No more Python env and package update
Table of ContentsIntroduction 1. Installing Docker Desktop
2. Docker help
3. Running Jupyter Docker Stacks
4. Formatting Docker ps
5. Entering the Docker container and using bash
6. Stopping and removing containers and images
7. Connecting the local directory to a Docker container
8. Inspecting a container
9. Getting started with Docker file
10. Publishing an image to Docker HubConclusion
Docker simplifies and accelerates your workflow, while giving developers the freedom to innovate with their choice of tools, application stacks, and deployment environments for each project. — from Developing with Docker
Docker provides a contained environment for your development. By using Docker, you may not need to install pyenv/pipenv/virtualenv or any programming languages on your computer. You just use a Docker container! In this article, you will learn how to run Jupyter on Docker.
$ docker run -p 8888:8888 -v $(pwd):/home/jovyan/work jupyter/minimal-notebook
$ docker run -p 8888:8888 -v $(pwd):/home/jovyan/work jupyter/scipy-notebookThe first command above will run the Jupyter minimal-notebook connecting the local directory to a Docker container.
The second command is the same as the first one. Only the difference is running the Jupyter Scipy-notebook.
Install Docker Desktop and when you start Docker you will see an icon in the menu bar.
The Docker Preferences menu allows you to configure your Docker settings such as installation, updates, version channels, Docker Hub login, and more. Open Preferences and go to Resources to change CPUs, Memory, and other setups. By default, Docker Desktop is set to use half the number of processors available on the host machine.
You can read more details on this page for Mac, and Windows.
In your terminal run docker and you will see all commands:
$ docker
Usage: docker [OPTIONS] COMMAND
A self-sufficient runtime for containers
Options:
--config string Location of client config files (default "/Users/shinokada/.docker")
-c, --context string Name of the context to use to connect to the daemon (overrides
DOCKER_HOST env var and default context set with "docker context use")
-D, --debug Enable debug mode
... MORE LINESDocker groups the commands logically into management commands. Here are the top-level commands.
Management Commands:
builder Manage builds
config Manage Docker configs
container Manage containers
context Manage contexts
image Manage images
network Manage networks
node Manage Swarm nodes
plugin Manage plugins
secret Manage Docker secrets
service Manage services
stack Manage Docker stacks
swarm Manage Swarm
system Manage Docker
trust Manage trust on Docker images
volume Manage volumesCommands:
attach Attach local standard input, output, and error streams to a running container
... MORE LINES
You can get more information about Management Commands using --help.
$ docker container --help
$ docker image --helpLet’s start creating Docker containers.
Jupyter Docker Stacks are a set of ready-to-run Docker images containing Jupyter applications and interactive computing tools.
Official Jupyter created different Docker images and we are going to use jupiter/minimal to learn how to use Docker. The image is based on jupyter/base-notebook and it has command line tools, TeX Live, git, emacs, vi, jed, and more.
$ docker run -p 8888:8888 jupyter/minimal-notebook Around the end of the outputs, you can find the URL with a token. You use cmd+click to open the URL on a browser.
Open another terminal tab and check the container-id number.
$ docker container ls
# or
$ docker psIt is a bit hard to read the above output. Let’s create a format.
Add the following to your ~/.zshrc or ~/.bash_profile.
FORMAT="\nID\t{{.ID}}\nIMAGE\t{{.Image}}\nCOMMAND\t{{.Command}}\nCREATED\t{{.RunningFor}}\nSTATUS\t{{.Status}}\nPORTS\t{{.Ports}}\nNAMES\t{{.Names}}\n"And you can use it by adding it to the--format option for docker:
$ docker ps --format=$FORMAT
# or
$ docker container ls --format=$FORMATYou can use it with or without = sign as you see above.
docker exec runs a command in a running container. The -i option allows us to use it interactively and the -t allocates a pseudo-TTY. We use /bin/bash to run a bash shell.
$ docker exec -it cb8b /bin/bashThe cb8b is the first 4 letters of my container. You have a different container id.
You can find more information by using docker exec --help.
$ docker exec --help
Usage: docker exec [OPTIONS] CONTAINER COMMAND [ARG...]
Run a command in a running container
Options:
-d, --detach Detached mode: run command in the background
--detach-keys string Override the key sequence for detaching a container
-e, --env list Set environment variables
... MORE LINESOnce you are in the container, you can run normal bash commands.
