes. This YAML file is crucial for configuring your WordPress installation through Docker.
Networks: We’re using external networks (wordpress-network and traefik-network). We’ll create these networks before deploying.
Volumes: Volumes are defined for data persistence.
Services: We’ve defined mariadb, wordpress, and traefik services with the necessary configurations.
Health checks: Ensure that services are healthy before dependent services start.
Labels: Configure Traefik routing, HTTPS settings, and enable the dashboard with basic authentication.
Step 3: Creating external networks
Before deploying your Docker Compose configuration, you need to create the external networks specified in your wordpress-traefik-letsencrypt-compose.yml.
Run the following commands to create the networks:
Deploy your WordPress site using Docker Compose with the following command (Figure 1):
docker compose -f wordpress-traefik-letsencrypt-compose.yml -p website up -d
Figure 1: Using Docker Compose to deploy your WordPress site.
Explanation:
-f wordpress-traefik-letsencrypt-compose.yml: Specifies the Docker Compose file to use.
-p website: Sets the project name to website.
up -d: Builds, (re)creates, and starts containers in detached mode.
Step 5: Verifying the deployment
Check that all services are running (Figure 2):
docker ps
Figure 2: Services running.
You should see the mariadb, wordpress, and traefik services up and running.
Step 6: Accessing your WordPress site and Traefik dashboard
WordPress site: Navigate to https://wordpress.yourdomain.com in your browser. Type in the username and password you set earlier in the .env file and click the Log In button. You should see your WordPress site running over HTTPS, with a valid TLS certificate automatically obtained by Traefik (Figure 3).
Figure 3: WordPress dashboard.
Important: To get cryptographic certificates, you need to set up A-type records in your external DNS zone that point to your server’s IP address where Traefik is installed. If you’ve just set up these records, wait a bit before starting the service installation because it can take anywhere from a few minutes to 48 hours — sometimes even longer — for these changes to fully spread across DNS servers.
Traefik dashboard: Access the Traefik dashboard at https://traefik.yourdomain.com. You’ll be prompted for authentication. Use the username and password specified in your .env file (Figure 4).
Figure 4: Traefik dashboard.
Step 7: Incorporating your existing WordPress data
If you’re migrating an existing WordPress site, you’ll need to incorporate your existing files and database into the Docker containers.
Step 7.1: Restoring WordPress files
Copy your existing WordPress files into the wordpress-data volume.
Option 1: Using Docker volume mapping
Modify your wordpress-traefik-letsencrypt-compose.yml to map your local WordPress files directly:
mysql -u root -p${WORDPRESS_DB_ADMIN_PASSWORD} ${WORDPRESS_DB_NAME} < wordpress_db_backup.sql
Step 7.3: Update wp-config.php (if necessary)
Because we’re using environment variables, WordPress should automatically connect to the database. However, if you have custom configurations, ensure they match the settings in your .env file.
Note: The Bitnami WordPress image manages wp-config.php automatically based on environment variables. If you need to customize it further, you can create a custom Dockerfile.
Step 8: Creating a custom Dockerfile (optional)
If you need to customize the WordPress image further, such as installing additional PHP extensions or modifying configuration files, create a Dockerfile in your project directory.
Example Dockerfile:
# Use the Bitnami WordPress image as the base
FROM bitnami/wordpress:6.3.1
# Install additional PHP extensions if needed
# RUN install_packages php7.4-zip php7.4-mbstring
# Copy custom wp-content (if not using volume mapping)
# COPY ./wp-content /bitnami/wordpress/wp-content
# Set working directory
WORKDIR /bitnami/wordpress
# Expose port 8080
EXPOSE 8080
Build the custom image:
Modify your wordpress-traefik-letsencrypt-compose.yml to build from the Dockerfile:
wordpress:
build: .
# Rest of the configuration
Then, rebuild your containers:
docker compose -p wordpress up -d --build
Step 9: Customizing WordPress within Docker
Adding themes and plugins
Because we’ve mapped the wordpress-data volume, any changes you make within the WordPress container (like installing plugins or themes) will persist across container restarts.
Via WordPress admin dashboard: Install themes and plugins as you normally would through the WordPress admin interface (Figure 5).
Figure 5: Adding plugins.
Manually: Access the container and place your themes or plugins directly.
Example:
docker exec -it wordpress_wordpress_1 bash
cd /bitnami/wordpress/wp-content/themes
# Add your theme files here
Managing and scaling WordPress with Docker and Traefik
Scaling your WordPress service
To handle increased traffic, you might want to scale your WordPress instances.
docker compose -p wordpress up -d --scale wordpress=3
Traefik will automatically detect the new instances and load balance traffic between them.
Note: Ensure that your WordPress setup supports scaling. You might need to externalize session storage or use a shared filesystem for media uploads.
Updating services
To update your services to the latest images:
Pull the latest images:
docker compose -p wordpress pull
Recreate containers:
docker compose -p wordpress up -d
Monitoring and logs
Docker logs: View logs for a specific service:
docker compose -p wordpress logs -f wordpress
Traefik dashboard: Use the Traefik dashboard to monitor routing, services, and health checks.
Optimizing your WordPress Docker setup
Implementing caching with Redis
To improve performance, you can add Redis for object caching.
Install a Redis caching plugin like Redis Object Cache.
Configure it to connect to the redis service.
Security best practices
Secure environment variables:
Use Docker secrets or environment variables to manage sensitive information securely.
Avoid committing sensitive data to version control.
Restrict access to Docker socket:
The Docker socket is mounted read-only (:ro) to minimize security risks.
Keep images updated:
Regularly update your Docker images to include security patches and improvements.
Advanced Traefik configurations
Middleware: Implement middleware for rate limiting, IP whitelisting, and other request transformations.
Monitoring: Integrate with monitoring tools like Prometheus and Grafana for advanced insights.
Wildcard certificates: Configure Traefik to use wildcard certificates if you have multiple subdomains.
Wrapping up
Dockerizing your WordPress site with Traefik simplifies your development and deployment processes, offering consistency, scalability, and efficiency. By leveraging practical examples and incorporating your existing data, we’ve created a tailored guide to help you set up a robust WordPress environment.
Whether you’re managing an existing site or starting a new project, this setup empowers you to focus on what you do best — developing great websites — while Docker and Traefik handle the heavy lifting.
So go ahead, give it a shot! Embracing these tools is a step toward modernizing your workflow and staying ahead in the ever-evolving tech landscape.
Learn more
To further enhance your skills and optimize your setup, check out these resources:
Before the beta release of organization access tokens, managing developer access to Docker resources was challenging, as it relied heavily on individual user accounts, leading to security risks and administrative inefficiencies.
Organization access tokens let you manage access at the organizational level, providing enhanced security. This feature allows teams to operate more securely and efficiently with centralized user management, reduced administrative overhead, and the flexibility to scale access as the organization grows. For businesses, this feature offers significant value by improving governance, enhancing security, and supporting scalable infrastructure from an administrative perspective.
Organizational access tokens empower organizations to maintain tighter control over their resources and security, making Docker Desktop even more valuable for enterprise users. This is one piece of the continuous updates we’re releasing to support administrators across large enterprise companies, ensuring they have the tools needed to manage complex environments with efficiency and confidence.
Docker Home (Beta)
Sign in to your Docker account to see the release of the new Docker Home page (Figure 1). The new Docker Home marks a milestone in Docker’s journey as a multi-product company, reinforcing Docker’s commitment to providing an expanding suite of solutions that help developers and businesses containerize applications with ease.
Unified experience: The home page provides a central hub for users to access Docker products, manage subscriptions, adjust settings, and find resources — all in one place. This approach simplifies navigation for developers and admins.
Admin access: Administrators can manage organizations, users, and onboarding processes through the new portal, with access to dashboards for monitoring Docker usage.
Future enhancements: Future updates will add personalized features for different roles, and business subscribers will gain access to tools like the Docker Support portal and organization-wide notifications.
Figure 1: New Docker home page.
Terminal experience in Docker Desktop
Our terminal feature in Docker Desktop is now generally available. While managing containerized applications, developers have often faced friction and inefficiencies when switching between the Docker Desktop CLI and GUI. This constant context switching disrupted workflows and reduced productivity.
The terminal enhancement integrates a terminal directly within the Docker Desktop GUI, enabling seamless transitions between CLI and GUI interactions within a single window. By incorporating a terminal shell into the Docker Desktop interface (Figure 2), we significantly reduce the friction associated with context switching for developers.
Figure 2: Terminal shell in Docker Desktop.
This functionality is designed to streamline workflows, accelerate delivery times, and enhance overall developer productivity.
Volumes Export is GA
With the 4.35 release, we’ve elevated volume backup capabilities in Docker Desktop, introducing an upgraded feature set (Figure 3). This enhancement directly integrates the previous Volumes Backup & Share extension directly into Docker Desktop, streamlining your backup processes.
Figure 3: Docker Desktop Volumes view showcasing new backup functionality.
Although this release marks a significant step forward, it’s just the beginning. We’re committed to expanding these capabilities, adding even more value in future updates. Check out the beta of Scheduled Backups as well as External Cloud Storage backups, which are also available.
Significantly improved performance experience on macOS (Beta)
Docker Desktop 4.35 also includes a beta release of Docker VMM, a container-optimized hypervisor for Apple Silicon Macs. Local developer workflows rely heavily on the performance of the hypervisor layer for everything from handling individual timer interrupts to accessing files and downloading images from the network.
Docker VMM allows us to optimize the Linux kernel and hypervisor layer together, massively improving the speed of many common developer tasks. For example, iterating over a large shared file system with find is now 2x faster than on Docker Desktop 4.34 with a cold cache and up to 25x faster — faster than running natively on the Mac — when the cache is warm. This is only the beginning. Thanks to Docker VMM, we have many exciting new performance improvements in the pipeline.
Enable Docker VMM via Settings > General > Virtual Machine options and try it for your developer workflows today (Figure 4).
Figure 4: Docker VMM.
Docker Desktop for Red Hat Enterprise Linux
Today we are excited to announce the general availability of Docker Desktop for Red Hat Enterprise Linux (RHEL). This feature marks a great milestone for both Docker and our growing community of developers.
By making Docker Desktop available on RHEL, we’re not only extending our reach — we’re meeting developers where they are. RHEL users can now access a seamless containerized development experience directly on the same OS that might power their production environments.
Docker Desktop for RHEL (Figure 5) offers the same intuitive interface, integrated tooling, and performance optimizations that you’ve come to expect on the other supported Linux distributions.
Figure 5: Docker Desktop for RHEL.
