Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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

Check Out Our Exclusive Docker Cheat Sheet!

Yana Khare
Yana Khare Last Updated : 03 Apr, 2024
6 min read

Introduction

Docker is an open-source platform that makes using containers to build, ship, and operate applications easier. With the use of containers, developers may bundle dependencies and apps to make them uniform and portable across many contexts. A short reference guide to the key Docker Commands and ideas for using Docker efficiently is provided by this cheat sheet.

Table of Contents

What is Docker?

Docker cheat sheet

Docker is a containerization platform enabling developers to create, deploy, and manage applications within isolated containers. These containers package the application code, runtime, libraries, and dependencies, ensuring consistency and efficiency across various systems.

Learn More: A Complete Guide on Docker for Beginners

How Docker Works

Docker is a platform that lets you use containers to develop, launch, and operate applications. However, what are containers precisely, and how does Docker CLI operate? Let’s dissect it.

Containers: Lightweight and Portable

The code, runtime environment, system tools, and libraries required to run an application are all included in a container, which is a small, stand-alone package. An independent operating system is not needed for containers, in contrast to virtual machines (VMs). Instead, they share the host machine’s operating system kernel, making them more lightweight and efficient.

The Docker Engine

Docker Compose uses a client-server architecture, with the Docker CLI Engine at the core. The Docker Engine consists of three main components:

  • Docker Daemon: This is the server component that manages containers, images, networks, and volumes.
  • Docker Client: The client communicates with the Docker Daemon to execute Docker Commands and manage objects.
  • Docker Objects: These include images, containers, networks, and volumes, which are the building blocks of Docker Compose applications.

The Docker Workflow

Here’s a typical workflow when working with Docker CLI:

  • Make a Dockerfile: A text file called a Dockerfile has instructions on how to construct a Docker image.
  • Create an Image: You can create a read-only Docker image that has all the components required to run your application by using the Dockerfile.
  • Run a Container: Following the creation of an image, one or more containers based on that image can be launched.
  • Distribute and Deploy: Docker containers are lightweight and easily distributable to various settings, including production, testing, and development.

Benefits of Docker

Docker Compose offers several benefits, including:

  • Consistency: When an application operates consistently in many circumstances, the “it works on my machine” issue is resolved.
  • Isolation: To improve security and resource management, containers are isolated from the host system and from one another.
  • Portability: Containers are easily transportable between various environments, like a production server and a developer’s workstation.
  • Scalability: Due to their lightweight design, Docker containers are simple to scale up or down in accordance with resource requirements.

By understanding how Docker works and leveraging its capabilities, developers can streamline their application development, deployment, and maintenance processes.

Also Read: Top 28 Cheat Sheets for Machine Learning, Data Science, Probability, SQL & Big Data

Docker Architecture

Docker Compose follows a client-server architecture:

  • Docker Client: The command-line tool that allows users to interact with the Docker daemon.
  • Docker Daemon: The background service responsible for building, running, and managing Docker containers.
  • Docker Images: Read-only templates used to create containers.
  • Docker Containers: Running instances of Docker images.
  • Docker Registry: A repository for storing and distributing Docker images.

Installation of Docker

Installation of Docker | Docker cheat sheet

To install Docker on your system, follow the appropriate instructions for your operating system. Below are the steps for Windows and MacOS.

Windows

Check system requirements

Windows 10 64-bit: Pro, Enterprise, or Education editions

Hardware Virtualization enabled in BIOS/UEFI (Intel VT-x/AMD-V)

Download Docker Desktop

Go to Docker’s official website and download the Docker Desktop installer for Windows.

Install Docker Desktop

Run the installer you downloaded and follow the installation wizard. It will guide you through the process.

Start Docker

Once installed, Docker Desktop should start automatically. You’ll see the Docker icon in the system tray when it’s running.

MacOS

Check system requirements

macOS Sierra 10.12 or newer

macOS must be a 2010 or newer model, with Intel’s hardware support for Memory Management Unit (MMU) virtualization.

Download Docker Desktop for Mac

Visit Docker’s official website and download the Docker Desktop installer for macOS.

Install Docker Desktop

Open the downloaded .dmg file, and drag the Docker icon into the Applications folder.

Start Docker

Open Docker from the Applications folder. It will appear in the menu bar, indicating that it’s running.

Also Read: Cheatsheet – Python & R codes for common Machine Learning Algorithms

Docker Registry and Repository

Docker Registry is a service that stores and manages Docker images. It acts as a central repository where users can store, share, and pull Docker images. Docker Hub is a public Docker Registry, while private registries can be set up for secure internal image storage. 

A Docker Repository is a collection of related images with the same name, differentiated by tags representing different versions or configurations. Users can push images to a repository in a registry, and others can pull those images to run containers. Registries and repositories are crucial in simplifying image distribution and facilitating collaborative development in the Docker ecosystem.

