Hello Docker. Docker is a great tool to make deployment and testing systems while keeping things clean. The code for this project is here, the code for this tutorial is in the same repository.
Docker is a great tool to make deployment and testing systems while keeping things clean. The code for this project is here, the code for this tutorial is in the same repository.
So what is Docker? Docker itself is a platform, but we care about the Docker containers. So a Docker container is a lightweight execution environment for running code in a “sealed” environment. This means, its a fake computer that lives inside another computer that runs code without having to share configurations.
This means that we can run our backend, database, model and frontend as containers without having problems with configurations or dependencies. Because each service can run in a independent container.
To create a Docker container one must first create a Docker image, this is the configuration for our container. The great thing about Docker is that you can use images create by other people or companies to make things easier.
Let’s start by creating our backend container. In the folder where our backend code lives, we have to create a file named
Dockerfile this is the default name for a image. The code for this image is the following:
In this article, we are going to build a TensorFlow model (v2) and, using FastAPI, create REST API calls to predict from the model…
Following the second video about Docker basics, in this video, I explain Docker architecture and explain the different building blocks of the docker engine; docker client, API, Docker Daemon. I also explain what a docker registry is and I finish the video with a demo explaining and illustrating how to use Docker hub.
Tensorflow Releases New Package For Recommendation Systems: TFRS - Google has introduced TensorFlow Recommenders (TFRS), an open-source TensorFlow package that makes building, evaluating, and serving sophisticated recommender models easy.
In this two part series, we would be looking at how to build a movie recommendation model using TensorFlow.
This article is the second part of our exploration of Docker caching — if you haven’t already, check out the first part, where we introduced Docker layers and the caching mechanism. Let’s now have a look at Docker Compose and some of the challenges when using both Docker and Docker Compose at the same time. Sharing a Cached Layer Between Docker and Docker Compose Builds