Python in Containers

Python in Containers
Python in Containers

English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 23h 48m | 6.96 GB
eLearning | Skill level: All Levels


All about containers, Docker, and Kubernetes for Python engineers.

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

Learn

  • Build container images having Python applications and ship them to Docker Hub
  • Run Jupyter Notebooks and create virtual machines in Docker
  • Use Docker Desktop for Windows Pro and macOS and Docker Toolbox for Windows Home
  • Create custom container images from scratch and automate container image builds with Dockerfile
  • Design Flask and Django multi-container deployments and automate them with Docker Compose
  • Containerize TensorFlow models into microservices and use Kubernetes with Minikube on a development host
  • Deploy complex multi-container applications in Docker Swarm and Kubernetes
+ Table of Contents

Introduction
1 Course Introduction and Agenda Overview
2 The Concept of Linux Containers
3 Containers Explained
4 Build, Ship, Run
5 Introduction to Docker
6 Ecosystem of Container Technologies
7 Introduction to Container Orchestration
8 Python App in Docker Container
9 Shipping the Image to Docker Hub
10 Running our App in Various Environments

Docker Deep Dive
11 Installing Docker for a Developer
12 Create Docker ID
13 Play with Docker
14 Install Docker on Ubuntu
15 Install Docker on CentOS
16 Docker on Linux – Security Warning
17 Docker Desktop on Windows Pro
18 Introduction to Windows Containers
19 Docker Desktop on MacOS
20 Docker Toolbox for Windows Home
21 Running Containers with Docker
22 Integrating Containers with a Host System
23 Container Images
24 Managing Containers
25 Running Multiple Containers
26 Container Networking
27 Data Persistency – Volumes
28 Dockerfile Introduction
29 Docker Hub Introduction
30 Python Base Images
31 Docker GUIs Part 1 – Kitematic
32 Docker GUIs Part 2 – Portainer
33 Docker Machine Overview
34 Docker Machine with VirtualBox
35 Docker Machine with Hyper-V
36 Docker Machine on AWS Cloud Hosts
37 Docker Machine on Google Cloud Hosts

Build Container Images
38 Elements of Containerized Python Project
39 Lifecycle of Containerized Python Project
40 Design Principles for Containerized Python Apps
41 Manual Image Build Process
42 Dockerfile – Automation of Image Build
43 Dockerfile Commands – Introduction and FROM
44 Dockerfile Commands – WORKDIR, COPY, ADD
45 Dockerfile Commands – RUN
46 Dockerfile Commands – ENV, LABEL, USER
47 Dockerfile Commands – VOLUME and EXPOSE
48 Dockerfile Commands – ENTRYPOINT and CMD
49 Parametrizing Dockerfiles with ARG
50 Building and Running Reusable Images
51 Build time versus Run time Execution
52 Building smaller Images
53 Multistage Image Build
54 Building Custom Python Images
55 Build Base Images from Scratch
56 Dockerizing PyTest and Pdb – Simple Case
57 Django Containerization for Development
58 Django Containerization for Production
59 Application Servers to Run Django and Flask
60 Production–grade Database Engine – PostgreSQL
61 Production–grade Database Engine – MariaDB
62 Implementing Proxy Server
63 The need of Automation

Ship Containers
64 Shipping Images
65 Image Registries and Repositories
66 Review of Key Cloud Registries
67 Review of Local Registry Technologies
68 GitHub and Docker Hub Integration
69 GitLab Container Image Build Workflow
70 Vulnerability Scanning of Images

Run Containers in Docker
71 Running Production Containers in Docker
72 Docker Compose – Introduction
73 Docker Compose File – Version and Volumes
74 Docker Compose File – Networks
75 Docker Compose File – Services
76 Managing Images with Docker Compose
77 Application Lifecycle with Docker Compose – Part 1
78 Application Lifecycle with Docker Compose – Part 2
79 Introduction to Docker Swarm
80 Provisioning Swarm with Docker Machine
81 Standalone Containers in Swarm
82 Services in Swarm
83 Service Modes and Ingress Routing Mesh
84 Application Stack in Swarm – Part 1
85 Application Stack in Swarm – Part 2
86 Application Environment in Swarm – Part 1
87 Application Environment in Swarm – Part 2
88 Application Lifecycle in Swarm
89 Summary of Docker Runtime Environment

Run Containers in Kubernetes
90 Services
91 Volumes
92 Deploying a Multi-Container Application in Google Kubernetes Engine
93 Application Environment in Kubernetes
94 Introduction to Kubernetes
95 Helicopter View of Kubernetes as the Application Platform
96 Installing a Small Kubernetes Cluster
97 Running Simple Application in Minikube
98 Deployment of Multi-Container Application in Minikube – Part 1
99 Deployment of Multi-Container Application in Minikube – Part 2
100 Pod Controllers Part 1 – Introduction and ReplicaSet
101 Pod Controllers Part 2- Deployment
102 Pod Controllers Part 3 – StatefulSet, DaemonSet
103 Pod Controllers Part4 – Job, CronJob

Data Science & Machine Learning in Containers
104 Section Introduction & Overview
105 Containers in Research & Experimentation
106 Machine Learning in Production
107 Jupyter Notebook in Docker
108 Run Python Code in Jupyter Container
109 Data Science in Jupyter Container
110 TensorFlow in Containers
111 MNIST Classification Models in Tensorflow Container
112 Tensorflow Serving – Prediction Example
113 Object Detection in TensorFlow Container
114 NVIDIA GPU and Docker