Let’s check the container Python and pip versions:
(base) jovyan@cb8b4579b2a6:~/work$ python -V
Python 3.8.5
(base) jovyan@cb8b4579b2a6:~/work$ pip -V
pip 20.2.3 from /opt/conda/lib/python3.8/site-packages/pip (python 3.8)This image has ipython and conda as well. Let’s check them:
(base) jovyan@cb8b4579b2a6:~$ ipython --version
7.18.1
(base) jovyan@cb8b4579b2a6:~$ conda info -aactive environment : base
active env location : /opt/conda
shell level : 1
user config file : /home/jovyan/.condarc
populated config files : /opt/conda/.condarc
conda version : 4.8.5
conda-build version : not installed
--- MORE LINES
You can find what Python packages are installed:
(base) jovyan@cb8b4579b2a6:~$ pip listPandas, numpy, etc are not installed. You can install them using pip install pandas numpy:
(base) jovyan@cb8b4579b2a6:~/work$ pip install pandas numpyYou can exit the bash using exit.
CTRL-C exits a running container.
Let’s stop a running container:
$ docker container ls
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
cb8b4579b2a6 jupyter/minimal-notebook "tini -g -- start-no…" 4 hours ago Up 4 hours 0.0.0.0:8888->8888/tcp suspicious_allenYou can stop a container using docker container stop <container-id>:
$ docker container stop cb8bYou can find all the containers including inactive containers:
$ docker container ls -a
# or
$ docker ps -aYou can list docker images:
$ docker imagesYou can remove an image using docker rmi <image-id>.
$ docker rmi -f <image-id>-f force to remove a running container without stopping a container.
If you want to remove all images at once:
$ docker rmi $(docker images -a -q)-a allows us to remove all images and -q shows numeric IDs after the removal.
You can use remove all containers in the same way:
$ docker rm $(docker ps -a -q)Using --rmYou can run a container with --rm to automatically clean up the container when exit the container.
$ docker run --rm jupyter/minimal-notebookYou don’t need to run docker ps -a and docker rm <image-id> after exiting your container.
You can combine the option with -it.
$ docker run -it --rm jupyter/minimal-notebook bash
(base) jovyan@c803e897b718:~$When you run this command, you can use the bash in the container and when you exit, it will clean up the container.
Docker volumes are directories (or files) that are outside of the default Docker file system and exist as normal directories and files on the host filesystem. A volume does not increase the size of the containers using it, and the volume’s contents exist outside the lifecycle of a given container.
We can create a volume using -v option.
$ docker run -p 8888:8888 -v /Users/yourname/yourdirectory:/home/jovyan/work jupyter/minimal-notebookIf you want to use the current working directory, use $(pwd).
$ docker run -p 8888:8888 -v $(pwd):/home/jovyan/work jupyter/minimal-notebookWhatever you make changes in the Jupyter notebook, it changes your local file as well.
You can inspect a docker container:
$ docker inspect <container-id or container-name>It returns the docker object information.
Let’s install all Python packages when you are creating a container.
Create a new directory and a file called Dockerfile with the following content:
ARG BASE_CONTAINER=jupyter/minimal-notebook
FROM $BASE_CONTAINERLABEL author="Shinichi Okada"USER rootRUN pip install pandas numpy matplotlib plotly# Switch back to jovyan to avoid accidental container runs as root
USER $NB_UID
Then run docker build:
$ docker build -t shinokada/jupyter-notebook .
$ docker run -p 8888:8888 -v /Users/shinokada/DataScience:/home/jovyan/work shinokada/jupyter-notebookOpen another terminal tab:
$ docker ps
$ docker exec -it <container-id> /bin/bash
(base) jovyan@a56262d7eabc:~$ pip list
...
matplotlib
...
numpy
...
pandasWe entered in a running container using docker exec and list the Python packages to see if our packages are installed.
Sign up at hub.docker, then in your terminal:
$ docker login
Username: your username
Password: your password
...
$ docker push shinokada/jupyter-notebookMore docker commands
# Show the history of image
docker history <image-id>
# Display system-wide information
docker info | moreThe jupyter/scipy-notebook is a Jupyter Notebook scientific Python stack and it includes popular packages from the scientific Python ecosystem. If you want to try Deep Learning then jupyter/tensorfolow-notebook is for you. It includes popular Python deep learning libraries. You can find more images at these links.
You can use docker not only for the Jupyter Notebook but also for your general development. There are many repositories at the Docker Hub. Try to find official ones you can trust or create your own and push to the Docker Hub. You won’t need to update Python packages on your system anymore.
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FAQs
Can you run Jupyter Notebook in Docker? ›
Jupyter has created a few Docker images containing Jupyter applications and other tools. It then starts a container running a Jupyter Notebook server on port 8888. Now it's very similar to running a regular Jupyter notebook, you'll have a link to the Jupyter localhost server and a given token.
