Python in Containers
4.6 (93 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
853 students enrolled

Python in Containers

All about Containers, Docker and Kubernetes for Python Engineers
4.6 (93 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
853 students enrolled
Created by Kris Celmer
Last updated 10/2019
English
English [Auto-generated]
Current price: $69.99 Original price: $99.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 24 hours on-demand video
  • 98 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Build Container Image with Python Application in it
  • Ship Container Images to Docker Hub and other Container Image Registries
  • Run Jupyter Notebooks in Docker
  • Use Docker Desktop for Windows Pro and MacOS
  • Use Docker Toolbox for Windows Home
  • Use Docker Machine to create Virtual Machines with Docker Software
  • Master Dockerfile to Automate Container Image Build
  • Create Custom Container Images from Scratch
  • Use Python Official Images
  • Design Flask and Django Multi-Container Deployments
  • Automate Multi-Container Deployments with Docker Compose
  • Containerize TensorFlow Models into Microservices
  • Deploy Complex, Multi-Container Applications in Docker Swarm
  • Deploy Complex, Multi-Container Application in Kubernetes
  • Use Kubernetes with Minikube on a Development Host
  • Use Kubernetes in Public Cloud (using example of Google Kubernetes Engine)
  • Kubernetes Objects: Pods, Pod Controllers: ReplicaSet, Deployment, Job, CronJob, Services, Ingress, Persistent Volumes
  • Writing Kubernetes Object Template Files
  • Monitor and Manage Application in Kubernetes
  • Execute Containers with NVIDIA GPU Acceleration
Course content
Expand all 114 lectures 23:49:07
+ Docker Deep Dive
27 lectures 04:53:19
Installing Docker for a Developer
04:41
Create Docker ID
01:33
Play with Docker
07:33
Install Docker on Ubuntu
09:16
Install Docker on CentOS
08:58
Docker on Linux - Security Warning
05:44
Docker Desktop on Windows Pro
13:38
Introduction to Windows Containers
03:52
Docker Desktop on MacOS
13:17
Docker Toolbox for Windows Home
07:24
Running Containers with Docker
10:37
Integrating Containers with a Host System
15:50
Container Images
13:40
Managing Containers
14:06
Running Multiple Containers
15:04
Container Networking
14:03
Data Persistency - Volumes
08:40
Dockerfile Introduction
10:17
Docker Hub Introduction
10:32
Python Base Images
07:54
Docker GUIs Part 1 - Kitematic
07:14
Docker GUIs Part 2 - Portainer
27:54
Docker Machine Overview
02:55
Docker Machine with VirtualBox
20:25
Docker Machine with Hyper-V
07:33
Docker Machine on AWS Cloud Hosts
19:23
Docker Machine on Google Cloud Hosts
11:16
+ Build Container Images
26 lectures 05:27:38

In this Lecture we look at all Components necessary to build a Container with Python Application.

Elements of Containerized Python Project
07:36

In this Lecture we review a typical workflow of actions in lifecycle of Container with Python Application.

Lifecycle of Containerized Python Project
17:38

Key Design Principles of Containerized Python Application are reviewed in this Lecture.