How to install Docker Desktop on Red Hat Enterprise Linux
Did you know that you can get Premium Customer Support for Docker Desktop with a Pro or Team subscription? With this GA release, we’re now ready to officially help support you if you’re thinking about using Docker Desktop. Check out our pricing page to learn more about what’s included in a Pro or Team subscription, and if it’s right for you.
Explore the latest updates
With this latest wave of updates, from the security enhancements of organization access tokens to the performance boost of Docker VMM for Apple Silicon Macs, we’re pushing Docker Desktop forward to meet the evolving needs of developers and organizations alike. Each new feature is designed to make development smoother, faster, and more secure — whether you’re managing large teams or optimizing your individual workflow.
We’re continuing to make improvements, with more tools and features on the way to help you build, manage, and scale your projects efficiently. Explore the latest updates and see how they can enhance your development experience
Docker Desktop is more than just a local application for containerized development — it’s your gateway to an integrated suite of cloud-native tools that streamline the entire development workflow. While Docker Desktop can be used without signing in, doing so unlocks the full potential of Docker’s powerful, interconnected ecosystem. By signing in, you gain access to advanced features and services across Docker Hub, Build Cloud, Scout, and Testcontainers Cloud, enabling deeper collaboration, enhanced security insights, and scalable cloud resources.
This blog post explores the full range of capabilities unlocked by signing in to Docker Desktop, connecting you to Docker’s integrated suite of cloud-native development tools. From enhanced security insights with Docker Scout to scalable build and testing resources through Docker Build Cloud and Testcontainers Cloud, signing in allows developers and administrators to fully leverage Docker’s unified platform.
Note that the following sections refer to specific Docker subscription plans. With Docker’s newly streamlined subscription plans — Docker Personal, Docker Pro, Docker Team, and Docker Business — developers and organizations can access a scalable suite of tools, from individual productivity boosters to enterprise-grade governance and security. Visit the Docker pricing page to learn more about how these plans support different team sizes and workflows.
Benefits for developers when logged in
Docker Personal
Access to private repositories: Unlock secure collaboration through private repositories on Docker Hub, ensuring that your sensitive code and dependencies are managed securely across teams and projects.
Increased pull rate: Boost your productivity with an increased pull rate from Docker Hub (40 pulls/hour per user), ensuring smoother, uninterrupted development workflows without waiting on rate limits. The rate limit without authentication is 10 pulls/hour per IP.
Docker Scout CLI: Leverage Docker Scout to proactively secure your software supply chain with continuous security insights from code to production. By signing in, you gain access to powerful CLI commands that help prevent vulnerabilities before they reach production.
Build Cloud and Testcontainers Cloud: Experience the full power of Docker Build Cloud and Testcontainers Cloud with free trials (7-day for Build Cloud, 30-day for Testcontainers Cloud). These trials give you access to scalable cloud infrastructure that speeds up image builds and enables more reliable integration testing.
Docker Pro/Team/Business
For users with a paid Docker subscription, additional features are unlocked.
Unlimited pull rate: No Hub rate limit will be enforced for users with a paid subscription plan.
Docker Scout base image recommendations: Docker Scout offers continuous recommendations for base image updates, empowering developers to secure their applications at the foundational level and fix vulnerabilities early in the development lifecycle.
Figure 1: Docker Scout showing recommendations.
Docker Debug: The docker debug CLI command can help you debug containers, while the images contain the minimum required to run your application.
FIgure 2: Docker debug CLI.
Docker Debug functionalities have also been integrated into the container view of the Docker Desktop UI.
Figure 3: Debug functionalities integrated into the container view of Docker Desktop.
Synchronized file shares: Host to Docker Desktop VM file sharing via bind mounts can be quite slow for large codebases. Speed up your development cycle with synchronized file shares, allowing you to sync large codebases into containers quickly and efficiently without performance bottlenecks—helping developers iterate faster on critical projects.
Figure 4: Synchronized file shares.
Additional free minutes for Docker Build Cloud: Docker Build Cloud helps developer teams speed up image builds by offloading the build process to the cloud. The following benefits are available for users depending on the subscription plan.
Docker Pro: 200 mins/month per org
Docker Team: 500 mins/month per org
Docker Business: 1500 mins/month per org
Additional free minutes for Testcontainers Cloud: Testcontainers Cloud simplifies the process for developers to run reliable integration tests using real dependencies defined in code, whether on their laptops or within their team’s CI pipeline. Depending on the subscription plan, the following benefits are available for users:
Docker Pro: 100 mins/month per org
Docker Team: 500 mins/month per org
Docker Business: 1,500 mins/month per org
Benefits for administrators when your users are logged in
Docker Business
Security and governance
The Docker Business plan offers enterprise-grade security and governance controls, which are only applicable if users are signed in. As of Docker Desktop 4.35.0, these features include:
Image Access Management and Registry Access Management: Enhance security governance by controlling exactly which images and registries your developers can access or push to, ensuring compliance with organizational security policies and minimizing risk exposure.
Settings management: Enforce organization-wide security policies by locking default settings across Docker Desktop instances, ensuring consistent configuration and compliance throughout your team’s development environment.
Enhanced Container Isolation: Protect developer environments from malicious containers with rootless containers and much more.
Tracking usage for licensing purposes can be challenging for administrators due to Docker Desktop not requiring authentication by default. By ensuring all users are signed in, administrators can use Docker Hub’s organization members list to manage licenses effectively.
This can be coupled with Docker Business’s Single Sign-On and SCIM capabilities to ease this process further.
Insights
Administrators and other stakeholders (such as engineering managers) must comprehensively understand Docker Desktop usage within their organization. With developers signed into Docker Desktop, admins gain actionable insights into usage, from feature adoption to image usage trends and login activity, helping administrators optimize team performance and security. A dashboard offering insights is now available to simplify monitoring. Contact your account rep to enable the dashboard.
Figure 5: Desktop Insights view when users log in to your organization.
Enforce sign-in for Docker Desktop
Docker Desktop includes a feature that allows administrators to require authentication at start-up. Admins can ensure that all developers sign in to access Docker Desktop, enabling full integration with Docker’s security and productivity features. Sign-in enforcement helps maintain continuous compliance with governance policies across the organization.
Figure 6: Prompting sign in.
Developers can then click on the sign-in button, which takes them through the authentication flow.
Unlock the full potential of Docker’s integrated suite
Signing into Docker Desktop unlocks significant benefits for both developers and administrators, enabling teams to fully leverage Docker’s integrated, cloud-native suite. Whether improving productivity, securing the software supply chain, or enforcing governance policies, signing in maximizes the value of Docker’s unified platform — especially for organizations using Docker’s paid subscription plans.
Note that new features are introduced with each new release, so keep an eye on our blog and subscribe to the Docker Newsletter for the latest product and feature updates.
This ongoing Docker Labs GenAI series explores the exciting space of AI developer tools. At Docker, we believe there is a vast scope to explore, openly and without the hype. We will share our explorations and collaborate with the developer community in real time. Although developers have adopted autocomplete tooling like GitHub Copilot and use chat, there is significant potential for AI tools to assist with more specific tasks and interfaces throughout the entire software lifecycle. Therefore, our exploration will be broad. We will be releasing software as open source so you can play, explore, and hack with us, too.
How do you turn a PDF into a set of images? A modern suggestion could be to get Claude or GPT to write you a CLI command for something like ImageMagick. Let’s follow that train of thought.
If we enter a prompt like “How do I turn a PDF into a bunch of images using ImageMagick,” we will indeed get something that looks like it might be a solution.
Imagemagick includes a command-line tool called convert which you can use to convert PDF files into images. Here's how you can do it:
1. Open your terminal
2. Change directory to where your PDF is located:
```
cd /path/to/your/pdf
```
3. Use the following command to convert the PDF into images (in this case JPEG):
```
convert -density 300 -quality 100 your_file.pdf output_name.jpg
```
The agent will then explain how convert works and finish with a suggestion to “please make sure that you have installed ImageMagick before running these commands.”
Instead of worrying about whether ImageMagick is installed, however, let’s use Docker as our tool provider and prompt it to go ahead and run the command. If you’re following along in this series, you’ll have seen that we are using Markdown files to mix together tools and prompts. Here’s the first prompt we tried:
---
tools:
- name: imagemagick
---
# prompt user
Use Imagemagick to convert the family.pdf file into a bunch of jpg images.
After executing this prompt, the LLM generated a tool call, which we executed in the Docker runtime, and it successfully converted family.pdf into nine .jpg files (my family.pdf file had nine pages).
We have given enough context to the LLM that it is able to plan a call to this ImageMagick binary. And, because this tool is available on Docker Hub, we don’t have to “make sure that ImageMagick is installed.” This would be the equivalent command if you were to use docker run directly:
# family.pdf must be located in your $PWD
docker run --rm -v $PWD:/project --workdir /project vonwig/imageMagick:latest convert -density 300 -quality 300 family.pdf family.jpg
The tool ecosystem
How did this work? The process relied on two things:
Tool distribution and discovery (pulling tools into Docker Hub for distribution to our Docker Desktop runtime).
Automatic generation of Agent Tool interfaces.
When we first started this project, we expected that we’d begin with a small set of tools because the interface for each tool would take time to design. We thought we were going to need to bootstrap an ecosystem of tools that had been prepared to be used in these agent workflows.
However, we learned that we can use a much more generic approach. Most tools already come with documentation, such as command-line help, examples, and man pages. Instead of treating each tool as something special, we are using an architecture where an agent responds to failures by reading documentation and trying again (Figure 2).
Figure 2: Agent process.
We see a process of experimenting with tools that is not unlike what we, as developers, do on the command line. Try a command line, read a doc, adjust the command line, and try again.
The value of this kind of looping has changed our expectations. Step one is simply pulling the tool into Docker Hub and seeing whether the agent can use it with nothing more than its out-of-the-box documentation. We are also pulling open source software (OSS) tools directly from nixpkgs, which gives us access to tens of thousands of different tools to experiment with.
Docker keeps our runtimes isolated from the host operating system, while the nixpkgs ecosystem and maintainers provide a rich source of OSS tools.
As expected, packaging agents still run into issues that force us to re-plan how tools are packaged. For example, the prompt we showed above might have generated the correct tool call on the first try, but the ImageMagick container failed on the first run with this terrible-looking error message:
function call failed call exited with non-zero code (1): Error: sh: 1: gs: not found
Fortunately, feeding that error back into the LLM resulted in the suggestion that convert needs another tool, called Ghostscript, to run successfully. Our agent was not able to fix this automatically today. However, we adjusted the image build slightly and now the “latest” version of the vonwig/imagemagick:latest no longer has this issue. This is an example of something we only need to learn once.
