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.

Learn more

• Dive into the AI Catalog.
• Join the AI Catalog.
• Subscribe to the Docker Newsletter. 
• Get the latest release of Docker Desktop.
• Have questions? The Docker community is here to help.
• New to Docker? Get started.

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

You can check IBM Granite’s documentation for details on using IBM Granite Models.

Join us at IBM TechXchange

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!

Learn more

• Read the Docker Labs GenAI series.
• Subscribe to the Docker Newsletter. 
• What is InstructLab?
• What are Granite Models?
• Accelerating AI Development with IBM Granite AI Models and Docker — IBM TechXchange session with Eli Aleyner.
• Developer productivity for apps with AI – IBM TechXchange session with Oleg Šelajev.

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:

terraform {
  required_providers {
    docker = {
      source  = "docker/docker"
      version = "~> 0.2"
    }
  }
}

# Initialize provider
provider "docker" {}

# Define local variables for customization
locals {
  namespace        = "my-docker-namespace"
  repo_name        = "my-docker-repo"
  org_name         = "my-docker-org"
  team_name        = "my-team"
  my_team_users    = ["user1", "user2"]
  token_label      = "my-pat-token"
  token_scopes     = ["repo:read", "repo:write"]
  permission       = "admin"
}

# Create repository
resource "docker_hub_repository" "org_hub_repo" {
  namespace        = local.namespace
  name             = local.repo_name
  description      = "This is a generic Docker repository."
  full_description = "Full description for the repository."
}

# Create team
resource "docker_org_team" "team" {
  org_name         = local.org_name
  team_name        = local.team_name
  team_description = "Team description goes here."
}

# Team association
resource "docker_org_team_member" "team_membership" {
  for_each = toset(local.my_team_users)

  org_name  = local.org_name
  team_name = docker_org_team.team.team_name
  user_name = each.value
}

# Create repository team permission
resource "docker_hub_repository_team_permission" "repo_permission" {
  repo_id    = docker_hub_repository.org_hub_repo.id
  team_id    = docker_org_team.team.id
  permission = local.permission
}

# Create access token
resource "docker_access_token" "access_token" {
  token_label = local.token_label
  scopes      = local.token_scopes
}

Future work

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.

For feedback and issue tracking, visit the official Docker Terraform Provider repository or submit feedback via our issue tracker.

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.

Learn more

• Visit the official Docker Terraform Provider repository.
• Submit feedback via our issue tracker.
• Subscribe to the Docker Newsletter. 
• Get the latest release of Docker Desktop.
• Have questions? The Docker community is here to help.
• New to Docker? Get started.

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.

$ docker inspect --format='{{json .Config.Labels}}' tagexample:1.0.1
{"version":"1.0.2"}

Best practices

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:

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.

Learn more

• Read the Docker Best Practices collection.
• Subscribe to the Docker Newsletter. 
• Get the latest release of Docker Desktop.
• Vote on what’s next! Check out our public roadmap.
• Have questions? The Docker community is here to help.
• New to Docker? Get started.