Design Principles for Containerized Python Apps
20:46
Manual Image Build Process
09:25
Dockerfile - Automation of Image Build
13:39
Dockerfile Commands - Introduction and FROM
09:10
Dockerfile Commands - WORKDIR, COPY, ADD
11:14
Dockerfile Commands - RUN
12:44
Dockerfile Commands - ENV, LABEL, USER
08:26
Dockerfile Commands - VOLUME and EXPOSE
13:10
Dockerfile Commands - ENTRYPOINT and CMD
21:47
Parametrizing Dockerfiles with ARG
09:22
Building and Running Reusable Images
09:12
Build time versus Run time Execution
18:10
Building smaller Images
11:59
Multistage Image Build
14:14
Building Custom Python Images
08:25
Build Base Images from Scratch
17:57
Dockerizing PyTest and Pdb - Simple Case
12:46
Django Containerization for Development
08:11
Django Containerization for Production
15:20
Application Servers to Run Django and Flask
08:28
Production--grade Database Engine - PostgreSQL
19:07
Production--grade Database Engine - MariaDB
07:57
Implementing Proxy Server
15:41
The need of Automation
05:14
+ Ship Containers
7 lectures 01:46:03
Shipping Images
13:12
Image Registries and Repositories
21:20
Review of Key Cloud Registries
09:05
Review of Local Registry Technologies
19:47
GitHub and Docker Hub Integration
19:35
GitLab Container Image Build Workflow
11:07
Vulnerability Scanning of Images
11:57
+ Run Containers in Docker
19 lectures 04:27:07
Running Production Containers in Docker
06:45
Docker Compose - Introduction
15:54
Docker Compose File - Version and Volumes
11:18
Docker Compose File - Networks
10:45
Docker Compose File - Services
23:20
Managing Images with Docker Compose
14:48
Application Lifecycle with Docker Compose - Part 1
10:59
Application Lifecycle with Docker Compose - Part 2
17:48
Introduction to Docker Swarm
14:00
Provisioning Swarm with Docker Machine
05:56
Standalone Containers in Swarm
11:42
Services in Swarm
18:56
Service Modes and Ingress Routing Mesh
16:39
Application Stack in Swarm - Part 1
17:40
Application Stack in Swarm - Part 2
06:33
Application Environment in Swarm - Part 1
21:35
Application Environment in Swarm - Part 2
20:30
Application Lifecycle in Swarm
18:49
Summary of Docker Runtime Environment
03:10
+ Run Containers in Kubernetes
14 lectures 04:12:36
Introduction to Kubernetes
10:04
Helicopter View of Kubernetes as the Application Platform
17:34
Installing a Small Kubernetes Cluster
10:03
Running Simple Application in Minikube
23:08
Deployment of Multi-Container Application in Minikube - Part 1
14:56
Deployment of Multi-Container Application in Minikube - Part 2
20:33
Pod Controllers Part 1 - Introduction and ReplicaSet
19:22
Pod Controllers Part 2- Deployment
19:31
Pod Controllers Part 3 - StatefulSet, DaemonSet
14:05
Pod Controllers Part4 - Job, CronJob
16:47
Services
17:19
Volumes
24:14
Deploying a Multi-Container Application in Google Kubernetes Engine
24:35
Application Environment in Kubernetes
20:25
+ Data Science & Machine Learning in Containers
11 lectures 01:24:26
Section Introduction & Overview
04:07
Containers in Research & Experimentation
10:50
Machine Learning in Production
09:05
Jupyter Notebook in Docker
08:52
Run Python Code in Jupyter Container
08:04
Data Science in Jupyter Container
06:35
TensorFlow in Containers
07:15
MNIST Classification Models in Tensorflow Container
04:27
Tensorflow Serving - Prediction Example
10:23
Object Detection in TensorFlow Container
07:07
NVIDIA GPU & Docker
07:41
Requirements
  • Basic Python Programming Skills
  • Basic understanding of Linux is a plus
Description

Docker and Kubernetes are the Must-Have Skills for Python Enginner these days.

Whether your focus is in Machine Learning & Data Science, or you use Python as General Programming Language, you must understand Docker & Kubernetes. Both form a basis of Modern Cloud Native Applications built in Microservices Architecture.

Quotes from selected course reviews:

  • "It covers pretty much everything you'd expect from enterprise project" Abbi1680@gmail.com

  • "This course is absolute gold for data science and machine learning people because all Docker and Kubernetes courses out there focus on nothing but web applications. Thanks to the instructor for handling the concept of virtualization from a much needed different perspective. There are a lot of sources for learning ML and DS but skills taught in this course are what will make you stand out from the crowd." Mertkan Alacahan

  • "Spot on. Great depth yet very concise." Toby Patterson

  • "This is a deep deep deep dive in Docker with python. It is the complete course. Thanks for putting this together it is more than enough for what a need. I think watching the basic lectures and some selected topics I get what I needed and this became my docker reference guide if I need to solve a specific scenario. Thanks for putting this together. Highly recommend the course if you are a python developer." Pedro

In this Course you learn how to:


  • Develop and Explore Machine Learning & Data Science 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 in Production

  • build highly modular Container-based Services in Micro-Services fashion

  • monitor and maintain Containerized Apps


You are going to become fluent and confident in using Docker Tools to create top-class Containers running your Python Code. You master Docker Runtime Tools like Compose and Swarm to run them. The Course also gives you sound knowledge and deep understanding of Kubernetes as the Application Platform. You gain confidence in Designing your Application to run on Kubernetes, as well as get deep knowledge of writing Kubernetes Object Declarations.

The Course is full of practical Exercises. There are over 40 GitHub Repositories full of Code Samples for the Course.

You can use the Course in two ways:

  1. If you use Python for Machine Learning & Data Science, go Top-Down: start with Section 7 to quickly gain practical Docker skills and use Sections 2 to 6 to dig deeper into specific Container Topics.

  2. If you want to use Python for Web Apps & Microservices, try Bottom-Up: use the Course in linear manner.

Start building Containers today!


Who this course is for:
  • Python Programmers
  • Data Scientists
  • Machine Learning Engineers