How do I run an entire code in Jupyter? ›- Use the following smart shortcuts to quickly run the code cells: Ctrl+Enter : Runs the current cell. Shift+Enter : Runs the current cell and select the cell below it. ...
- To execute all code cells in your notebook, click. on the notebook toolbar or press Ctrl+Alt+Shift+Enter .
To begin, go to your Jupyter Notebook to create a notebook with a containerized kernel. In your Jupyter Notebook, choose New, then choose the kernel type you want from the dropdown list. (The kernel type should start with "Containerized" and end with whatever kernel you would have otherwise selected.
How much RAM is required for Jupyter Notebook? ›Sizing requirements:
Number of CPU Cores: 4. RAM: 16 Gb.
Docker For Development Environment Databases
Should you run databases in Docker? If you're doing so in your development environment, there's nothing to be concerned about. You don't have important data to lose. In case anything goes wrong, you simply recreate your environment from scratch.
- jupyter: image: jupyter/datascience-notebook:latest container_name: jupyter ports: - 8888:8888.
- jupyter: image: jupyter/datascience-notebook:latest container_name: jupyter ports: - 8888:8888 environment: JUPYTER_ENABLE_LAB: "yes" JUPYTER_TOKEN: "docker"
The most basic and easy way to run a Python script is by using the python command. You need to open a command line and type the word python followed by the path to your script file like this: python first_script.py Hello World! Then you hit the ENTER button from the keyboard, and that's it.
How do I run a whole Python script? ›To run Python scripts with the python command, you need to open a command-line and type in the word python , or python3 if you have both versions, followed by the path to your script, just like this: $ python3 hello.py Hello World! If everything works okay, after you press Enter , you'll see the phrase Hello World!
Can you run a Jupyter Notebook as a Python script? ›You can use the nbconvert package to execute ipython/jupyter notebooks from within python. Instructions are available within the nbconvert documentation: Executing notebooks. The output is an ipython/jupyter notebook including the output of all cells.
How to execute Python script in docker container? ›- Step 1: Creating the Files and Folders.
- Step 2: Creating the Dockerfile.
- Step 3: Building the Docker Container.
- Step 4: Verify the Image Build.
- Step 5: Running the Docker Container.
What should you not Containerize? ›
So, one example of when not to use containers is if a high level of security is critical. They can require more work upfront: If you're using containers right, you will have decomposed your application into its various constituent services, which, while beneficial, isn't necessary if you are using VMs.
How to dockerize my Python script? ›- Create a new, named project within your editor.
- Form your new directory by creating a new root project folder in the sidebar, and naming it.
- Open a new workspace named main.py .
- Enter the cd [root folder name] command in the Terminal to tap into your new directory.
So no, there is no significant difference, and the minor difference is in favor of Jupyter.
How many cores does Jupyter notebook use? ›The maximum number of cores per session is 40 (number of core on a single compute node). The Python kernel running in Jupyter is a single-node process, it cannot utilize more than one computer (node).
What is the maximum RAM for Jupyter? ›JupyterLab (or AI Notebook) in GCP by default is set with a maximum of 3.2GB of memory. If the amount of data loaded into memory (i.e. by RAM) is large, the Jupyter Kernel will “die”.
What are the main drawback of Docker? ›- Docker is not good for application that requires rich GUI.
- It is difficult to manage large amount of containers.
- Docker does not provide cross-platform compatibility means if an application is designed to run in a Docker container on windows, then it cannot run on Linux Docker container.
The “official” Docker image has the benefit of providing every version of Python you might want, and tends to be very up-to-date with bugfix releases.
Is Docker safer than VM? ›Docker vs VM: Data Security
A virtual machine has an edge over the Docker container system concerning client-server-based data security. This is because a virtual machine does not share an operating system, which makes the virtual machine very strong in terms of being isolated from threats.
JupyterLab is the next generation of the Jupyter Notebook. It aims at fixing many usability issues of the Notebook, and it greatly expands its scope. JupyterLab offers a general framework for interactive computing and data science in the browser, using Python, Julia, R, or one of many other languages.
Will JupyterLab replace Jupyter Notebook? ›JupyterLab will eventually replace the classic Jupyter Notebook.
Can you use JupyterLab without anaconda? ›
JupyterLab can be installed using conda , mamba , pip , pipenv or docker .
How do I run an infinite script in Python? ›We can create infinite loops in Python via the while statement. In a loop, the variable is evaluated and repeatedly updated (while the given condition is True). We can create an infinite loop in Python if we set the condition in a way that it always evaluates to True.