The LLM figured out convert on its own. But its agency came from the addition of a tool.
When testing complex systems, the more edge cases you can identify, the better your software performs in the real world. But how do you efficiently generate hundreds or thousands of meaningful tests that reveal hidden bugs? Enter model-based testing (MBT), a technique that automates test case generation by modeling your software’s expected behavior.
In this demo, we’ll explore the model-based testing technique to perform regression testing on a simple REST API.
We’ll use the jqwik test engine on JUnit 5 to run property and model-based tests. Additionally, we’ll use Testcontainers to spin up Docker containers with different versions of our application.
Model-based testing
Model-based testing is a method for testing stateful software by comparing the tested component with a model that represents the expected behavior of the system. Instead of manually writing test cases, we’ll use a testing tool that:
Takes a list of possible actions supported by the application
Automatically generates test sequences from these actions, targeting potential edge cases
Executes these tests on the software and the model, comparing the results
In our case, the actions are simply the endpoints exposed by the application’s API. For the demo’s code examples, we’ll use a basic service with a CRUD REST API that allows us to:
Find an employee by their unique employee number
Update an employee’s name
Get a list of all the employees from a department
Register a new employee
Figure 1: Finding an employee, updating their name, finding their department, and registering a new employee.
Once everything is configured and we finally run the test, we can expect to see a rapid sequence of hundreds of requests being sent to the two stateful services:
Figure 2: New requests being sent to the two stateful services.
Docker Compose
Let’s assume we need to switch the database from Postgres to MySQL and want to ensure the service’s behavior remains consistent. To test this, we can run both versions of the application, send identical requests to each, and compare the responses.
We can set up the environment using a Docker Compose that will run two versions of the app:
Model (mbt-demo:postgres): The current live version and our source of truth.
Tested version (mbt-demo:mysql): The new feature branch under test.
At this point, we could start the application and databases manually for testing, but this would be tedious. Instead, let’s use Testcontainers’ ComposeContainer to automate this with our Docker Compose file during the testing phase.
In this example, we’ll use jqwik as our JUnit 5 test runner. First, let’s add the jqwik and Testcontainers and the jqwik-testcontainers dependencies to our pom.xml:
Additionally, in the test class, we can declare another method that helps us create TestHttpClients for the two services started by the ComposeContainer:
static TestHttpClient testClient(String service) {
int port = ENV.getServicePort(service, 8080);
String url = "http://localhost:%s/api/employees".formatted(port);
return new TestHttpClient(service, url);
}
jqwik
Jqwik is a property-based testing framework for Java that integrates with JUnit 5, automatically generating test cases to validate properties of code across diverse inputs. By using generators to create varied and random test inputs, jqwik enhances test coverage and uncovers edge cases.
If you’re new to jqwik, you can explore their API in detail by reviewing the official user guide. While this tutorial won’t cover all the specifics of the API, it’s essential to know that jqwik allows us to define a set of actions we want to test.
To begin with, we’ll use jqwik’s @Property annotation — instead of the traditional @Test — to define a test:
Next, we’ll define the actions, which are the HTTP calls to our APIs and can also include assertions.
For instance, the GetOneEmployeeAction will try to fetch a specific employee from both services and compare the responses:
record ModelVsTested(TestHttpClient model, TestHttpClient tested) {}
record GetOneEmployeeAction(String empNo) implements Action<ModelVsTested> {
@Override
public ModelVsTested run(ModelVsTested apps) {
ApiResponse<EmployeeDto> actual = apps.tested.get(empNo);
ApiResponse<EmployeeDto> expected = apps.model.get(empNo);
assertThat(actual)
.satisfies(hasStatusCode(expected.statusCode()))
.satisfies(hasBody(expected.body()));
return apps;
}
}
Additionally, we’ll need to wrap these actions within Arbitrary objects. We can think of Arbitraries as objects implementing the factory design pattern that can generate a wide variety of instances of a type, based on a set of configured rules.
For instance, the Arbitrary returned by employeeNos() can generate employee numbers by choosing a random department from the configured list and concatenating a number between 0 and 200:
After declaring all the other Actions and Arbitraries, we’ll create an ActionSequence:
@Provide
Arbitrary<ActionSequence<ModelVsTested>> mbtJqwikActions() {
return Arbitraries.sequences(
Arbitraries.oneOf(
MbtJqwikActions.getOneEmployeeAction(),
MbtJqwikActions.getEmployeesByDepartmentAction(),
MbtJqwikActions.createEmployeeAction(),
MbtJqwikActions.updateEmployeeNameAction()
));
}
static Arbitrary<Action<ModelVsTested>> getOneEmployeeAction() { /* ... */ }
static Arbitrary<Action<ModelVsTested>> getEmployeesByDepartmentAction() { /* ... */ }
// same for the other actions
Now, we can write our test and leverage jqwik to use the provided actions to test various sequences. Let’s create the ModelVsTested tuple and use it to execute the sequence of actions against it:
That’s it — we can finally run the test! The test will generate a sequence of thousands of requests trying to find inconsistencies between the model and the tested service:
INFO com.etr.demo.utils.TestHttpClient -- [app-tested] PUT /api/employeesFrontend-129?name=v
INFO com.etr.demo.utils.TestHttpClient -- [app-model] PUT /api/employeesFrontend-129?name=v
INFO com.etr.demo.utils.TestHttpClient -- [app-tested] GET /api/employees/Frontend-129
INFO com.etr.demo.utils.TestHttpClient -- [app-model] GET /api/employees/Frontend-129
INFO com.etr.demo.utils.TestHttpClient -- [app-tested] POST /api/employees { name=sdxToS, empNo=Frontend-91 }
INFO com.etr.demo.utils.TestHttpClient -- [app-model] POST /api/employees { name=sdxToS, empNo=Frontend-91 }
INFO com.etr.demo.utils.TestHttpClient -- [app-tested] PUT /api/employeesFrontend-4?name=PZbmodNLNwX
INFO com.etr.demo.utils.TestHttpClient -- [app-model] PUT /api/employeesFrontend-4?name=PZbmodNLNwX
INFO com.etr.demo.utils.TestHttpClient -- [app-tested] GET /api/employees/Frontend-4
INFO com.etr.demo.utils.TestHttpClient -- [app-model] GET /api/employees/Frontend-4
INFO com.etr.demo.utils.TestHttpClient -- [app-tested] GET /api/employees?department=ٺ⯟桸
INFO com.etr.demo.utils.TestHttpClient -- [app-model] GET /api/employees?department=ٺ⯟桸
...
Catching errors
If we run the test and check the logs, we’ll quickly spot a failure. It appears that when searching for employees by department with the argument ٺ⯟桸 the model produces an internal server error, while the test version returns 200 OK:
Upon investigation, we find that the issue arises from a native SQL query using Postgres-specific syntax to retrieve data. While this was a simple issue in our small application, model-based testing can help uncover unexpected behavior that may only surface after a specific sequence of repetitive steps pushes the system into a particular state.
Wrap up
In this post, we provided hands-on examples of how model-based testing works in practice. From defining models to generating test cases, we’ve seen a powerful approach to improving test coverage and reducing manual effort. Now that you’ve seen the potential of model-based testing to enhance software quality, it’s time to dive deeper and tailor it to your own projects.
Clone the repository to experiment further, customize the models, and integrate this methodology into your testing strategy. Start building more resilient software today!
Cloud Expo Asia 2024 in Singapore drew thousands of cloud professionals and tech business leaders to explore and exchange the latest in cloud computing, security, GenAI, sustainability, DevOps, and more. At our Cloud Expo Asia booth, Docker showcased our latest innovations in AI integration, containerization, security best practices, and updated product offerings. Here are a few highlights from our experience at the event.
AI/ML and GenAI everywhere
AI/ML and GenAI were hot topics at Cloud Expo Asia. Docker CPO Giri Sreenivas’s talk on Transforming App Development: Docker’s Advanced Containerization and AI Integration highlighted that GenAI impacts software in two big ways — it accelerates product development and creates new types of products and experiences. He discussed how containers are an ideal tool for containerizing GenAI workflows in development, ensuring consistency across CI/CD pipelines and reproducibility across diverse platforms in production.
Sreenivas highlighted the Docker extension for GitHub Copilot as an example of how Docker helps empower development teams to focus on innovation — closing the gap from the first line of code to production. Sreenivas also gave a sneak peek into upcoming products designed to streamline GenAI development to illustrate Docker’s commitment to evolving solutions to meet emerging needs.
Adopting security best practices and shifting left
Developer efficiency and security were also popular themes at the event. When Sreenivas mentioned in his talk that security vulnerabilities that cost dollars to fix early in development would cost hundreds of dollars later in production, members of the audience nodded in agreement.
Docker CTO Justin Cormack gave a keynote address titled “The Docker Effect: Driving Developer Efficiency and Innovation in a Hybrid World.” He discussed how implementing best practices and investing in the inner loop are crucial for today’s development teams.
One best practice, for example, is shifting left and identifying problems as quickly as possible in the software development lifecycle. This approach improves efficiency and reduces costs by detecting and addressing software issues earlier before they become expensive problems.
At Docker CTO Justin Cormack’s talk, attendees were eager to snap pictures of every slide.
Cormack also provided a few tips for meeting the security and control needs of modern enterprises with a layered approach. Start with key building blocks, he explained, such as trusted content, which provides dev teams with a good foundation to build securely from the start.
Docker CTO Justin Cormack’s recommendations on meeting the security and control needs of modern enterprises.
At the Docker event booth, we demonstrated Docker Scout, which helps development teams identify, analyze, and remediate security vulnerabilities early in the dev process. Docker Business customers can take advantage of enterprise controls, letting admins, IT teams, and security teams continuously monitor and manage risk and compliance with confidence.
After four hours of demos at the Docker booth, senior software engineer Chase Frankenfeld was still enthusiastically discussing Docker products, while our CEO Scott Johnston listened attentively to an attendee’s questions.
New Docker innovations and updated plan
From students to C-level executives who visited our booth, everyone was eager to learn more about containers and Docker. People lined up to see an end-to-end demo of how the suite of Docker products, such as Docker Desktop, Docker Hub, Docker Build Cloud, and Docker Scout, work together seamlessly to enable development teams to work more efficiently.
Attendees also had the opportunity to learn more about Docker’s updated plans, which makes accessing the full suite of Docker products and solutions easy, with options for individual developers, small teams, and large enterprises.
Senior software engineer Maxime Clement explains Docker’s updated plans and demos Docker products to booth visitors.
Thanks, Cloud Expo Asia!
