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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Docker Tutorial : Step-by-Step Tutorial for Beginners

Abhishek Jaiswal
Abhishek Jaiswal Last Updated : 29 Feb, 2024
6 min read

This article was published as a part of the Data Science Blogathon.

Introduction

In this article, we are going to introduce you to Docker tutorial and will further learn the basic commands as well from installing docker, Pulling images from the hub to running Linux machines in docker.

Docker tutorial

Let’s first understand the challenges we face while deploying an application or server and then how docker solved this problem

When we run and deploy any application or a machine learning model or application or a database or some third parties package they need some libraries and dependencies, Working on multiple applications requires different versions of dependencies can lead to a dependencies conflict.

Using separate machines or packaging all the dependencies with their application could solve this problem.

What is Docker?

Docker is a containerization and manager tool, Docker means Develop, Ship, and Run anywhere no matter what operating system we are using and the environment. Docker is a kind of isolated space where an application runs by using system resources.

Note: In this article, I have used the word Capsule for the word Container. Containers are often called Capsules.

why Docker| , Docker tutorial

Features of Docker

Docker is a full package solution for an app migration. It comes with the following features:

  • Containers are light and flexible compared to running an application on virtual machines. It doesn’t depend on the version of the Operating System
  • It can be easily deployed to the cloud server or local servers with ease.
  • easily scalable. Supports Scaling with ease.
Features of Docker, docker tutorial

Docker Components

Docker is made up of 3 components, these components are responsible for their own tasks from building, and running to creating capsules.

  • Docker Engine ( Used for building and creating docker containers).
  • Docker Hub ( This is a place where you can host your docker images).
  • Docker Compose ( It defines applications )

Docker Architecture

Docker Architecture consists of the Units that the docker is made up of these units are responsible for their own pre-defined tasks.

  • Docker CLI ( Command Line Interface)
  • Rest API ( connects docker daemon)
  • Docker Daemon ( Responsible for Objects, Images, Containers, Engines, etc.)
Docker Architecture, docker tutorial

After 2015, Docker redesigned itself according to standardization and ensured support for the Runtime Specification and Image Specification.

Docker Structure, docker tutorial
  • Runtime Specification defines the lifecycle of the capsule technology.
  • Earlier Docker Daemon was responsible for all the processes in docker but after the Standardization runc (Container Runtime) is responsible for spinning the Image. In Container orchestration tools like Kubernetes, we only need to install a container runtime to spin a container on its pod.
  • shim runc + shim makes docker daemon less.Shim monitors the running capsules.

Docker Setup

The complexity of docker installation depends on the operating system. Use this link for installing docker in your system.

For this article, I’ll be using docker on my Linux machine.

After installing verify if docker is installed by typing the code

$ docker --version
docker version

Managing Docker as a Service

Getting the Status of Docker-Engine if it’s up or not.

$ systemctl status docker
Output Terminal

As you see our docker engine is up and active. It can be easily stopped by the command

# stops the docker engine
$ systemctl stop docker
#Starts the docker engine
$ systemctl start docker

Starting the docker in debug mode if the docker service is facing errors.

$ sudo dockerd --debug

Docker CLI & Deploy Your first Container

A docker image contains all the dependencies, source files, and tools needed to run an application. A Docker image is a source file that creates a capsule.

A capsule is a running instance created by a docker image.

Docker Hello-world

Docker hello-world is an image file that makes a docker capsule and that prints hello world.

docker-hub.com, docker tutorial
docker run hello-world

It will first check the hello-world image file in its local registry. If not found, it pulls the image file from the docker hub ( default public registry) and runs the capsule.

Output Terminal Image 1

Printing All the Containers

List only already running capsules

docker ps

List down all the docker-capsules

docker ps -a
list all Capsules

The highlighted part with the yellow colour is the Capsule ID/Container ID, and whenever we want to select a specific capsule, we use its Container ID.

Note: We don’t need to write the whole container-ID, if the container-ID is unique we can only provide its first few starting characters.

docker rm 70

This command will remove the Capsules whose container ID starts with 70.

List Down All the Images

This lists all the docker images available in our local registry.

docker images
Image files list

Deleting an Image

If you want to delete an image from our local repository, we need to execute the following commands.

docker image rm ID_OF_CONTAINER
#---OR----
docker image rm Image_Name
Deleting an Image

Pulling Image from Docker Hub

Any Image file available on the docker hub or from other sources can be pulled in the local docker registry using the pull command.

The command docker pull only download the image file; it won’t make any capsule using the image file unless we ask docker to do that.

Pulling Image

Docker Inspect

The Docker inspect is a powerful command that lets us examine a container’s information, exposed ports, and network properties.

Inspect command can be used to inspect an image file or a capsule.

#Inspecting docker image
docker image inspect IMAGE_ID
Inspect Command
# Docker Container Inspect
docker container inspect ID_OF_CONTAINER
# --- OR----
docker inspect ID_OF_CONTAINER
docker inspect ID

Running Ubuntu in Docker

Running Ubuntu in Docker

To run a Linux as a docker capsule, we first need to pull the ubuntu image file.

docker pull ubuntu
docker images
docker images

Now we have an ubuntu image file in our local registry. It’s time to spin the capsule using the image file.

docker run -it ubuntu
Running Ubuntu in Interactive Mode
  • Here -it signifies that we want our capsule to run in interactive mode. It will wait for our response.
  • We can run the Linux commands now to check if our Linux capsule is up or not.
  • Ubuntu uses the command “/bin/bash” to enter the capsule. We can use the terminal of ubuntu in our spinning ubuntu capsule using the command “bin/bash”.
Ubuntu uses
  • docker exec This command runs a new command in already spinning capsules.
Execute command

Stopping the Running Capsules

We need its id to stop a Running capsule and run this command.

ˆdocker stop ID_OF_CONTAINER
Running Capsules| Docker

Conclusion

In this article, we learned some basics codes of docker to manage docker services and performed some Linux-based docker commands.

  • We discussed commands for managing capsules, docker-images, and the docker engine pulling Images from the hub to the local repository.
  • We don’t need to install the docker to run its capsule, and we only need docker runtime to run a deployment.
  • You can create Docker images from the project files
  • Linux natively supports docker.

Docker became daemon-less since a capsule no longer needs docker daemon to run. Docker provides storage layers where the capsules keep their files, and even if the tablet crashes, it won’t delete the stored files.

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Abhishek Jaiswal
Abhishek Jaiswal

A data enthusiast exploring the leading technologies related to the data

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