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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

20+ Docker Commands for Building, Running, and Managing Containers

Pankaj Singh
Pankaj Singh Last Updated : 16 Apr, 2024
5 min read

Introduction

Docker is an open-source platform that gives developers everything they need to create, package, and deploy applications in a streamlined way. With Docker’s container technology, you can bundle your applications and all their dependencies into a single, self-contained unit that can easily be moved across different platforms and run seamlessly in containers. However, to make the most of what Docker offers, you must get comfortable with its command-line interface (CLI). In this article, we’ll walk you through the must-know Docker commands every developer and system administrator should have in their toolkit.

Docker Commands

Table of contents

Why Do You Need Docker Commands?

Docker commands are essential for managing and interacting with Docker containers and images. It includes creating, running, stopping, deleting containers, and creating images from Dockerfiles. In addition, it enables the ability to run tasks such as listing live containers, checking container status, transferring files between the host machine and containers, and managing Docker networks and Docker volumes. In use, it is impossible to achieve the desired state of utilizing Docker in containerizing applications, achieving portability and making it easy to deploy it across various platforms.

How to Use Docker Commands?

Here are some common ways to use Docker commands:

  1. Run a container: docker run [OPTIONS] IMAGE[:TAG|@DIGEST] [COMMAND] [ARG...] This command creates and starts a new container from the specified image.
  2. List running containers: docker ps Lists all currently running containers.
  3. Stop a container: docker stop CONTAINER_ID Stops the running container specified by its ID or name.
  4. Remove a container: docker rm CONTAINER_ID Removes the stopped container from the system.
  5. Pull an image: docker pull IMAGE[:TAG|@DIGEST] Downloads the specified image from a registry (e.g., Docker Hub).
  6. Build an image: docker build [OPTIONS] PATH | URL | - Builds a new image from the instructions in a Dockerfile.
  7. List images: docker images Lists all available images on the local system.
  8. Remove an image: docker rmi IMAGE[:TAG|@DIGEST] Removes the specified image from the local system.
  9. Run a command in a container: docker exec [OPTIONS] CONTAINER_ID COMMAND [ARG...] Runs the specified command within a running container.
  10. View logs: docker logs CONTAINER_ID Fetches the logs of the specified container.

These are just a few examples. Below, I have provided a list of docker commands. You can also explore more commands and their options by running docker --help or refer to the official Docker documentation.

Here is the list of Top Docker Commands

Docker Version

The docker version command displays the current version of Docker installed on your system. It provides information about the Docker client and server versions as well as various other details such as the operating system, architecture, and kernel version.

Usage

docker version

docker search 

The docker search command allows you to search for Docker images on Docker Hub, the official registry for Docker images. You can search for images by name or use keywords to find relevant images.

Usage

docker search <image_name>

docker pull 

The docker pull command downloads a Docker image from a registry (such as Docker Hub) to your local machine. You need to pull an image from it before creating a container.

Usage

docker pull <image_name>:<tag>

docker run

The docker run command is one of the most frequently used Docker commands. It creates a new container from a specified image and starts it. You can pass various options to customize the container’s behavior, such as exposing ports, mounting volumes, and setting environment variables.

Usage

docker run [OPTIONS] <image_name>:<tag> [COMMAND] [ARG...]

docker ps 

The docker ps command lists all the currently running containers on your system. By default, it shows only the running containers, but you can use the -a flag to list all containers (running and stopped).

Usage

docker ps
docker ps -a

docker stop

The docker stop command stops one or more running containers. You can specify the container by its name or ID.

Usage

docker stop <container_name_or_id>

docker restart 

The docker restart command restarts one or more running containers. It first stops the container(s) and then starts them again.

Usage

docker restart <container_name_or_id>

docker kill

The docker kill command forcibly stops a running container by sending a KILL signal. It should be used when the docker stop command fails to stop a container gracefully.