We enjoyed our conversations with event attendees and appreciate everyone who helped make this such a successful event. Thank you to the organizers, speakers, sponsors, and the community for a productive, information-packed experience.
What’s better than Docker swag? Docker swag in a claw machine.
From accelerating app development, supporting best practices of shifting left, meeting the security and control needs of modern enterprises, and innovating with GenAI, Docker wants to be your trusted partner to navigate the challenges in modern app development.
Explore our Docker updated plans to learn how Docker can empower your teams, or contact our sales team to discover how we can help you innovate with confidence.
This ongoing Docker Labs GenAI series explores the exciting space of AI developer tools. At Docker, we believe there is a vast scope to explore, openly and without the hype. We will share our explorations and collaborate with the developer community in real-time. Although developers have adopted autocomplete tooling like GitHub Copilot and use chat, there is significant potential for AI tools to assist with more specific tasks and interfaces throughout the entire software lifecycle. Therefore, our exploration will be broad. We will be releasing software as open source so you can play, explore, and hack with us, too.
At Docker Labs, we’ve been exploring how LLMs can connect different parts of the developer workflow, bridging gaps between tools and processes. A key insight is that LLMs excel at fixing code issues when they have the right context. To provide this context, we’ve developed a process that maps out the codebase using linting violations and the structure of top-level code blocks.
By combining these elements, we teach the LLM to construct a comprehensive view of the code, enabling it to fix issues more effectively. By leveraging containerization, integrating these tools becomes much simpler.
Previously, my linting process felt a bit disjointed. I’d introduce an error, run Pylint, and receive a message that was sometimes cryptic, forcing me to consult Pylint’s manual to understand the issue. When OpenAI released ChatGPT, the process improved slightly. I could run Pylint, and if I didn’t grasp an error message, I’d copy the code and the violation into GPT to get a better explanation. Sometimes, I’d ask it to fix the code and then manually paste the solution back into my editor.
However, this approach still required several manual steps: copying code, switching between applications, and integrating fixes. How might we improve this process?
Docker’s AI Tools for Devs prompt runner is an architecture that allows us to integrate tools like Pylint directly into the LLM’s workflow through containerization. By containerizing Pylint and creating prompts that the LLM can use to interact with it, we’ve developed a system where the LLM can access the necessary tools and context to help fix code issues effectively.
Understanding the cognitive architecture
For the LLM to assist effectively, it needs a structured way of accessing and processing information. In our setup, the LLM uses the Docker prompt runner to interact with containerized tools and the codebase. The project context is extracted using tools such as Pylint and Tree-sitter that run against the project. This context is then stored and managed, allowing the LLM to access it when needed.
By having access to the codebase, linting tools, and the context of previous prompts, the LLM can understand where problems are, what they are, and have the right code fragments to fix them. This setup replaces the manual process of finding issues and feeding them to the LLM with something automatic and more engaging.
Streamlining the workflow
Now, within my workflow, I can ask the assistant about code quality and violations directly. The assistant, powered by an LLM, has immediate access to a containerized Pylint tool and a database of my code through the Docker prompt runner. This integration allows the LLM to use tools to assist me directly during development, making the programming experience more efficient.
This approach helps us rethink how we interact with our tools. By enabling a conversational interface with tools that map code to issues, we’re exploring possibilities for a more intuitive development experience. Instead of manually finding problems and feeding them to an AI, we can convert our relationship with tools themselves to be conversational partners that can automatically detect issues, understand the context, and provide solutions.
Walking through the prompts
Our project is structured around a series of prompts that guide the LLM through the tasks it needs to perform. These prompts are stored in a Git repository and can be versioned, tracked, and shared. They form the backbone of the project, allowing the LLM to interact with tools and the codebase effectively. We automate this entire process using Docker and a series of prompts stored in a Git repository. Each prompt corresponds to a specific task in the workflow, and Docker containers ensure a consistent environment for running tools and scripts.
Workflow steps
An immediate and existential challenge we encountered was that this class of problem has a lot of opportunities to overwhelm the context of the LLM. Want to read a source code file? It has to be small enough to read. Need to work on more than one file? Your realistic limit is three to four files at once. To solve this, we can instruct the LLM to automate its own workflow with tools, where each step runs in a Docker container.
Again, each step in this workflow runs in a Docker container, which ensures a consistent and isolated environment for running tools and scripts. The first four steps prepare the agent to be able to extract the right context for fixing violations. Once the agent has the necessary context, the LLM can effectively fix the code issues in step 5.
1. Generate violations report using Pylint:
Run Pylint to produce a violation report.
2. Create a SQLite database:
Set up the database schema to store violation data and code snippets.
3. Generate and run INSERT statements:
Decouple violations from the range they represent.
Use a script to convert every violation and range from the report into SQL insert statements.
Run the statements against the database to populate it with the necessary data.
4. Index code in the database:
Generate an abstract syntax tree (AST) of the project with Tree-sitter (Figure 1).
Figure 1: Generating an abstract syntax tree.
Find all second-level nodes (Figure 2). In Python’s grammar, second-level nodes are statements inside of a module.
Figure 2: Extracting content for the database.
Index these top-level ranges into the database.
Populate a new table to store the source code at these top-level ranges.
5. Fix violations based on context:
Once the agent has gathered and indexed the necessary context, use prompts to instruct the LLM to query the database and fix the code issues (Figure 3).
Figure 3: Instructions for fixing violations.
Each step from 1 to 4 builds the foundation for step 5, where the LLM, with the proper context, can effectively fix violations. The structured preparation ensures that the LLM has all the information it needs to address code issues with precision.
Refining the context for LLM fixes
To understand how our system improves code fixes, let’s consider a specific violation flagged by Pylint. Say we receive a message that there’s a violation on line 60 of our code file block_listed_name.py:
From this Pylint violation, we know that the variable foo is a disallowed name. However, if we tried to ask the LLM to fix this issue based solely on this snippet of information, the response wouldn’t be as effective. Why? The LLM lacks the surrounding context — the full picture of the function in which this violation occurs.
This is where indexing the codebase becomes essential
Because we’ve mapped out the codebase, we can now ask the LLM to query the index and retrieve the surrounding code that includes the do_front function. The LLM can even generate the SQL query for us, thanks to its knowledge of the database schema. Once we’ve retrieved the full function definition, the LLM can work with a more complete view of the problem:
def do_front(front_filename, back_filename):
"""
Front strategy: loop over front image,
detect blue pixels there,
substitute in pixels from back.
Return changed front image.
"""
foo = SimpleImage(front_filename)
back = SimpleImage(back_filename)
for y in range(foo.height):xc
for x in range(foo.width):
pixel = foo.get_pixel(x, y)
# Detect blue pixels in front and replace with back pixels
if pixel[2] > 2 * max(pixel[0], pixel[1]):
back_pixel = back.get_pixel(x, y)
foo.set_pixel(x, y, back_pixel)
return foo
Now that the LLM can see the whole function, it’s able to propose a more meaningful fix. Here’s what it suggests after querying the indexed codebase and running the fix:
def do_front(front_filename, back_filename):
"""
Front strategy: loop over front image,
detect blue pixels there,
substitute in pixels from back.
Return changed front image.
"""
front_image = SimpleImage(front)
back_image = SimpleImage(back_filename)
for y in range(front_image.height):
for x in range(front_image.width pixel = front_image.get_pixel(x y)
# Detect blue pixels in front and replace with back pixels
if pixel[2 > * max(pixel[0 pixel[1]):
back_pixel = back_image.get_pixel(x,)
front_image.set_pixel(x,, back_pixel)
return front_image
Here, the variable foo has been replaced with the more descriptive front_image, making the code more readable and understandable. The key step was providing the LLM with the correct level of detail — the top-level range — instead of just a single line or violation message. With the right context, the LLM’s ability to fix code becomes much more effective, which ultimately streamlines the development process.
Remember, all of this information is retrieved and indexed by the LLM itself through the prompts we’ve set up. Through this series of prompts, we’ve reached a point where the assistant has a comprehensive understanding of the codebase.
At this stage, not only can I ask for a fix, but I can even ask questions like “what’s the violation at line 60 in naming_conventions/block_listed_name.py?” and the assistant responds with:
On line 60 of naming_conventions/block_listed_name.py, there's a violation: Disallowed name 'foo'. The variable name 'foo' is discouraged because it doesn't convey meaningful information about its purpose.
Although Pylint has been our focus here, this approach points to a new conversational way to interact with many tools that map code to issues. By integrating LLMs with containerized tools through architectures like the Docker prompt runner, we can enhance various aspects of the development workflow.
We’ve learned that combining tool integration, cognitive preparation of the LLM, and a seamless workflow can significantly improve the development experience. This integration allows an LLM to use tools to directly help while developing, and while Pylint has been the focus here, this also points to a new conversational way to interact with many tools that map code to issues.
To follow along with this effort, check out the GitHub repository for this project.
We are thrilled to announce that Granite models, IBM’s family of open source and proprietary models built for business, as well as Red Hat InstructLab model alignment tools, are now available on Docker Hub.
Now, developer teams can easily access, deploy, and scale applications using IBM’s AI models specifically designed for developers.
This news will be officially announced during the AI track of the keynote at IBM TechXchange on October 22. Attendees will get an exclusive look at how IBM’s Granite models on Docker Hub accelerate AI-driven application development across multiple programming languages.
Why Granite on Docker Hub?
With a principled approach to data transparency, model alignment, and security, IBM’s open source Granite models represent a significant leap forward in natural language processing. The models are available under an Apache 2.0 license, empowering developer teams to bring generative AI into mission-critical applications and workflows.
Granite models deliver superior performance in coding and targeted language tasks at lower latencies, all while requiring a fraction of the compute resources and reducing the cost of inference. This efficiency allows developers to experiment, build, and scale generative AI applications both on-premises and in the cloud, all within departmental budgetary limits.
Here’s what this means for you:
Simplified deployment: Pull the Granite image from Docker Hub and get up and running in minutes.
Scalability: Docker offers a lightweight and efficient method for scaling artificial intelligence and machine learning (AI/ML) applications. It allows you to run multiple containers on a single machine or distribute them across different machines in a cluster, enabling horizontal scalability.
Flexibility: Customize and extend the model to suit your specific needs without worrying about underlying infrastructure.
Portability: By creating Docker images once and deploying them anywhere, you eliminate compatibility problems and reduce the need for configurations.
Community support: Leverage the vast Docker and IBM communities for support, extensions, and collaborations.
In addition to the IBM Granite models, Red Hat also made the InstructLab model alignment tools available on Docker Hub. Developers using InstructLab can adapt pre-trained LLMs using far less real-world data and computing resources than alternative methodologies. InstructLab is model-agnostic and can be used to fine-tune any LLM of your choice by providing additional skills and knowledge.
