Docker logging and logging drivers Docker not only changed how applications are deployed but also the workflow for log management. Instead of writing logs to files, containers write logs to the console (stdout/stderr). Docker then uses a logging driver to export container logs to the specified destinations. Container log management Log management is an essential […]
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The best setting for a highly available NGINX container should be something like the following: $ docker run -d –name nginx –restart unless-stopped \ -p 80:80 –memory 1000M –memory-reservation 250M nginx:1.18.0 Let’s take a look at this in more detail: We will look into other flags in the subsequent sections as we get more hands-on. […]
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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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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 […]
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The environment repository stores the environment-specific configurations needed to run the application code. Therefore, they will typically have Infrastructure as Code (IaC) in the form of Terraform scripts, CaC in the form of Ansible playbooks, or Kubernetes manifests that typically help deploy the code we’ve built from the application repository. The environment repository should follow […]
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Structuring the Git repository To implement GitOps, we require at least two repositories: the application repository and the environment repository. This does not mean that you cannot combine the two, but for the sake of simplicity, let’s take a look at each of them separately. The application repository The application repository stores the application code. […]
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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