Containerizing AI Workloads for Simplified Development and Deployment
**Containerizing AI Workloads: Simplifying Development and Deployment**
One of the biggest challenges associated with AI workloads is managing all the dependencies required to run them smoothly. From drivers to libraries, the list of requirements can be overwhelming, especially for hardware-accelerated tasks. A wrong version of CUDA, ROCm, or PyTorch can lead to frustrating errors and compatibility issues.
To tackle this problem, many developers are turning to containerization. By creating isolated environments within containers, developers can build images specifically configured for their tasks. This not only streamlines the development process but also ensures consistent and repeatable deployments every time.
Containers also offer the flexibility to run multiple apps with conflicting software stacks simultaneously. For example, you can have two containers, one with CUDA 11 and the other with CUDA 12, running concurrently without any issues.
Chipmakers like Nvidia often provide containerized versions of their accelerated-computing software libraries to users. These pre-built images serve as a consistent starting point for development, making it easier for developers to get started with AI projects.
In this tutorial, we explore various ways containerization can assist in the development and deployment of AI workloads, whether CPU or GPU accelerated. We focus on using Docker, a popular container runtime known for its simplicity and broad compatibility.
**Exposing GPUs to Docker Containers**
Unlike virtual machines, Docker containers allow you to pass GPUs through to multiple containers without exceeding available vRAM. For Intel GPUs, you can simply append `–device /dev/dri` to the `docker run` command. Similarly, for AMD GPUs, use `–device /dev/kfd`.
For Nvidia GPUs, you’ll need to install the Nvidia Container Toolkit before exposing them to Docker containers. By adding the toolkit repository to your sources list and configuring Docker to use the Nvidia runtime, you can easily expose Nvidia GPUs to your containers.
**Using Docker Containers as Development Environments**
One of the most valuable applications of Docker containers in AI development is creating development environments. By spinning up containers with GPU access and installing necessary libraries like CUDA, ROCm, PyTorch, or TensorFlow, developers can work in isolated environments without impacting their host systems.
**Using Prebuilt and Custom Images**
While building containers from scratch can be time-consuming, there are prebuilt images available for popular AI frameworks like CUDA, ROCm, and OpenVINO. These images simplify the setup process and save developers time.
Additionally, developers can convert existing containers into reproducible images or build custom images using Dockerfiles. By defining the base image, copying necessary files, and installing dependencies, developers can create custom images tailored to their specific requirements.
**Nvidia Inference Microservices (NIMs)**
Nvidia’s NIMs offer optimized containers with specific software versions tuned for performance on Nvidia hardware. These containers are designed to deliver the best performance and can be easily updated to leverage new features or improvements.
While NIMs will be available for free for research and testing purposes, deploying them in production will require an AI Enterprise license. Developers can still build their own optimized images following the steps outlined in this tutorial.
In conclusion, containerizing AI workloads offers a range of benefits, from simplifying development and deployment to optimizing performance on specific hardware configurations. Whether using prebuilt images or creating custom containers, developers can leverage containerization to streamline their AI projects effectively.