With IBM Granite AI models and InstructLab available on Docker Hub, Docker and IBM enable easy integration into existing environments and workflows.
Getting started with Granite
You can find the following images available on Docker Hub:
InstructLab: Ideal for desktop or Mac users looking to explore InstructLab, this image provides a simple introduction to the platform without requiring specialized hardware. It’s perfect for prototyping and testing before scaling up.
Granite-7b-lab: This image is optimized for model serving and inference on desktop or Mac environments, using the Granite-7B model. It allows for efficient and scalable inference tasks without needing a GPU, perfect for smaller-scale deployments or local testing.
How to pull and run IBM Granite images from Docker Hub
IBM Granite provides a toolset for building and managing cloud-native applications. Follow these steps to pull and run an IBM Granite image using Docker and the CLI. You can follow similar steps for the Red Hat InstructLab images.
Authenticate to Docker Hub
Enter your Docker username and password when prompted.
Pull the IBM Granite Image
Pull the IBM Granite image from Docker Hub.
redhat/granite-7b-lab-gguf: For Mac/desktop users with no GPU support
Run the Image in a Container
Start a container with the IBM Granite image. The container can be started in two modes: CLI (default) and server.
To start the container in CLI mode, run the following: docker run --ipc=host -it redhat/granite-7b-lab-gguf
This command opens an interactive bash session within the container, allowing you to use the tools.
To run the container in server mode, run the following command:
docker run --ipc=host -it redhat/granite-7b-lab-gguf -s
Granite on Docker Hub will be officially announced at the IBM TechXchange Conference, which will be held October 21-24 in Las Vegas. Our head of technical alliances, Eli Aleyner, will show a live demonstration at the AI track of the keynote during IBM TechXchange. Oleg Šelajev, Docker’s staff developer evangelist, will show how app developers can test their GenAI apps with local models. Additionally, you’ll learn how Docker’s collaboration with Red Hat is improving developer productivity.
The availability of Granite on Docker Hub marks a significant milestone in making advanced AI models accessible to all. We’re excited to see how developer teams will harness the power of Granite to innovate and solve complex challenges.
Stay anchored for more updates, and as always, happy coding!
We’re excited to announce the launch of the Docker Terraform Provider, designed to help users and organizations automate and securely manage their Docker-hosted resources. This includes repositories, teams, organization settings, and more, all using Terraform’s infrastructure-as-code approach. This provider brings a unified, scalable, and secure solution for managing Docker resources in an automated fashion — whether you’re managing a single repository or a large-scale organization.
A new way of working with Docker Hub
The Docker Terraform Provider introduces a new way of working with Docker Hub, enabling infrastructure-as-code best practices that are already widely adopted across cloud-native environments. By integrating Docker Hub with Terraform, organizations can streamline resource management, improve security, and collaborate more effectively, all while ensuring Docker resources remain in sync with other infrastructure components.
The Problem
Managing Docker Hub resources manually can become cumbersome and prone to errors, especially as teams grow and projects scale. Maintaining configurations can lead to inconsistencies, reduced security, and a lack of collaboration between teams without a streamlined, version-controlled system. The Docker Terraform Provider solves this by allowing you to manage Docker Hub resources in the same way you manage your other cloud resources, ensuring consistency, auditability, and automation across the board.
The solution
The Docker Terraform Provider offers:
Unified management: With this provider, you can manage Docker repositories, teams, users, and organizations in a consistent workflow, using the same code and structure across environments.
Version control: Changes to Docker Hub resources are captured in your Terraform configuration, providing a version-controlled, auditable way to manage your Docker infrastructure.
Collaboration and automation: Teams can now collaborate seamlessly, automating the provisioning and management of Docker Hub resources with Terraform, enhancing productivity and ensuring best practices are followed.
Scalability: Whether you’re managing a few repositories or an entire organization, this provider scales effortlessly to meet your needs.
Example
At Docker, even we faced challenges managing our Docker Hub resources, especially when adding repositories without owner permissions — it was a frustrating, manual process. With the Terraform provider, anyone in the company can create a new repository without having elevated Docker Hub permissions. All levels of employees are now empowered to write code rather than track down coworkers. This streamlines developer workflows with familiar tooling and reduces employee permissions. Security and developers are happy!
Here’s an example where we are managing a repository, an org team, the permissions for the created repo, and a PAT token:
We’re just getting started with the Docker Terraform Provider, and there’s much more to come. Future work will expand support to other products in Docker’s suite, including Docker Scout, Docker Build Cloud, and Testcontainers Cloud. Stay tuned as we continue to evolve and enhance the provider with new features and integrations.
We’re confident this new provider will enhance how teams work with Docker Hub, making it easier to manage, secure, and scale their infrastructure while focusing on what matters most — building great software.
If you’ve worked with Docker for any length of time, you’re likely accustomed to writing or at least modifying a Dockerfile. This file can be thought of as a recipe for a Docker image; it contains both the ingredients (base images, packages, files) and the instructions (various RUN, COPY, and other commands that help build the image).
In most cases, Dockerfiles are written once, modified seldom, and used as-is unless something about the project changes. Because these files are created or modified on such an infrequent basis, developers tend to rely on only a handful of frequently used instructions — RUN, COPY, and EXPOSE being the most common. Other instructions can enhance your image, making it more configurable, manageable, and easier to maintain.
In this post, we will discuss the ARG and ENV instructions and explore why, how, and when to use them.
ARG: Defining build-time variables
The ARG instruction allows you to define variables that will be accessible during the build stage but not available after the image is built. For example, we will use this Dockerfile to build an image where we make the variable specified by the ARG instruction available during the build process.
FROM ubuntu:latest
ARG THEARG="foo"
RUN echo $THEARG
CMD ["env"]
If we run the build, we will see the echo foo line in the output:
However, we can pass a value for THEARG on the build command line using the --build-arg argument. Notice that we now see THEARG has been replaced with foo in the output:
=> CACHED [1/2] FROM docker.io/library/ubuntu:latest@sha256:8a37d68f4f73ebf3d4efafbcf66379bf3728902a8038616808f04e 0.0s
=> => resolve docker.io/library/ubuntu:latest@sha256:8a37d68f4f73ebf3d4efafbcf66379bf3728902a8038616808f04e34a9ab6 0.0s
=> [2/2] RUN echo foo 0.1s
=> exporting to image 0.0s
=> => exporting layers 0.0s
<-- SNIP -->
The same can be done in a Docker Compose file by using the args key under the build key. Note that these can be set as a mapping (THEARG: foo) or a list (- THEARG=foo):
If we run docker compose up --build, we can see the THEARG has been replaced with foo in the output:
$ docker compose up --build
<-- SNIP -->
=> [argtest 1/2] FROM docker.io/library/ubuntu:latest@sha256:8a37d68f4f73ebf3d4efafbcf66379bf3728902a8038616808f04 0.0s
=> => resolve docker.io/library/ubuntu:latest@sha256:8a37d68f4f73ebf3d4efafbcf66379bf3728902a8038616808f04e34a9ab6 0.0s
=> CACHED [argtest 2/2] RUN echo foo 0.0s
=> [argtest] exporting to image 0.0s
=> => exporting layers 0.0s
<-- SNIP -->
Attaching to argtest-1
argtest-1 | PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
argtest-1 | HOSTNAME=d9a3789ac47a
argtest-1 | HOME=/root
argtest-1 exited with code 0
Overriding ENV
You can also override ENV at build time; this is slightly different from how ARG is overridden. For example, you cannot supply a key without a value with the ENV instruction, as shown in the following example Dockerfile:
FROM ubuntu:latest
ENV THEENV
RUN echo $THEENV
CMD ["env"]
When we try to build the image, we receive an error:
$ docker build -t envtest .
[+] Building 0.0s (1/1) FINISHED docker:desktop-linux
=> [internal] load build definition from Dockerfile 0.0s
=> => transferring dockerfile: 98B 0.0s
Dockerfile:3
--------------------
1 | FROM ubuntu:latest
2 |
3 | >>> ENV THEENV
4 | RUN echo $THEENV
5 |
--------------------
ERROR: failed to solve: ENV must have two arguments
However, we can remove the ENV instruction from the Dockerfile:
If you need to access a variable during the build process but not at runtime, use ARG. If you need to access the variable both during the build and at runtime, or only at runtime, use ENV.
To decide between them, consider the following flow (Figure 1):
Both ARG and ENV can be overridden from the command line in docker run and docker compose, giving you a powerful way to dynamically update variables and build flexible workflows.
At Docker, innovation and efficiency are integral to how we operate. When our own IT team needed to deploy Docker Desktop to various teams, including non-engineering roles like customer support and technical sales, the existing process was functional but manual and time-consuming. Recognizing the need for a more streamlined and secure approach, we leveraged new Early Access (EA) Docker Business features to refine our deployment strategy.
A seamless deployment process
Faced with the challenge of managing diverse requirements across the organization, we knew it was time to enhance our deployment methods.
The Docker IT team transitioned from using registry.json files to a more efficient method involving registry keys and new MSI installers for Windows, along with configuration profiles and PKG installers for macOS. This transition simplified deployment, provided better control for admins, and allowed for faster rollouts across the organization.
“From setup to deployment, it took 24 hours. We started on a Monday morning, and by the next day, it was done,” explains Jeffrey Strauss, Head of Docker IT.
Enhancing security and visibility
Security is always a priority. By integrating login enforcement with single sign-on (SSO) and System for Cross-domain Identity Management (SCIM), Docker IT ensured centralized control and compliance with security policies. The Docker Desktop Insights Dashboard (EA) offered crucial visibility into how Docker Desktop was being used across the organization. Admins could now see which versions were installed and monitor container usage, enabling informed decisions about updates, resource allocation, and compliance. (Docker Business customers can learn more about access and timelines by contacting their account reps. The Insights Dashboard is only available to Docker Business customers with enforced authentication for organization users.)
Steven Novick, Docker’s Principal Product Manager, emphasized, “With the new solution, deployment was simpler and tamper-proof, giving a clear picture of Docker usage within the organization.”
Benefits beyond deployment
The improvements made by Docker IT extended beyond just deployment efficiency:
Improved visibility: The Insights Dashboard provided detailed data on Docker usage, helping ensure all users are connected to the organization.
Efficient deployment: Docker Desktop was deployed to hundreds of computers within 24 hours, significantly reducing administrative overhead.
Enhanced security: Centralized control to enforce authentication via MDM tools like Intune for Windows and Jamf for macOS strengthened security and compliance.
Seamless user experience: Early and transparent communication ensured a smooth transition, minimizing disruptions.
