You can create Docker containers out of Docker container images. While we will discuss container images and their architecture in the following chapters, an excellent way to visualize them is as a copy of all files, application libraries, and dependencies comprising your application environment, similar to a virtual machine image. To run a Docker container, […]
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For this discussion, we will configure overlay2 as the storage driver. Although it is configured by default, and you can skip the steps if you are following this book, it is worth a read in case you want to change it to something else. First, let’s list the existing storage driver: $ docker info | […]
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Docker containers are ephemeral workloads. Whatever data you store on your container filesystem gets wiped out once the container is gone. The data lives on a disk during the container’s life cycle but does not persist beyond it. Pragmatically speaking, most applications in the real world are stateful. They need to store data beyond the […]
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We will be installing Docker in an Ubuntu system. For other OSs, please refer to https://docs. docker.com/engine/install/. To install Docker, we need to install supporting tools to allow the apt package manager to download Docker through HTTPS. Let’s do so using the following commands: $ sudo apt-get update $ sudo apt-get install -y ca-certificates curl […]
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GitOps has the following key principles: Implementing and living by these principles is simple with modern DevOps tools and techniques, and we will look at practically implementing them later in Chapters 11 and 12. In this chapter, however, we’ll examine their design principles using a branching strategy and GitOps workflow. […]
Read More… from The principles of GitOps – Source Code Management with Git and GitOps
GitOps is a method that involves implementing DevOps so that Git forms the single source of truth. Instead of maintaining a long list of scripts and tooling to support this, GitOps focuses on writing declarative code for everything, including the infrastructure, configuration, and application code. This means you can spin anything out of thin air […]
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Step 5: Train the model: This code initiates the training of the neural network model using the training data (X_train and y_train) for 20 epochs, with a batch size of 32. The validation data (X_test and y_test) is used to evaluate the model’s performance during training: Train the modelmodel.fit(X_train, y_train, epochs=20, batch_size=32, \ validation_data=(X_test, y_test)) Step 6: […]
Read More… from Hands-on – labeling audio data using a CNN – Labeling Audio Data-3