Python in Containers

Explore how to install and configure Docker across Linux, Windows, and macOS to build and run Linux containers for Python, including Docker tools, orchestration options, and Docker Hub images.

Explore docker basics hands-on via play with docker labs; learn to create a multi-node environment, run a flask app in a docker container, build image simple-flask:v1.0.5, and expose port 5000.

Explore security implications of running Docker on Linux, including root access via the Docker daemon and socket, group permissions, and the need for isolated Docker Machine environments in shared environments.

Discover how ephemeral container filesystems disappear after restart, and achieve data persistence with volumes and bind mounts. Learn to create, mount, and inspect volumes and host directories for lasting data.

Ship container images to Docker Hub by logging in, tagging, and pushing, while exploring repositories, tags, private vs public plans, collaborators, official images, and vulnerability scanning insights.

Create and manage docker machines on Google Cloud Engine, configure credentials via gcloud or credentials.json, and open ports with firewall rules to run python in containers.

Explore the RUN command in Dockerfiles, its shell and JSON array forms, how it runs in intermediate containers, and how environment variables, PATH, and caching influence image builds and layering.

Modify image metadata with ENV, USER, and LABEL in Dockerfiles, manage environment variables and PATH, and run commands as different users, with runtime overrides via --env.

Master how ENTRYPOINT and CMD define a container's start-up command, how the exec and shell forms operate, and how overrides with docker run and signals shape behavior.

Explore parameterizing dockerfiles with arg and build-arg. Convert build-time args to envs for runtime configuration and label images to track base image and library variants.

Learn how to build custom Python images for dev and prod stages, using multi-stage dockerfiles to preinstall Cython and Flask components, shortening build time while preserving performance.

deploy a production-grade MariaDB database for the Django Pulse app by building a MariaDB container, configuring environment variables, performing migrations, and using a universal image with DJ database URL.

Ship container images efficiently by exporting, importing, saving, and loading tar archives, or using image registries like Docker Hub with push and pull for automated deployment.

Explore how GitLab enables containerized Python projects with a multi-stage docker build, pytest tests, and image security scanning, all through a single ci/cd pipeline and integrated image registry.

Learn how to declare version and volumes in a Docker Compose file using yaml, focusing on version 3.7, networks and external volumes, and sharing volumes across services.

Learn how Docker Compose shifts from imperative deployment to a declarative model, manage a three-service Django app (db, app, proxy), upgrade Postgres, and initialize migrations with the run subcommand.

Discover Docker Swarm fundamentals for multi-host deployments, including manager and worker nodes, overlay networks, swarm services, global and replicated deployments, port publishing, and data replication for resilience.

Navigate Docker Swarm as a programmatic platform for running microservice components as containerized services. Discover how services, tasks, replicas, deployment modes, placement constraints, networks, and service endpoints enable scalable architectures.

Docker enables container image creation and registry delivery to ship Python applications from development to production, using runtime and orchestration tools, compose for deployments, and swarm stacks for scalable clusters.

Launch a minikube dashboard, deploy a Flask app with kubectl run, then expose it via a replica set, service, and ingress using yaml configurations.

Deploy a multi-container Django Pulse app on minikube, wiring components with postgres, nginx, and services through an ingress while using DNS, replica sets, and secrets for stable microservice communication.

Explore Kubernetes job and cronjob controllers, using a config map to initialize a Django app database, run migrations, and perform scheduled backups, with backoff and logging practices.

Learn how Kubernetes services provide stable interfaces to dynamic pods by using cluster IPs, DNS, and label selectors, including nodeport, headless, external name, and load balancer types.

Launch docker containers to isolate data science projects with images like scipy, numpy, and pandas. Access notebooks via jupyter images with bind mounts and port mappings while noting docker limitations.

Explore building a custom Jupyter image with pandas and plotly to analyze world development indicators via the World Bank RESTful API, using docker and docker compose.

Package a trained classification model into a container and deploy a self-contained microservice that serves predictions via a REST API using a TensorFlow Serving image and Docker Swarm.