Looking ahead
The successful deployment of Docker Desktop within 24 hours demonstrates Docker’s commitment to continuous improvement and innovation. We are excited about the future developments in Docker Desktop management and look forward to supporting our customers as they achieve their goals with Docker.
Existing Docker Business customers can learn more about access and timelines by contacting their account reps. The Insights Dashboard is only available in Early Access to select Docker Business customers with enforced authentication for organization users.
In the past, securely managing access to organization resources has been difficult. The only way to gain access has been through an assigned user’s personal access tokens. Whether these users are your engineer’s accounts, bot accounts, or service accounts, they often become points of risk for your organization.
Organization access tokens are like personal access tokens, but at an organizational level with many improvements and features. In this post, we walk through a few reasons why this feature release is so exciting.
Frictionless management
Every day, we are reducing the friction for organizations and engineers using our products. We want you working on your projects, not managing your development tools.
Organization access tokens do not require you to manage groups and repository assignments like users require. This means you benefit from a straightforward way to manage access that each access token has instead of managing users and their placement within the organization.
If your organization has SSO enabled and enforced, you have likely run into the issue where machine or service accounts cannot log in easily because they don’t have the ability to log into your identity provider. With organization access tokens, this is no longer a problem.
Did someone leave your organization? No problem! With organization access tokens, you are still in control of the token instead of having to track down which tokens were on that user’s account and deal with the resulting challenges.
Fine-grained access
Organization access tokens introduce a new way to allow for tokens to access resources within your organization. These tokens can be assigned to specific repositories with specific actions for full access management with “least privilege” applied. Of course, you can also allow access to all resources in your organization.
Expirations
Another critical feature is the ability to set expirations for your organization access tokens. This is great for customers who have compliance requirements for token rotation or for those who just like the extra security.
Visibility
Management and registry actions all show up in your organization’s activity logs for each access token. Each token’s usage also shows up on your organization’s usage reports.
Business use cases and fair use
We believe that organization access tokens are useful in the context of teams and companies, which is why we are making them available to Docker Team and Docker Business subscribers. With the usual attention to the security aspect, avoiding any “misuse” related to the proliferation of the number of access tokens created, we are introducing a limitation in the maximum number of organization access tokens based on the type of subscription. There will be a limit of 10 for Team plans and 100 for Business plans.
Try organization access tokens
If you are on a team or business subscription, check out our documentation to learn more about using organization access tokens.
DevOps brings together developers and operations teams to create better software by introducing organizational principles that encourage communication, collaboration, innovation, speed, security, and agility throughout the software development lifecycle. And, the popularity and adoption rates of DevOps continue to grow, with 83% of 10,000 global developers surveyed saying that they use the principles, according to an April 2024 report commissioned by the Continuous Delivery Foundation (CDF), a Linux Foundation project.
DevOps includes everything from continuous integration/improvement and continuous deployment/delivery (CI/CD) as code is created and modified, to critical automation capabilities covering a wide range of development processes. Also built into DevOps principles is a focus on creating better applications from code conception all the way through to end-user experiences. Before this unified framework existed, code typically was created in separate silos that did not easily allow collaboration or foster efficient management, speed, or quality. These conditions eventually inspired the DevOps framework and principles.
DevOps principles and practices also help organizations by constantly integrating user feedback regarding application features, shortcomings, and code glitches, thereby reducing security and operational risks in code as it reaches production.
This blog post aims to help enterprises focus on one of these critical DevOps capabilities in particular — the use of automation to speed and streamline processes across the development lifecycle of applications — to further expand and drive the benefits of using DevOps processes within an organization.
As DevOps use continues to grow, more developers are finding that the Docker containerization platform integrates well as a crucial component of DevOps practices, especially due to its built-in automation features and capabilities.
What is DevOps automation?
DevOps automation is a major time-saver for developers and operations teams because it automates labor-intensive and repetitive processes that can free up developers to instead work on new code innovations and ideas that can create business value.
Automating repetitive manual tasks using DevOps automation tools drives notable efficiencies and productivity boosts for developers and organizations, using automatic actions that eliminate frequent developer or operations team intervention.
What DevOps processes can you automate?
DevOps automation is especially valuable because it can be used on a broad spectrum of tasks in the application development environment, including CI/CD pipelines and workflows, code writing, monitoring and logging, and Infrastructure as Code (IaC) tools. It can also help improve and streamline configuration management, infrastructure provisioning, unit tests, code testing, security steps and scans, troubleshooting, code review, deploying and delivering code, project management, and more.
By bringing beneficial and time-saving automation to the DevOps lifecycle, developers can create cleaner and more secure code with much less manual intervention and human error compared to traditional software development methods.
Benefits of DevOps automation tools
For development and operations teams, using DevOps automation to streamline and improve their operations goes far beyond just reducing human error rates and increasing the efficiency and speed of code creation and the deployment process.
Other benefits of DevOps automation include improved consistency and reliability, delivery of predictable and repeatable results, and enhanced scalability and manageability of multiple applications and processes. These benefits become possible with automation because it reduces many human mistakes and miscalculations.
DevOps automation benefits can also include smoother collaboration among multiple developers working on applications at the same time by automatically handling merge conflicts, and performing automatic code testing for multiple developers at once. Automation that troubleshoots applications can also speed up project development times by immediately notifying systems personnel of problems as they arise.
How to automate DevOps with Docker
As a flexible tool for DevOps automation, Docker is available in four subscription levels, from the free Docker Personal version to the top-of-the-line Docker Business tier.
Docker Business delivers a wide range of helpful tools that empower DevOps teams to identify development bottlenecks where automation can free up resources and resolve repetitive tasks and operations. The following tools are included with Docker Business. (Read our September 2024 announcement about upgraded Docker subscription plans that will deliver even more value, flexibility, and power to your development workflows.)
Docker Image Access Management
With Docker Business, developers and operations teams can quickly start automating tasks using features such as Docker Image Access Management, which gives administrators control over the types of container images that developers can pull and use from Docker Hub. This includes Docker Official Images, Docker Verified Publisher Images, and community images. Using Image Access Management, developers and teams can more easily search private registries and community repositories for needed container images to use to build their applications.
Image Access Management allows organizations to give developers freedom of choice while providing some guardrails to prevent developers from accidentally using untrusted, malicious community images as components of their applications. This is an important benefit, compared with only allowing developers to use a handful of internally built images, for example.
Docker Image Access Management is available only to Docker Business customers.
Docker automated testing
Other Docker DevOps automation features include automated testing, including source code repository testing, that can be done through Docker Hub to automatically test changes to source code repositories using containers. Any Docker Hub repository can enable an autotest function to run tests on pull requests to the source code repository to create a continuous integration testing service.
Automated test files to perform the tests can be set up by creating a docker-compose.test.yml file, which defines a service that lists the tests to be run. The docker-compose.test.yml file should be placed in the same directory that contains the Dockerfile used to build the image.
Hardened Docker Desktop
To automate security within Docker, administrators can use a wide range of features within Hardened Docker Desktop, which is available to Docker Business subscribers. Hardened Docker Desktop security features aim to bolster the security of developer environments while causing minimal speed or performance impacts on developer experiences or productivity.
These features allow administrators to enforce strict security settings, which prevent developers and containers from bypassing the controls intentionally or unintentionally. The features also enable enhanced container isolation capabilities to prevent potential security threats, such as malicious payloads, from breaching the Docker Desktop Linux VM and the underlying host.
Using Hardened Docker Desktop, security administrators can take more control and ownership over Docker Desktop configurations, removing and preventing potential changes by users, which is vital for security-conscious organizations.
Automated builds
Another automation and productivity tool is the Docker Automated builds feature, which automatically builds images from source code in an external repository and then pushes the built image to designated Docker repositories. Available in the Docker Business, Pro, or Teams tiers, Automated builds — also called autobuilds — create a list of branches and tags that can be built into Docker images using a series of commands. Automated builds can handle images of up to 10 GB in size.
Enhanced collaboration tools
Throughout Docker’s unified suite, tools built to deliver enhanced collaboration are available to developers and operations teams to work together to get the most out of their projects and applications.
Easier scaling and orchestration with Kubernetes integration
Docker’s containerization platform also integrates well with the Kubernetes container orchestration platform, optimizing the developer experience for container development, deployment, and management. Docker and Kubernetes can work together using Docker Engine as a user-friendly and secure foundation for basic Kubernetes (K8s) functionality, or by using Docker Desktop for a more comprehensive approach that avoids potential challenges associated with do-it-yourself container configurations. Docker Desktop includes K8s setup at the push of a button, which is one of its numerous and useful automation features.
Support and troubleshooting
As Docker continues to mature, its knowledge base is constantly being expanded and deepened, with core documentation and resources freely available to Docker developers within the Docker ecosystem. And, because Docker uses a collaborative approach between developers and operations teams, developers can often find common answers to their inquiries and learn from each other to tackle most issues.
More information and help about using Docker can be found in the Docker Training page, which offers live and on-demand training and other resources to help developers and teams negotiate their Docker landscapes and learn fresh skills to resolve technical problems.
Other resources: Docker Scout and Docker Build Cloud
Docker offers even more tools to help with automation, collaboration, and creating better and more nimble code for developer teams and operations managers.
Docker Scout, for example, is built to help organizations better protect their software supply chain security when using container images, which may contain software elements that are susceptible to security vulnerabilities.
Docker Scout helps with this issue by proactively analyzing container images and compiling a Software Bill of Materials (SBOM), which is a detailed inventory of code included in an application or container. That SBOM is then matched against a continuously updated vulnerability database to pinpoint and correct security weaknesses to help make the code more secure.
Docker Build Cloud is a Docker service to help developers build container images more quickly, both locally and in the cloud. Those builds run on cloud infrastructure that requires no configuration and where the environment is optimally dimensioned for all workloads using a remote build cache. This approach ensures fast builds anywhere for all team members.
To use Docker Build Cloud, developers take the same steps they would take for a regular build using the command docker buildx build. With a regular build command, the build runs on a local instance of BuildKit, bundled with the Docker daemon. But when using Docker Build Cloud, the build request is sent to a BuildKit instance running remotely, in the cloud, with all data encrypted in transit. Docker Build Cloud provides several benefits over local builds, including faster build speed, shared build cache, and native multi-platform builds.
Future trends in DevOps automation
As DevOps automation continues to mature, it will gain more capabilities from artificial intelligence (AI), machine learning (ML), serverless architectures, cloud-native platforms, and other technologies across the IT landscape.
