Docker Partners with NVIDIA to Support Building and Running AI/ML Applications

The domain of GenAI and LLMs has been democratized and tasks that were once purely in the domain of

The domain of GenAI and LLMs has been democratized and tasks that were once purely in the domain of AI/ML developers must now be reasoned with by regular application developers into everyday products and business logic. This is leading to new products and services across banking, security, healthcare, and more with generative text, images, and videos. Moreover, GenAI’s potential economic impact is substantial, with estimates it could add trillions of dollars annually to the global economy.  Docker offers an ideal way for developers to build, test, run, and deploy the software platform — an end-to-end, cloud-native software platform that brings generative AI within reach for every business. The platform , deployable as microservices. This enables teams to focus on cutting-edge AI applications where performance isn’t just a goal — it’s a necessity. This week, at the global AI conference, the latest release of was announced, providing businesses with the tools and frameworks necessary to build and deploy custom generative AI models with , the , and the NVIDIA NIM inference microservices, which deliver enhanced performance and efficient runtime.  This blog post summarizes some of the Docker resources available to customers today. is the world’s largest repository for container images with an extensive collection of AI/ML development-focused container images, including leading frameworks and tools such as , , , , and . With for AI/ML-related images, Docker Hub’s significance to the developer community is self-evident. It not only simplifies the development of AI/ML applications but also democratizes innovation, making AI technologies accessible to developers across the globe. offers a suite of that harness the power of accelerated computing, supplementing . Docker Hub’s vast audience — which includes approximately 27 million monthly active IPs, showcasing an impressive 47% year-over-year growth — can use these container images to enhance AI performance.  Docker Hub’s extensive reach, underscored by an astounding 26 billion monthly image pulls, suggests immense potential for continued growth and innovation. on Windows and Mac helps deliver NVIDIA AI Workbench developers a smooth experience on local and remote machines.  is an easy-to-use toolkit that allows developers to create, test, and customize AI and machine learning models on their PC or workstation and scale them to the data center or public cloud. It simplifies interactive development workflows while automating technical tasks that halt beginners and derail experts. AI Workbench makes workstation setup and configuration fast and easy. Example projects are also included to help developers get started even faster with their own data and use cases.    Docker engineering teams are collaborating with NVIDIA to improve the user experience with NVIDIA GPU-accelerated platforms through recent improvements to the AI Workbench installation on WSL2. Check out how can be used locally to tune a generative image model to produce more accurate prompted results: In a near-term update, AI Workbench will use the to govern local and remote GPU-enabled environments. CDI is a CNCF-sponsored project led by NVIDIA and Intel, which exposes NVIDIA GPUs inside of containers to support complex device configurations and CUDA compatibility checks. This simplifies how research, simulation, GenAI, and ML applications utilize local and cloud-native GPU resources.   With Docker Desktop 4.29 (which includes Moby 25), developers can and then easily make all NVIDIA GPUs available in a running container by using the via support for CDI devices. The lets teams easily integrate NVIDIA accelerated computing into their AI workflows. This stack, designed for seamless component integration, can be set up on a developer’s laptop using Docker Desktop for Windows. It helps deliver the power of NVIDIA GPUs and NVIDIA NIM to accelerate LLM inference, providing tangible improvements in application performance. Developers can experiment and modify five pre-packaged applications to leverage the stack’s capabilities. facilitates an accelerated machine learning development environment on a developer’s laptop. By tapping NVIDIA GPU support for containers, developers can leverage tools distributed via Docker Hub, such as PyTorch and TensorFlow, to see significant speed improvements in their projects, underscoring the efficiency gains possible with NVIDIA technology on Docker. Securing the software supply chain is a crucial aspect of continuously developing ML applications that can run reliably and securely in production. Building with verified, trusted content from and staying on top of security issues through actionable insights from is key to improving security posture across the software supply chain. By following these best practices, customers can minimize the risk of security issues hitting production, improving the overall reliability and integrity of applications running in production. This comprehensive approach not only accelerates the development of ML applications built with the Docker GenAI Stack but also allows for more secure images when building on images sourced from Hub that interface with LLMs, such as Ultimately, this provides developers with the confidence that their applications are built on a secure and reliable foundation. With exploding interest in AI from a huge range of developers, we are excited to work with NVIDIA to build tooling that helps accelerate building AI applications. The ecosystem around Docker and NVIDIA has been building strong foundations for many years and this is enabling a new community of enterprise AI/ML developers to explore and build GPU accelerated applications.” Enterprise applications like NVIDIA AI Workbench can benefit enormously from the streamlining that Docker Desktop provides on local systems. Our work with the Docker team will help improve the AI Workbench user experience for managing GPUs on Windows.” By leveraging Docker Desktop and Docker Hub with NVIDIA technologies, developers are equipped to harness the revolutionary power of AI, grow their skills, and seize opportunities to deliver innovative applications that push the boundaries of what’s possible. Check out  and to get started with your own AI solutions.