![]() ![]() These two terms have been used indistinguishably. What is Docker?ĭocker has become so incredibly popular over the past years that it essentially became synonymous with containers. “It works on my machine, I don’t understand why it doesn’t work here.”Ĭontainers also run on top of the operating system’s kernel and they don't “replicate” the Operating system as Virtual machines do, and therefore are far more lightweight and portable. This simply means that we can run the exact same software in any environment regardless of the operating system and hardware.Īs you might have guessed, it immediately solves problems such as missing dependencies, enables easy collaboration between developers, and provides isolation from other applications.Īnd of course, it eliminate a statements that origins from the beginning of time: What is a Container?Ī container is a standard unit of software that packages the code and all its dependencies so that the application runs quickly and reliably from one computing environment to another. Remember that our ultimate goal is to deploy our model into the cloud and scale it to millions of users. ![]() If all that sounds interesting to you, hope in. I’m sure that some don’t entirely understand what this means, so I hope to make it crystal clear through this article. For those who haven't followed this article series, I suggest having a look at the previous articles.įor this purpose,we will build a Docker image that packages our Deep Learning/Flask code, an image for Nginx and we will combine them using Docker Compose. Our application consists of a Tensorflow model that performs image segmentation, Flask, uWSGI for serving purposes, and Nginx for load balancing. In this article, we will containerize our Deep Learning application using Docker. Besides, they eliminate discrepancies between the development and the production environment, they are lightweight compared to virtual machines, and they can easily be scaled up or down. This is especially true in machine learning and for a very good reason! Containers provide flexibility to experiment with different frameworks, versions, GPU's with minimal overhead. Containers have become the standard way to develop and deploy applications these days with Docker and Kubernetes dominating the field.
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