Such advancements can be found in Docker’s AI collaborations with NVIDIA. For example, Docker Desktop dovetails with the NVIDIA AI Workbench, which is an easy-to-use toolkit that lets developers create, test, and customize AI and machine learning models on a PC or workstation and then scale them to a data center or public cloud. NVIDIA AI Workbench makes interactive development workflows easier, while automating technical tasks that can halt beginners and derail experts.
DevOps automation is ripe for further improvements and enhancements from AI and ML in areas of agility, process improvements, and more for developers and operations teams. AI and ML will drive further labor savings for software development teams by delivering fresh new automated, self-service tools that free them up from a broader range of routine tasks, giving them more time to conduct valuable and critical work that will drive their companies forward.
Docker will be an important part of this changing landscape as the unified suites and tools continue to expand and deliver further new benefits and capabilities to DevOps, the Docker ecosystem, and developers and operations teams around the world.
Wrapping up
Improving DevOps automation by using the Docker containerization platform inside your business organization is a smart strategy that helps developers and operations teams deliver their best work with efficiency, creativity, and broad collaboration.
Docker Business plays a leadership role in enhancing DevOps automation in companies around the world as they look to automate their DevOps operations effectively.
I recently joined Docker in January as Chief Revenue Officer. My role is responsible for the entire customer journey, from your first interaction with Docker’s sales org to post-sales support and onboarding. As I speak with customers and hear stories about their journey with Docker over the past decade, I’m often reminded of the immense trust you’ve placed in us. Whether you’ve been with us from the days of Docker Swarm or have more recently started using Docker Desktop, your partnership has been invaluable in shaping who we are today.
I want to take a moment to personally thank you for being part of our story, especially as we continue to evolve in a rapidly changing ecosystem.
We know that change can bring challenges. Over the years, as containers became the backbone of modern software development, Docker has evolved alongside them. This evolution has not always been easy and I understand that shifts in our product offerings, changes in pricing, and recent adjustments to our subscription plans have impacted many of you. Our priority now, as it always has been, is to deliver unrivaled value to you.
We recognize that to continue innovating and addressing the complex needs of modern developers, we must continue to invest in Docker products and our relationships with customers like you. This investment isn’t just about tools and features; it’s about creating a holistic ecosystem — a unified suite — that makes your development process more productive, secure, and manageable at an enterprise scale, while building a go-to-market organization that is equipped to support our growing customer base.
To that end, we’ve redefined our strategy to focus on a deeper, more meaningful engagement with you. We’re committed to building stronger relationships, listening carefully to your feedback, and ensuring that the solutions we bring to market truly address your pain points. By focusing on your needs, we’re working to make every interaction with Docker more valuable, whether it’s through enhanced support, new features, or better licensing management. If you’d like to discuss this with me further, I’m happy to schedule time. (Reach out by email or connect with your Account Executive to set this up.)
Additionally, we’ve made key investments in our enterprise suite of products that surrounds Docker Desktop. We understand that the demands of modern development extend beyond the individual developer’s experience. Docker is the only container-first platform built specifically for development teams, improving developer experience and productivity while meeting the security and control needs of modern enterprises. Docker offers a comprehensive suite of enterprise-ready tools, cloud services, trusted content, and a collaborative community that helps streamline workflows and maximize development efficiency.
As we continue to invest in both vectors above, we’re excited about what lies ahead in our product roadmap. Our aim is simple: to help your teams develop with confidence, knowing that Docker is a trusted partner invested in your success. I am personally dedicated to ensuring that our roadmap reflects your needs and that our solutions empower your teams to reach their full potential.
Thank you again for your continued trust and partnership. We wouldn’t be here without you, and I look forward to what we will achieve together.
This post was contributed by Jesús Espino, Principal Engineer at Mattermost.
In the ever-evolving software development landscape, ensuring robust and reliable plugin integration is no small feat. For Mattermost, relying solely on mocks for plugin testing became a limitation, leading to brittle tests and overlooked integration issues. Enter Testcontainers, an open source tool that provides isolated Docker environments, making complex integration testing not only feasible but efficient.
In this blog post, we dive into how Mattermost has embraced Testcontainers to overhaul its testing strategy, achieving greater automation, improved accuracy, and seamless plugin integration with minimal overhead.
The previous approach
In the past, Mattermost relied heavily on mocks to test plugins. While this approach had its merits, it also had significant drawbacks. The tests were brittle, meaning they would often break when changes were made to the codebase. This made the tests challenging to develop and maintain, as developers had to constantly update the mocks to reflect the changes in the code.
Furthermore, the use of mocks meant that the integration aspect of testing was largely overlooked. The tests did not account for how the different components of the system interacted with each other, which could lead to unforeseen issues in the production environment.
The previous approach additionally did not allow for proper integration testing in an automated way. The lack of automation made the testing process time-consuming and prone to human error. These challenges necessitated a shift in Mattermost’s testing strategy, leading to the adoption of Testcontainers for complex integration testing.
Mattermost’s approach to integration testing
Testcontainers for Go
Mattermost uses Testcontainers for Go to create an isolated testing environment for our plugins. This testing environment includes the Mattermost server, the PostgreSQL server, and, in certain cases, an API mock server. The plugin is then installed on the Mattermost server, and through regular API calls or end-to-end testing frameworks like Playwright, we perform the required testing.
We have created a specialized Testcontainers module for the Mattermost server. This module uses PostgreSQL as a dependency, ensuring that the testing environment closely mirrors the production environment. Our module allows the developer to install and configure any plugin you want in the Mattermost server easily.
To improve the system’s isolation, the Mattermost module includes a container for the server and a container for the PostgreSQL database, which are connected through an internal Docker network.
Additionally, the Mattermost module exposes utility functionality that allows direct access to the database, to the Mattermost API through the Go client, and some utility functions that enable admins to create users, channels, teams, and change the configuration, among other things. This functionality is invaluable for performing complex operations during testing, including API calls, users/teams/channel creation, configuration changes, or even SQL query execution.
This approach provides a powerful set of tools with which to set up our tests and prepare everything for verifying the behavior that we expect. Combined with the disposable nature of the test container instances, this makes the system easy to understand while remaining isolated.
This comprehensive approach to testing ensures that all aspects of the Mattermost server and its plugins are thoroughly tested, thereby increasing their reliability and functionality. But, let’s see a code example of the usage.
We can start setting up our Mattermost environment with a plugin like this:
Here, you can decide when you tear down your Mattermost instance and recreate it. Once per test? Once per a set of tests? It is up to you and depends strictly on your needs and the nature of your tests.
Testcontainers for Node.js
In addition to using Testcontainers for Go, Mattermost leverages Testcontainers for Node.js to set up our testing environment. In case you’re unfamiliar, Testcontainers for Node.js is a Node.js library that provides similar functionality to Testcontainers for Go. Using Testcontainers for Node.js, we can set up our environment in the same way we did with Testcontainers for Go. This allows us to write Playwright tests using JavaScript and run them in the isolated Mattermost environment created by Testcontainers, enabling us to perform integration testing that interacts directly with the plugin user interface. The code is available on GitHub.
This approach provides the same advantages as Testcontainers for Go, and it allows us to use a more interface-based testing tool — like Playwright in this case. Let me show a bit of code with the Node.js and Playwright implementation:
With these two approaches, we can create integration tests covering the API and the interface without having to mock or use any other synthetic environment. Also, we can test things in absolute isolation because we consciously decide whether we want to reuse the Testcontainers instances. We can also reach a high degree of isolation and thereby avoid the flakiness induced by contaminated environments when doing end-to-end testing.
Examples of usage
Currently, we are using this approach for two plugins.
This integration helps users in their daily tasks using AI large language models (LLMs), providing things like thread and meeting summarization and context-based interrogation.
This plugin has a rich interface, so we used the Testcontainers for Node and Playwright approach to ensure we could properly test the system through the interface. Also, this plugin needs to call the AI LLM through an API. To avoid that resource-heavy task, we use an API mock, another container that simulates any API.
This approach gives us confidence in the server-side code but in the interface side as well, because we can ensure that we aren’t breaking anything during the development.
This integration is designed to connect MS Teams and Mattermost in a seamless way, synchronizing messages between both platforms.
For this plugin, we mainly need to do API calls, so we used Testcontainers for Go and directly hit the API using a client written in Go. In this case, again, our plugin depends on a third-party service: the Microsoft Graph API from Microsoft. For that, we also use an API mock, enabling us to test the whole plugin without depending on the third-party service.
We still have some integration tests with the real Teams API using the same Testcontainers infrastructure to ensure that we are properly handling the Microsoft Graph calls.
Benefits of using Testcontainers libraries
Using Testcontainers for integration testing offers benefits, such as:
Isolation: Each test runs in its own Docker container, which means that tests are completely isolated from each other. This approach prevents tests from interfering with one another and ensures that each test starts with a clean slate.
Repeatability: Because the testing environment is set up automatically, the tests are highly repeatable. This means that developers can run the tests multiple times and get the same results, which increases the reliability of the tests.
Ease of use: Testcontainers is easy to use, as it handles all the complexities of setting up and tearing down Docker containers. This allows developers to focus on writing tests rather than managing the testing environment.
Testing made easy with Testcontainers
Mattermost’s use of Testcontainers libraries for complex integration testing in their plugins is a testament to the power and versatility of Testcontainers.
By creating a well-isolated and repeatable testing environment, Mattermost ensures that our plugins are thoroughly tested and highly reliable.
This ongoing Docker Labs GenAI series explores the exciting space of AI developer tools. At Docker, we believe there is a vast scope to explore, openly and without the hype. We will share our explorations and collaborate with the developer community in real time. Although developers have adopted autocomplete tooling like GitHub Copilot and use chat, there is significant potential for AI tools to assist with more specific tasks and interfaces throughout the entire software lifecycle. Therefore, our exploration will be broad. We will be releasing software as open source so you can play, explore, and hack with us, too.
LLMs are now quite good at transforming data. For example, we were recently working with some data generated by the Pylint tool. This tool generates big arrays of code violations.
Here’s an example showing the kind of data that gets returned.
During this session with our AI assistant, we decided that it would be helpful to create a database and insert the data to make it easier for the AI to analyze (LLMs are very good at writing SQL). As is now our habit, we wrote a quick prompt to see if the assistant could generate the SQL:
1. Read the json data from /thread/violations.json
2. For each element in the array, transform each element into two SQL INSERT statements.
* The first should insert columns PATH, START_LINE, END_LINE, START_COLUMN, END_COLUMN into a table named RANGES.
* The second should insert columns MESSAGE, TYPE, RANGE, and VIOLATION_ID into a table named VIOLATIONS.
