Containerization simplifies the task of managing and distributing software. It bundles up applications and all their software dependencies in portable packages that can be moved easily from system to system.\nThis approach to software distribution simplifies IT operations. Software development teams can put all the pieces and parts together into a package that is ready to run on compatible hardware. IT administrators can focus on the infrastructure that will run the containerized application, without considering software issues.\nThose are the upsides of containerization. But then there is a question about the potential for\u00a0 performance \u00a0penalties that come with containerized applications. In the IT world, there is a perception that abstraction can lead to degradation of performance.\nWe put this perception to the test in the Dell EMC HPC and AI Innovation Lab. And, to cut to the chase, our tests showed that software can be containerized with no significant performance penalties.\nThe tests\nFor our tests, we used the Dell EMC Ready Solution for AI \u2013 Deep Learning with Intel. This CPU-based scale-out solution provides a flexible platform for training a wide variety of neural network models with different capabilities and performance characteristics. The platform utilizes Nauta, an open source deep learning training platform built on cloud native technologies, such as Docker and Kubernetes. Nauta provides a simplified software environment that can be easily customized to suit whatever requirements the data scientist has.\nWe measured and analyzed the performance of this solution using three different deep learning training use cases:\n\nImage classification using convolutional neural networks\nLanguage translation using multi-head attention networks\nProduct recommendation using restricted Boltzmann machines\n\nWe chose the computational and workload diversity of these use cases to highlight the flexibility of the solution for applications across different customer segments and problem types. In all three use cases, our tests demonstrated near-linear scaling in performance up to the full size of the solution. We encountered no performance penalties.\nAdditional tests we performed on analogous hardware in the Dell EMC Zenith cluster in our lab showed that the solution can scale all tested use cases beyond 16 compute nodes. These tests confirmed that IT organizations can scale the solution as their compute requirements grow, without taking a performance hit.\nKey takeaways\nIn our lab tests, we demonstrated that \u2014 in addition to greater flexibility for the data scientist who is training models \u2014 the use of containers does not adversely affect the performance of the solutions we examined. In fact, we even found that, in some cases, organizations can expect better performance from containerized workloads on the solution than they could expect from the same hardware deployed in a bare metal configuration.\nSo, where do we go from here? Our successful tests suggest that we can explore the use of containers for other performance-critical use cases, such as high performance computing and financial transactions. And we can think more broadly about containers to answer questions like\n\nCan we run parallel applications inside containers? and\nCan we use containers to achieve simplified use and management across the computing spectrum?\n\nThese questions are worth pursuing as we go forward into a world where containers are sure to be used more broadly.\nTo learn more\n\nFor a fuller look at this story, see the white paper \u201cDell EMC Ready Solutions for AI \u2013 Deep Learning with Intel: Measuring performance and capability of deep learning use cases.\u201d\nFor a look at independent third-party findings, read the ESG technical validation of the Dell EMC Ready Solutions for AI \u2013 Deep Learning with Intel.\nFor a high-level look at Nauta software, see my blog \u201cSimple, Scalable, Containerized Deep Learning using Nauta.\u201d\n\nLucas Wilson, Ph.D., is an artificial intelligence researcher and lead data scientist in the HPC and AI Innovation Lab at Dell EMC.