Python in Containers [Video]
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-
Free ChapterIntroduction
- Course Introduction and Agenda Overview
- The Concept of Linux Containers
- Containers Explained
- Build, Ship, Run
- Introduction to Docker
- Ecosystem of Container Technologies
- Introduction to Container Orchestration
- Python App in Docker Container
- Shipping the Image to Docker Hub
- Running our App in Various Environments
-
Docker Deep Dive
- Installing Docker for a Developer
- Create Docker ID
- Play with Docker
- Install Docker on Ubuntu
- Install Docker on CentOS
- Docker on Linux - Security Warning
- Docker Desktop on Windows Pro
- Introduction to Windows Containers
- Docker Desktop on MacOS
- Docker Toolbox for Windows Home
- Running Containers with Docker
- Integrating Containers with a Host System
- Container Images
- Managing Containers
- Running Multiple Containers
- Container Networking
- Data Persistency - Volumes
- Dockerfile Introduction
- Docker Hub Introduction
- Python Base Images
- Docker GUIs Part 1 - Kitematic
- Docker GUIs Part 2 - Portainer
- Docker Machine Overview
- Docker Machine with VirtualBox
- Docker Machine with Hyper-V
- Docker Machine on AWS Cloud Hosts
- Docker Machine on Google Cloud Hosts
-
Build Container Images
- Elements of Containerized Python Project
- Lifecycle of Containerized Python Project
- Design Principles for Containerized Python Apps
- Manual Image Build Process
- Dockerfile - Automation of Image Build
- Dockerfile Commands - Introduction and FROM
- Dockerfile Commands - WORKDIR, COPY, ADD
- Dockerfile Commands - RUN
- Dockerfile Commands - ENV, LABEL, USER
- Dockerfile Commands - VOLUME and EXPOSE
- Dockerfile Commands - ENTRYPOINT and CMD
- Parametrizing Dockerfiles with ARG
- Building and Running Reusable Images
- Build time versus Run time Execution
- Building smaller Images
- Multistage Image Build
- Building Custom Python Images
- Build Base Images from Scratch
- Dockerizing PyTest and Pdb - Simple Case
- Django Containerization for Development
- Django Containerization for Production
- Application Servers to Run Django and Flask
- Production--grade Database Engine - PostgreSQL
- Production--grade Database Engine - MariaDB
- Implementing Proxy Server
- The need of Automation
-
Ship Containers
-
Run Containers in Docker
- Running Production Containers in Docker
- Docker Compose - Introduction
- Docker Compose File - Version and Volumes
- Docker Compose File - Networks
- Docker Compose File - Services
- Managing Images with Docker Compose
- Application Lifecycle with Docker Compose - Part 1
- Application Lifecycle with Docker Compose - Part 2
- Introduction to Docker Swarm
- Provisioning Swarm with Docker Machine
- Standalone Containers in Swarm
- Services in Swarm
- Service Modes and Ingress Routing Mesh
- Application Stack in Swarm - Part 1
- Application Stack in Swarm - Part 2
- Application Environment in Swarm - Part 1
- Application Environment in Swarm - Part 2
- Application Lifecycle in Swarm
- Summary of Docker Runtime Environment
-
Run Containers in Kubernetes
- Introduction to Kubernetes
- Helicopter View of Kubernetes as the Application Platform
- Installing a Small Kubernetes Cluster
- Running Simple Application in Minikube
- Deployment of Multi-Container Application in Minikube - Part 1
- Deployment of Multi-Container Application in Minikube - Part 2
- Pod Controllers Part 1 - Introduction and ReplicaSet
- Pod Controllers Part 2- Deployment
- Pod Controllers Part 3 - StatefulSet, DaemonSet
- Pod Controllers Part4 - Job, CronJob
- Services
- Volumes
- Deploying a Multi-Container Application in Google Kubernetes Engine
- Application Environment in Kubernetes
-
Data Science & Machine Learning in Containers
- Section Introduction & Overview
- Containers in Research & Experimentation
- Machine Learning in Production
- Jupyter Notebook in Docker
- Run Python Code in Jupyter Container
- Data Science in Jupyter Container
- TensorFlow in Containers
- MNIST Classification Models in Tensorflow Container
- Tensorflow Serving - Prediction Example
- Object Detection in TensorFlow Container
- NVIDIA GPU and Docker
About this
video
Docker and Kubernetes are must-have skills for Python engineers these days. Whether your focus is on machine learning and data science or you use Python as a general programming language, you must understand Docker and Kubernetes, as they form the basis of modern cloud-native applications built using microservice architectures.
In this course, you’ll learn to do the following:
• Develop and explore machine learning, data science, and Jupyter Notebooks in Docker
• Run machine learning models in production with Kubernetes and Docker Swarm
• Package your Python code into containers
• Publish your containers in image registries
• Deploy containers to production, both in Docker and Kubernetes
• Build highly modular, container-based services in a microservices way
• Monitor and maintain containerized apps
You can use the course in two ways:
• If you use Python for machine learning and data science, go top-down - start with section 7 to quickly develop practical Docker skills and use sections 2 to 6 to delve deeper into specific container topics
• If you want to use Python for building web apps and microservices, try the bottom-up approach - use the course in a linear way
All the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Python-in-Containers
- Publication date:
- March 2020
- Publisher
- Packt
- Duration
- 24 hours 0 minutes
- ISBN
- 9781800200753