To see what’s going on within the container, you can use the docker logs command. But before using that, we need to know the container’s ID or name to see the container’s logs. To get a list of containers running within the host, run the following command $ docker ps […]
Read More… from Troubleshooting containers – Containerization with Docker
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, […]
Read More… from Running your first container – Containerization with Docker
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 […]
Read More… from Introducing Docker storage drivers and volumes – Containerization with Docker
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 […]
Read More… from Installing Docker – Containerization with Docker
In the previous chapter, we talked about source code management with Git, where we took a crash course on Git and then discussed GitOps and how it shapes modern DevOps practices. In this chapter, we’ll get hands-on and explore Docker – the de facto container runtime. By the end of this chapter, you should be […]
Read More… from Technical requirements – Containerization with Docker
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 […]
Read More… from What is GitOps? – Source Code Management with Git and GitOps
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