For instance, f-strings were introduced in Python 3.6 and won’t work in older versions of Python. Similarly, assignment expressions only became available in Python 3.8. Development versions: The Python community is continuously working on new versions of Python. At the time of this writing, Python 3.9 was under development.
Docker Image with Python Application
We shall learn to build a docker image with python application, and save the docker image to a file for usage in different computer.
Build a Ubuntu docker with Python3 and pip support. I am using the official Ubuntu docker. The following is a minimum Dockerfile: FROM ubuntu:18.04 RUN apt-get update && apt-get install -y software-properties-common gcc && add-apt-repository -y ppa:deadsnakes/ppa RUN apt-get update && apt-get install -y python3.6 python3-distutils python3-pip. Docker SDK for Python¶ A Python library for the Docker Engine API. It lets you do anything the docker command does, but from within Python apps – run containers, manage containers, manage Swarms, etc. For more information about the Engine API, see its documentation. FROM tells Docker which image you base your image on (in the example, Python 3). RUN tells Docker which additional commands to execute. CMD tells Docker to execute the command when the image loads. The Python script myscript.py looks like the following. 6 minutes ago Exited (0) 7 minutes ago giftednobel Now, stop it again and remove all the containers manually: docker stop daemon docker rm docker rm daemon To remove all containers, we can use the following command: docker rm -f $(docker ps -aq) docker rm is the command to remove the container.
Build Docker Image with Python Application
1. Create a directory
A separate directory is useful to organise docker applications. For this Python Example, create a directory somewhere with name of your choice. We shall use the name python-application
2. Create Python Application
Create a simple Python File, in the directory python-application, with name PythonExample.py containing the following content :
3. Create Dockerfile
Create a file with name Dockerfile. Dockerfile contains instructions to prepare Docker image with our Python Application.
Following is the content of Dockerfile.
4. Build Docker Image
Run the following command in Terminal, from python-application directory, to create Docker Image with Python Application.
Docker image with python application is created successfully.
5. Check the docker image
To display available docker images, run the following command.
Run Docker Image with Python Application
Save Docker Image to a tar file
Save the Docker Image file to a tar file, so that the image file could be copied to other machines through disk storage devices like pen-drive, etc.
Run the following command to save Docker image as a tar file.
Saving might take few seconds. Wait for the command to complete.
Now you may copy or ship the Docker Image file that is having Python Application.
In this Docker Tutorial – Docker Python Application Example, we have learnt to build a Docker Image with Python Application and also how to save the image to a file and transfer it to other computers or servers.
Artificial Intelligence(AI) and Machine Learning(ML) are literally on fire these days. Powering a wide spectrum of use-cases ranging from self-driving cars to drug discovery and to God knows what. AI and ML have a bright and thriving future ahead of them.
On the other hand, Docker revolutionized the computing world through the introduction of ephemeral lightweight containers. Containers basically package all the software required to run inside an image(a bunch of read-only layers) with a COW(Copy On Write) layer to persist the data.
Enough talk let’s get started with building a Python data science container.
Python Data Science Packages
Our Python data science container makes use of the following super cool python packages:
- NumPy: NumPy or Numeric Python supports large, multi-dimensional arrays and matrices. It provides fast precompiled functions for mathematical and numerical routines. In addition, NumPy optimizes Python programming with powerful data structures for efficient computation of multi-dimensional arrays and matrices.
- SciPy: SciPy provides useful functions for regression, minimization, Fourier-transformation, and many more. Based on NumPy, SciPy extends its capabilities. SciPy’s main data structure is again a multidimensional array, implemented by Numpy. The package contains tools that help with solving linear algebra, probability theory, integral calculus, and many more tasks.
- Pandas: Pandas offer versatile and powerful tools for manipulating data structures and performing extensive data analysis. It works well with incomplete, unstructured, and unordered real-world data — and comes with tools for shaping, aggregating, analyzing, and visualizing datasets.
- SciKit-Learn: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. It is one of the best-known machine-learning libraries for python. The Scikit-learn package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. The primary emphasis is upon ease of use, performance, documentation, and API consistency. With minimal dependencies and easy distribution under the simplified BSD license, SciKit-Learn is widely used in academic and commercial settings. Scikit-learn exposes a concise and consistent interface to the common machine learning algorithms, making it simple to bring ML into production systems.
- Matplotlib: Matplotlib is a Python 2D plotting library, capable of producing publication quality figures in a wide variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the Jupyter notebook, web application servers, and four graphical user interface toolkits.
- NLTK: NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.
Building the Data Science Container
Python is fast becoming the go-to language for data scientists and for this reason we are going to use Python as the language of choice for building our data science container.
The Base Alpine Linux Image
Alpine Linux is a tiny Linux distribution designed for power users who appreciate security, simplicity and resource efficiency.
As claimed by Alpine:
Small. Simple. Secure. Alpine Linux is a security-oriented, lightweight Linux distribution based on musl libc and busybox.
The Alpine image is surprisingly tiny with a size of no more than 8MB for containers. With minimal packages installed to reduce the attack surface on the underlying container. This makes Alpine an image of choice for our data science container.
Downloading and Running an Alpine Linux container is as simple as:
In our, Dockerfile we can simply use the Alpine base image as:
Talk is cheap let’s build the Dockerfile
Now let’s work our way through the Dockerfile.
FROM directive is used to set
alpine:latest as the base image. Using the
WORKDIR directive we set the
/var/www as the working directory for our container. The
ENV PACKAGES lists the software packages required for our container like
libgfortran. The python packages for our data science container are defined in the
We have combined all the commands under a single Dockerfile
RUN directive to reduce the number of layers which in turn helps in reducing the resultant image size.
Building and tagging the image
Now that we have our Dockerfile defined, navigate to the folder with the Dockerfile using the terminal and build the image using the following command:
-t flag is used to name a tag in the 'name:tag' format. The
-f tag is used to define the name of the Dockerfile (Default is 'PATH/Dockerfile').
Running the container
We have successfully built and tagged the docker image, now we can run the container using the following command:
Voila, we are greeted by the sight of a python shell ready to perform all kinds of cool data science stuff.
Our container comes with Python 2.7, but don’t be sad if you wanna work with Python 3.6. Lo, behold the Dockerfile for Python 3.6:
Build and tag the image like so:
Run the container like so:
With this, you have a ready to use container for doing all kinds of cool data science stuff.
Figures, you have the time and resources to set up all this stuff. In case you don’t, you can pull the existing images that I have already built and pushed to Docker’s registry Docker Hub using:
After pulling the images you can use the image or extend the same in your Dockerfile file or use it as an image in your docker-compose or stack file.
The world of AI, ML is getting pretty exciting these days and will continue to become even more exciting. Big players are investing heavily in these domains. About time you start to harness the power of data, who knows it might lead to something wonderful.
You can check out the code here.
Docker Python 3 6 2
Docker image for python datascience container with NumPy, SciPy, Scikit-learn, Matplotlib, nltk, pandas packages…github.com
Docker Python3 6 Ubuntu
I hope this article helped in building containers for your data science projects. Clap if it increased your knowledge, help it reach more people.
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