Below are the docker cheat sheet, Docker Commands for various functions-

Running Containers

To run a container from an image, use the following command: docker run [options] IMAGE [command]

For example, to run an Nginx web server: docker run -d -p 80:80 nginx

Starting or Stopping the Container

  • To start a stopped container, use: docker start CONTAINER_ID
  • To stop a running container, use: docker stop CONTAINER_ID

Obtaining Container Information

  • To list all running containers, use: docker ps
  • To view all containers (including stopped ones), use: docker ps -a

Managing Images

  • To pull an image from a registry, use: docker pull IMAGE_NAME[:TAG]
  • To build an image from a Dockerfile, navigate to the Dockerfile directory and use: docker build -t IMAGE_NAME[:TAG].

Networking

  • To create a user-defined bridge network, use: docker network create NETWORK_NAME
  • To connect a container to a network, use: docker network connect NETWORK_NAME CONTAINER_NAME

Cleaning Docker

  • To remove a stopped container, use: docker rm CONTAINER_ID
  • To remove an image, use: docker rmi IMAGE_NAME
  • To clean up unused resources (containers, networks, images, and volumes), use: docker system prune

Orchestrate

Docker Compose is a tool for defining and managing multi-container Docker applications. It configures the application’s services, networks, and volumes using a YAML file. With Docker Compose, you can easily spin up complex environments and applications. Here are the commands for it Docker Compose cheat sheet-

  • To create and start all containers defined in the docker-compose.yml file use: docker-compose up
  • To create and start containers in detached mode use: docker-compose up -d
  • To stop and remove all containers, networks, and volumes created by docker-compose up use: docker-compose down
  • To list all running containers defined in the docker-compose.yml file use: docker-compose ps
  • To view the logs of a specific service use: docker-compose logs [service_name]
  • To build or rebuild the images defined in the docker-compose.yml file use: docker-compose build
  • To execute a command inside a specific service container use: docker-compose exec [service_name] [command]

Services

Services in Docker Swarm mode define the tasks on a cluster. They allow you to scale the application across multiple nodes and ensure high availability. Here are the Docker Commands for it: 

  • To initialize a Docker Swarm on the current node, use: docker swarm init
  • To join a Docker Swarm as a worker node use the following: docker swarm join
  • To list nodes in the Docker Swarm, use: docker node ls
  • To create a new service use the following: docker service create
  • To list all services running in the Swarm, use: docker service ls
  • To list tasks (containers) of a service use: docker service ps [service_name]
  • To scale a service to N replicas, use: docker service scale [service_name]=N
  • To update a service (e.g., change replicas, image version), use: docker service update
  • To remove a service from the Swarm, use: docker service rm [service_name]
  • To set a node to “drain” mode, meaning it will not receive new tasks use: docker node update –availability drain [node_name]

Interaction with a Container

To execute Docker Commands inside a running container, use: docker exec [options] CONTAINER_ID COMMAND [ARG…]

For example, to access the shell of a running container: docker exec -it CONTAINER_ID sh

Important Terms

  • Image: A lightweight, standalone, and executable software package that includes everything needed to run a piece of software, including the code, runtime, libraries, and dependencies.
  • Container: An instance of a Docker image that can be run, started, stopped, and deleted.
  • Dockerfile: A text file that contains instructions for building a Docker image.
  • Registry: A storage and content delivery system for named Docker images.
  • Repository: A collection of Docker images with the same name, distinguished by tags.
  • Docker Compose: A tool for defining and managing multi-container Docker applications.
  • Docker Swarm: A native clustering and orchestration solution for Docker.

Conclusion

In conclusion, Docker is an essential containerization platform that simplifies the process of building, shipping, and running applications by encapsulating them and their dependencies in isolated containers. With this comprehensive cheat sheet, users can efficiently harness the power of Docker, streamline application deployment, and maintain consistency across diverse environments.

Frequently Asked Questions?

Q1. What is Docker and its role in application development?

A. Docker simplifies application development, deployment, and management through containerization, ensuring consistency across environments.

Q2. How do containers and Docker enhance software deployment?

A. Containers, like those in Docker, package applications and dependencies, improving deployment efficiency.

Q3. What are the main components of Docker’s architecture?

A. Docker Engine consists of the Daemon, Client, and Docker Objects, essential for managing containers and images.

Q4. What benefits does Docker provide to developers and operations?

A. Docker offers consistency, isolation, portability, and scalability, benefiting both developers and operations teams.

Q5. Can you outline the typical Docker workflow?

A. The Docker workflow involves creating a Dockerfile, building images, launching containers, and distributing them across various environments.

Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details