Usage

docker kill <container_name_or_id>

docker exec 

The docker exec command runs a new command inside a running container. This is useful for inspecting or troubleshooting containers without starting a new shell.

Usage

docker exec [OPTIONS] <container_name_or_id> [COMMAND] [ARG...]

docker login 

The docker login command authenticates you with a Docker registry, such as Docker Hub. You need to be authenticated to push images to a registry.

Usage

docker login [OPTIONS] [SERVER]

docker commit

The docker commit command creates a new image from a container’s changes. This is useful for capturing the state of a running container and creating a new image based on that state.

Usage

docker commit [OPTIONS] <container_name_or_id> [REPOSITORY[:TAG]]

docker push

The docker push command uploads an image to a Docker registry, such as Docker Hub. You need to be authenticated with the registry before pushing an image.

Usage

docker push <image_name>:<tag>

docker network

The docker network command manages Docker networks. It allows you to create, inspect, and manage networks for communication between containers.

Usage

docker network [COMMAND] [ARG...]

docker history

The docker history command shows the history of an image, including the layers that make up the image and the commands used to create each layer.

Usage

docker history <image_name>:<tag>

docker rmi

The docker rmi command removes one or more images from your local system. You need to stop and remove all containers based on the image before removing the image itself.

Usage

docker rmi <image_name>:<tag>

docker ps -a

The docker ps -a command lists all containers (running and stopped) on your system. It’s a useful command for getting an overview of all the containers on your machine.

Usage

docker ps -a

docker copy

The docker copy command copies files or directories between a container and the local filesystem.

Usage

docker copy [OPTIONS] <container_name_or_id>:<src_path> <dest_path>
docker copy [OPTIONS] <src_path> <container_name_or_id>:<dest_path>

docker logs

The docker logs command retrieves log output from a container. It’s an essential command for troubleshooting and debugging containers.

Usage

docker logs [OPTIONS] <container_name_or_id>

docker volume

The docker volume command manages Docker volumes. Volumes are used to persist data generated by Docker containers.

Usage

docker volume [COMMAND]

docker logout

The docker logout command logs out from a Docker registry.

Usage

docker logout [SERVER]

Now, you know just a few essential Docker commands, but Docker comes with many more commands and options that help you manage and work with containers. In the longer examples above, the Docker command-line interface offers a powerful and flexible method to interact with Docker containers and images. When pulling images from a registry, running containers, or managing networks and volumes, these Docker commands optimize your workflow and maximize the potential of container technology.

Also read: End-to-End Guide to Docker for aspiring Data Engineers

Bonus: Additional Commands

docker images

Lists all Docker images in your local repository.

  • Usage: docker images
  • Output: Displays image ID, repository name, tag, and size of each image.

docker rm

Removes one or more Docker containers.

  • Usage: docker rm [container_id or container_name]
  • Output: Deletes the specified container(s).

docker build

Builds a Docker image from a Dockerfile.

  • Usage: docker build [options] [path]
  • Options:
    • -t repository:tag to specify the repository and tag for the built image.
    • -f Dockerfile to specify a Dockerfile other than the default one in the build context.

Also read: Docker Tutorial: Step-by-Step Tutorial for Beginners

Conclusion

In conclusion, these top Docker commands are designed to help manage containers, images, networks, logs, and other resources such as volumes. Once you have learned how to use these commands, you can accomplish multiple tasks, including running containers, watching logs, managing images, and working with volumes. Try using these commands in your Docker projects to improve your work and get the most out of the Docker platform.

In the comment section, please let us know how useful these Docker commands are for you. We would love to hear from you.

Artificial Intelligencecommand linedata scienceDockerDocker Basic Commandsmachine learning
Pankaj Singh
Pankaj Singh

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.

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A Complete Guide on Docker for Beginners
A Complete Guide on Docker for Beginners
Detailed Insight on Docker for Beginners
Detailed Insight on Docker for Beginners

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