How do I make Python run incredibly fast? ›- Proper Algorithm & Data Structure. Each data structure has a significant effect on runtime. ...
- Using Built-in Functions and Libraries. Python's built-in functions are one of the best ways to speed up your code. ...
- Use Multiple Assignments. ...
- Prefer List Comprehension Over Loops. ...
- Proper Import. ...
- String Concatenation.
- Edit your cronjob file by running crontab -e command.
- Add the following line for an every-5-minutes interval. */5 * * * * /path/to/script-or-program.
- Save the file, and that is it.
- Create Your First Task. Search for “Task Scheduler”. ...
- Create an Action. Go to Actions > New.
- Add the Python Executable File to the Program Script. ...
- Add the Path to Your Python Script in the Arguments. ...
- Trigger Your Script Execution.
The simplest way to keep a program running “forever” is to put your code in a while loop and set the condition to True.
Can you make a full game with Python? ›You can write whole games in Python using PyGame. See a list of other PythonGameLibraries maintained in this Wiki, or this list maintained on DevMaster.net. A full tutorial can be found in the free book "Making Games with Python & Pygame".
Is Jupyter best IDE for Python? ›Jupyter is another best IDE for Python Programming that offers an easy-to-use, interactive data science environment across many programming languages besides Python.
Can Jupyter be used as an IDE? ›Jupyter Notebook provides you with an easy-to-use, interactive data science environment across many programming languages that doesn't only work as an IDE, but also as a presentation or education tool.
How to use Jupyter without Anaconda? ›Jupyter does not need to have anaconda installed. You could install it in any python environment with pip install jupyter[all] .
How do I start Python in Docker? ›
Then you'd only have to exit your program with a non-zero code (i.e: run exit 1 from your start.sh ) and docker will restart it from scratch.
How to run code in docker container? ›- Install Docker on your machine. For Ubuntu: ...
- Create your project. ...
- Edit the Python file. ...
- Edit the Docker file. ...
- Create the Docker image. ...
- Run the Docker image.
- Prerequisites.
- Create a container.
- Build a container image for the app.
- Start your app container.
- Update the code and replace the container.
- Share your image.
- Run the image on a new instance.
- Clean up resources.
Docker is great for developing web applications, but if your end-product is a desktop application, then we would suggest you not to use Docker. As it doesn't provide the environment for running the software with a graphical interface, you would need to perform additional workarounds.
When should we not use Docker and Docker? ›Docker is very useful for web applications running on a server or console-based software. But if your product is a standard desktop application, especially with a rich GUI, Docker may not be the best choice.
Is Docker still the best container? ›So, why the change? Simply put, Docker is heavy. We get better performance with a lightweight container runtime like containerd or CRI-O. As a recent example, Google benchmarks have shown that containerd consumes less memory and CPU, and that pods start in less time than on Docker.
Why do people use Docker? ›Docker is a tool designed to make it easier for developers to develop, ship, and run applications by using containers. Containers allow devs to package an application with all of its requirements and configurations, such as libraries and other dependencies and deploy it as a single package.
How do I Dockerize my code? ›- Choose a base Image. You can start from a Base OS and install everything by yourself. ...
- Install the necessary packages. ...
- Add your custom files. ...
- Define which user will (or can) run your container. ...
- Define the exposed ports. ...
- Define the entrypoint. ...
- Define a Configuration method. ...
- Externalize your data.
You can use Docker to pack your application with everything you need to run the application (such as libraries) and ship it as one package - a container. Containers are created from images that specify their precise contents. Dockerizing is a big hit nowadays.
Do professionals use Jupyter Notebook? ›Yes, and yes. I'm a medical researcher doing a lot of data analysis in Python. I use both Jupyter and Visual Studio Code (I prefer that over PyCharm). Generally, I do the actual running of the code in Jupyter.
Which is better Anaconda or Jupyter? ›
Anaconda is an open source Python distribution / data discovery & analytics platform. Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text.
Will Jupyter Notebook be deprecated? ›Jupyter Notebook is deprecated and it is advised to transition to JupyterLab. Please note that this repository is currently maintained by a skeleton crew of maintainers from the Jupyter community. We encourage users to transition to JupyterLab, where more immediate support can occur.
Is Jupyter slower than Python? ›In short, Jupyter is an editing tool from which you can execute python code (among others). Jupyter doesn't replace, substitute, or enhance the performance of your code in any way. So no, Jupyter notebook is not slower than python since they are independent of each other.
