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. […]
Read More… from Putting it all together – Containerization with Docker
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
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
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 […]
Read More… from The environment repository – 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