A Dockerfile instruction is a capitalized word at the start of a line followed by its arguments. Each line in a Dockerfile can contain an instruction. Instructions are processed from top to bottom when an image is built. In this article, I’m assuming you are using a Unix-based Docker image. You can also use Windows-based images, but that’s a slower, less-pleasant, less-common process. So use Unix if you can. Let’s do a quick once-over of the dozen Dockerfile instructions we’ll explore.
In this article you’ll learn how to speed up your Docker build cycles and create lightweight images. One of Docker’s strengths is that it provides caching to help you more quickly iterate your image builds. When building an image, Docker steps through the instructions in your Dockerfile, executing each in order. As each instruction is examined, Docker looks for an existing intermediate image in its cache that it can reuse instead of creating a new (duplicate) intermediate image.
Better encoding of categorical data can mean better model performance. In this series, I’ll introduce you to a wide range of encoding options from the Category Encoders package for use with scikit-learn in Python. Use Category Encoders to improve model performance when you have nominal or ordinal data that may provide value. In this article we’ll discuss terms, general usage and five classic encoding options: Ordinal, One Hot, Binary, BaseN, and Hashing.
Cloud computing, containerization, and container orchestration are the most important trends in DevOps. Whether you’re a data scientist, software developer, or product manager, it’s good to know Docker and Kubernetes basics. Both technologies help you collaborate with others, deploy your projects, and increase your value to employers. In this article, we’ll cover essential Kubernetes concepts. T Kubernetes is an open-source platform for managing containerized apps in production. Kubernetes is referred to as K8s for short. here are a lot of Kubernetes terms.
Kubernetes is the premier technology for deploying and managing large apps. In this article, we’ll get up and running with K8s on your local machine. You will know how to set up K8s and run your first K8s app. Also, you will know how to inspect, create, and delete your K8 resources with common commands. Then you’ll deploy your first app. Finally, you’ll see the top K8s commands to know.
There are many different basic sorting algorithms. Some perform faster and use less memory than others. Some are better suited to big data and some work better if the data are arranged in certain ways. Choosing which library and type of sorting algorithm to use can be tricky. Implementations change quickly. In this article, I’ll give you the lay of the land, provide tips to help you remember the methods and share the results of a speed test.
Recall that a Docker image is made of a Dockerfile + any necessary dependencies. Also recall that a Docker container is a Docker image brought to life. To work with Docker commands, you first need to know whether you’re dealing with an image or a container. Once you know what you’re working with you can find the right command for the job. This article highlights the key commands for running vanilla Docker. Here are a few things to know about Docker commands.
There are many of ways to make your Docker containers safer. Securities is not set-it and forget it. It requires vigilance to keep your images and containers secure. Keeping Docker containers secure means AIMing for safety. Don’t forget to keep Docker, your languages and libraries, your images, and your host software updated. Finally, consider using Docker Enterprise if you’re running Docker as part of a team. If you’re serving files, or running apps in production, you need to be considerably more knowledgeable about Docker security.
Python is an interpreted, high-level, general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. In this article, you will learn how to add and configure Black, pytest, Travis CI, Coveralls, and PyUp. We’ve set the stage for more secure code with more consistent style. Here’s our ten-step plan for this article. This guide is for macOS with Python 3.7. Everything works as of early 2019, but things change fast.
Data engineers play a vital role for organizations by creating and maintaining pipelines and databases for injesting, transforming, and storing data. They are responsible for storing and making data usable by others. Data engineers set up pipelines to injest streaming and batch data from many sources. Eventually the data finds its way into dashboards, reports, and machine learning models. Which tech skills are most in demand for data engineers? How do they compare to the most in demand tech skills for data scientists? Read on to find out!