Is there GPU in Jupyter notebook? ›Jupyter Notebooks from the NGC catalog can run on GPU-powered on-prem systems, including NVIDIA DGX™, as well as on cloud instances. Instructions for running a Jupyter Notebook from the NGC catalog.
Which is faster Jupyter notebook or PyCharm? ›PyCharm's auto-complete feature really facilitates faster development and workflow, and it's something that Jupyter does not offer. This smart editing feature is why PyCharm is clearly the choice for developers and software engineers, especially those working exclusively in Python.
What is the best way to run Jupyter notebook? ›How to open Jupyter Notebook. To launch a Jupyter notebook, open your terminal and navigate to the directory where you would like to save your notebook. Then type the command jupyter notebook and the program will instantiate a local server at localhost:8888 (or another specified port).
Can you run a whole Jupyter notebook? ›You can run the whole notebook in a single step by clicking on the menu Cell -> Run All. To restart the kernel (i.e. the computational engine), click on the menu Kernel -> Restart.
Do you need 16GB of RAM for data science? ›For data science applications and workflows, 16GB of RAM is recommended. If you're looking to train large complex models locally, HP offers configurations of up to 128GB of blazing-fast DDR5 RAM.
Can I run Python on Docker? ›After you have created both the Python script and the Dockerfile, you can now use the Docker build command to build your Docker Image. Here -t is for adding tags so as to identify your image easily.
Which tool can you use to run Jupyter notebooks? ›Jupyter notebook
You can install it using either pip or Anaconda using conda . I'd recommend using something like pyenv and a virtual environment to setup and run a newer version of Python if you don't choose conda . The Jupyter project recommends using Anaconda in their docs.
Can you run Jupyter Notebook on Linux? ›
Using Anaconda:
Install Python and Jupyter using the Anaconda Distribution, which includes Python, the Jupyter Notebook, and other commonly used packages for scientific computing and data science. To install Anaconda, go through How to install Anaconda on Linux? and follow the instructions provided.
On Linux systems, Jupyter Notebook can be installed both through its command line interface and its graphical user interface. The command line interface rests on its Terminal. To install Jupyter, you would first have to install or update Python and then install Python notebook.
Why Docker is shutting down? ›The process inside the container has been terminated: This is when the program that runs inside the container is given a signal to shut down. This happens if you run a foreground container (using docker run ), and then press Ctrl+C when the program is running.
Which Docker image is best for Python? ›The official Docker Python image in its slim variant—e.g. python:3.8-slim-buster —is a good base image for most use cases. it's 60MB when downloaded, 180MB when uncompressed to disk, it gives you the latest Python releases, and it's got all the benefits of Debian Buster.
Is Docker good for machine learning? ›Docker makes it easy to manage dependencies and set up environments, which is essential for machine learning projects that can be repeated. Researchers and developers can easily recreate the exact environment and dependencies used to train and deploy a model.
What is the difference between Jupyter and Jupyter Notebook? ›JupyterLab runs in a single tab, with sub-tabs displayed within that one tab, Jupyter Notebook opens new notebooks in new tabs. So JupyterLab feels more like an IDE; in Notebook notebooks, it feels more standalone. All the files are opened as different tabs in your webbrowser. It depends on you what you prefer more.
Can Jupyter run SQL? ›In this post you will learn two easy ways to use Python and SQL from the Jupyter notebooks interface and create SQL queries with a few lines of code. These two methods are almost database-agnostic, so you can use them for any SQL database of your choice: MySQL, Postgres, Snowflake, MariaDB, Azure, etc.
Can I run C++ in Jupyter? ›A C++ notebook
You can now make use of the C++ programming language in the Jupyter notebook.
- STEP 1 — Installing Python. Go to https://www.python.org/downloads/ and download the latest version of the python. ...
- STEP 2 — Installing Jupyter Notebook Using Pip. ...
- STEP 3 — CREATING A JUPYTER NOTEBOOK SHORTCUT. ...
- STEP 4 — CHANGING ICON. ...
- STEP5 — CHANGING THE STARTING DIRECTORY.
How to open Jupyter Notebook. To launch a Jupyter notebook, open your terminal and navigate to the directory where you would like to save your notebook. Then type the command jupyter notebook and the program will instantiate a local server at localhost:8888 (or another specified port).
How do I start Jupyter in Linux terminal? ›
3.1.
To launch Jupyter Notebook App: Click on spotlight, type terminal to open a terminal window. Enter the startup folder by typing cd /some_folder_name . Type jupyter notebook to launch the Jupyter Notebook App The notebook interface will appear in a new browser window or tab.
Language of choice. Jupyter supports over 40 programming languages, including Python, R, Julia, and Scala.