LLMs are obviously good at tasks of this kind, and this was no exception. Our prompt engine had been augmented with a function to read local files but, besides that, this was a pretty straightforward prompt (we used GPT-3.5). The LLM responded with the correct INSERT statements.
INSERT INTO RANGES (PATH, START_LINE, END_LINE, START_COLUMN, END_COLUMN)
VALUES ('src/app.py', 1, 1, 0, 13);
INSERT INTO VIOLATIONS (MESSAGE, TYPE, RANGE, VIOLATION_ID)
VALUES ('Missing module docstring', 'convention', (SELECT ID FROM RANGES WHERE PATH='src/app.py' AND START_LINE=1 AND START_COLUMN=0), 'C0114');
...
...
We’re starting to get accustomed to this kind of capability, so the result wasn’t too surprising. However, what about the context window here? It really doesn’t make a lot of sense to pass all of this data to the LLM, especially if this is a task that we’ll need to do continuously. Plus, this is really not how a programmer would have solved the problem.
Programmers would write programs.
So, instead of asking the LLM to do a thing, we should try asking it to write a program to do that same thing. Starting with the same prompt, let’s prefix it with “Please write a JavaScript program to …” In other words, let’s ask the LLM to describe how it would accomplish the task in JavaScript, and hopefully automate itself out of the loop. Here’s the adjusted prompt:
Write a javascript function that reads json from a file named `/thread/violations.json`.
For each element in the array, transform each element into two SQL INSERT statements.
* The first should insert columns PATH, START_LINE, END_LINE, START_COLUMN, END_COLUMN into a table named RANGES.
* The second should insert columns MESSAGE, TYPE, RANGE, and VIOLATION_ID into a table named VIOLATIONS.
and write them to the file /thread/insert.sql
Now execute the javascript code in a container.
Tool code
Current LLMs can do tasks of this kind. Here’s a sample of the output generated by GPT-3.5. A quick scan will convince many of you that this is probably going to do the trick. However, if our prompt engine is already running in Docker, we might as well just run it:
const fs = require('fs');
const crypto = require('crypto');
function getRandomId() {
return crypto.randomBytes(16).toString('hex');
}
function escapeString(str) {
return str.replace(/'/g, \\\"''\\\");
}
let data = JSON.parse(fs.readFileSync('/thread/violations.json', 'utf8'));
let sqlStatements = '';
data.forEach((violation) => {
let id = getRandomId();
sqlStatements += `INSERT INTO RANGES(ID, PATH, START_LINE, END_LINE, START_COLUMN, END_COLUMN) VALUES('${id}', '${escapeString(violation.path)}', ${violation.line}, ${violation.endLine}, ${violation.column}, ${violation.endColumn});\\\\n`;
sqlStatements += `INSERT INTO VIOLATIONS(MESSAGE, TYPE, RANGE) VALUES('${escapeString(violation.message)}', '${escapeString(violation.type)}', '${id}');\\\\n`;
});
fs.writeFileSync('/thread/insert.sql', sqlStatements);
Running the tool
You may not have noticed but at the end of our prompt, we added a final instruction: “Now execute the JavaScript code in a container”. This is a nice addition to our session, because it means we get to see the results.
This is also where tool calling comes back into the picture. To give our AI the capacity to try running the program that it has just written, we have defined a new function to create an isolated runtime sandbox for trying out our new tool.
Here’s the agent’s new tool definition:
tools:
- name: run-javascript-sandbox
description: execute javascript code in a container
parameters:
type: object
properties:
javascript:
type: string
description: the javascript code to run
container:
image: vonwig/javascript-runner
command:
- "{{javascript|safe}}"
We’ve asked the AI assistant to generate a tool from a description of that tool. As long as the description of the tools doesn’t change, the workflow won’t have to go back to the AI to ask it to build a new tool version.
The role of Docker in this pattern is to create the sandbox for this code to run. This function really doesn’t need much of a runtime, so we give it a pretty small sandbox.
No access to a network.
No access to the host file system (does have access to isolated volumes for sharing data between tools).
No access to GPU.
Almost no access to software besides the Node.js runtime (no shell for example).
The ability for one tool to create another tool is not just a trick. It has very practical implications for the kinds of workflows that we can build up because it gives us a way for us to control the volume of data sent to LLMs, and it gives the assistant a way to “automate” itself out of the loop.
Next steps
This example was a bit abstract but in our next post, we will describe the practical scenarios that have driven us to look at this idea of prompts generating new tools. Most of the workflows we’re exploring are still just off-the-shelf tools like Pylint, SQLite, and tree_sitter (which we embed using Docker, of course!). For example:
Use pylint to extract violations from my codebase.
Transform the violations into SQL and then send that to a new SQLite.
Find the most common violations of type error and show me the top level code blocks containing them.
However, you’ll also see that part of being able to author workflows of this kind is being able to recognize when you just need to add a custom tool to the mix.
With many organizations moving to container-based workflows, keeping track of the different versions of your images can become a problem. Even smaller organizations can have hundreds of container images spanning from one-off development tests, through emergency variants to fix problems, all the way to core production images. This leads us to the question: How can we tame our image sprawl while still rapidly iterating our images?
A common misconception is that by using the “latest” tag, you are guaranteeing that you are pulling the “latest” version of the image. Unfortunately, this assumption is wrong — all latest means is “the last image pushed to this registry.”
Read on to learn more about how to avoid this pitfall when using Docker and how to get a handle on your Docker images.
Using tags
One way to address this issue is to use tags when creating an image. Adding one or more tags to an image helps you remember what it is intended for and helps others as well. One approach is always to tag images with their semantic versioning (semver), which lets you know what version you are deploying. This sounds like a great approach, and, to some extent, it is, but there is a wrinkle.
Unless you’ve configured your registry for immutable tags, tags can be changed. For example, you could tag my-great-app as v1.0.0 and push the image to the registry. However, nothing stops your colleague from pushing their updated version of the app with tag v1.0.0 as well. Now that tag points to their image, not yours. If you add in the convenience tag latest, things get a bit more murky.
Let’s look at an example:
FROM busybox:stable-glibc
# Create a script that outputs the version
RUN echo -e "#!/bin/sh\n" > /test.sh && \
echo "echo \"This is version 1.0.0\"" >> /test.sh && \
chmod +x /test.sh
# Set the entrypoint to run the script
ENTRYPOINT ["/bin/sh", "/test.sh"]
We build the above with docker build -t tagexample:1.0.0 . and run it.
$ docker run --rm tagexample:1.0.0
This is version 1.0.0
What if we run it without a tag specified?
$ docker run --rm tagexample
Unable to find image 'tagexample:latest' locally
docker: Error response from daemon: pull access denied for tagexample, repository does not exist or may require 'docker login'.
See 'docker run --help'.
Now we build with docker build . without specifying a tag and run it.
$ docker run --rm tagexample
This is version 1.0.0
The latest tag is always applied to the most recent push that did not specify a tag. So, in our first test, we had one image in the repository with a tag of 1.0.0, but because we did not have any pushes without a tag, the latest tag did not point to an image. However, once we push an image without a tag, the latest tag is automatically applied to it.
Although it is tempting to always pull the latest tag, it’s rarely a good idea. The logical assumption — that this points to the most recent version of the image — is flawed. For example, another developer can update the application to version 1.0.1, build it with the tag 1.0.1, and push it. This results in the following:
$ docker run --rm tagexample:1.0.1
This is version 1.0.1
$ docker run --rm tagexample:latest
This is version 1.0.0
If you made the assumption that latest pointed to the highest version, you’d now be running an out-of-date version of the image.
The other issue is that there is no mechanism in place to prevent someone from inadvertently pushing with the wrong tag. For example, we could create another update to our code bringing it up to 1.0.2. We update the code, build the image, and push it — but we forget to change the tag to reflect the new version. Although it’s a small oversight, this action results in the following:
$ docker run --rm tagexample:1.0.1
This is version 1.0.2
Unfortunately, this happens all too frequently.
Using labels
Because we can’t trust tags, how should we ensure that we are able to identify our images? This is where the concept of adding metadata to our images becomes important.
The first attempt at using metadata to help manage images was the MAINTAINER instruction. This instruction sets the “Author” field (org.opencontainers.image.authors) in the generated image. However, this instruction has been deprecated in favor of the more powerful LABEL instruction. Unlike MAINTAINER, the LABEL instruction allows you to set arbitrary key/value pairs that can then be read with docker inspect as well as other tooling.
Unlike with tags, labels become part of the image, and when implemented properly, can provide a much better way to determine the version of an image. To return to our example above, let’s see how the use of a label would have made a difference.
To do this, we add the LABEL instruction to the Dockerfile, along with the key version and value 1.0.2.
FROM busybox:stable-glibc
LABEL version="1.0.2"
# Create a script that outputs the version
RUN echo -e "#!/bin/sh\n" > /test.sh && \
echo "echo \"This is version 1.0.2\"" >> /test.sh && \
chmod +x /test.sh
# Set the entrypoint to run the script
ENTRYPOINT ["/bin/sh", "/test.sh"]
Now, even if we make the same mistake above where we mistakenly tag the image as version 1.0.1, we have a way to check that does not involve running the container to see which version we are using.
Although you can use any key/value as a LABEL, there are recommendations. The OCI provides a set of suggested labels within the org.opencontainers.image namespace, as shown in the following table:
Label
Content
org.opencontainers.image.created
The date and time on which the image was built (string, RFC 3339 date-time).
org.opencontainers.image.authors
Contact details of the people or organization responsible for the image (freeform string).
org.opencontainers.image.url
URL to find more information on the image (string).
org.opencontainers.image.documentation
URL to get documentation on the image (string).
org.opencontainers.image.source
URL to the source code for building the image (string).
org.opencontainers.image.version
Version of the packaged software (string).
org.opencontainers.image.revision
Source control revision identifier for the image (string).
org.opencontainers.image.vendor
Name of the distributing entity, organization, or individual (string).
org.opencontainers.image.licenses
License(s) under which contained software is distributed (string, SPDX License List).
org.opencontainers.image.ref.name
Name of the reference for a target (string).
org.opencontainers.image.title
Human-readable title of the image (string).
org.opencontainers.image.description
Human-readable description of the software packaged in the image (string).
Because LABEL takes any key/value, it is also possible to create custom labels. For example, labels specific to a team within a company could use the com.myorg.myteam namespace. Isolating these to a specific namespace ensures that they can easily be related back to the team that created the label.
Final thoughts
Image sprawl is a real problem for organizations, and, if not addressed, it can lead to confusion, rework, and potential production problems. By using tags and labels in a consistent manner, it is possible to eliminate these issues and provide a well-documented set of images that make work easier and not harder.
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