Jupyter Notebook is a web-based development tool that makes it easier for developers to manage projects. With a user-friendly interface, Jupyter includes interactive elements to create and share live documents that contain code, visuals, equations, and even narrative texts.
I’ve already written about how to install Jupyter Notebook in my piece “ Jupyter Notebooks: The Web-Based Dev Tool You’ve Been Seeking,” so you should read through that tutorial to get Jupyter up and running.
Thing is, with a default Jupyter installation, you miss out on GitHub integration. And given how so many developers depend on the likes of GitHub, this is a feature that is sorely missed.
Fortunately, a developer has created an extension that makes it possible for you to use Jupyter with GitHub. Unfortunately, since the developer created the extension for Jupyter/GitHub, things have changed on the side of GitHub, so there’s one caveat to using this tool (I’ll explain later). But even with that caveat, this extension is a good way to keep your Jupyter Notebooks in sync with a GitHub repository (otherwise, all of those notebooks will remain on your local machine).
Let’s get these two pieces of technology connected.
Before you start this process, make sure you’ve taken care of getting Jupyter installed. Make sure you don’t launch a notebook yet. We’ll do that in a bit.
You’ve already installed the necessary dependencies for Jupyter (Python and pip). You now need to install the Jupyter GitHub extension. Log into your machine that contains Jupyter and open a terminal window. From the CLI, issue the following commands:
pip install git+https://github.com/sat28/githubcommit.git
jupyter serverextension enable --py githubcommit
jupyter nbextension install --py githubcommit --user
jupyter nbextension enable githubcommit --user --py
The above commands will install the extension and make sure it is available for all notebooks.
#data #development #tutorial #github
As a data scientist and machine learning engineering, the Jupyter notebook is handy tools you can use.
How cool if you convert that notebook into a blog within less than 5 min?
You can find a live demo here.
#github-pages #jupyter-notebook #github-actions #data-science #markdown
Nosso convidado de hoje é diretor técnico na Work & Co, PhD em Ciências da Computação, já contribuiu com inúmeros projetos open source em Python, ajudou a fundar a Associação Python Brasil e já foi premiado com o Prêmio Dorneles Tremea por contribuições para a comunidade Python Brasil.
If you have project code hosted on GitHub, chances are you might be interested in checking some numbers and stats such as stars, commits and pull requests.
You might also want to compare some similar projects in terms of the above mentioned stats, for whatever reasons that interest you.
We have the right tool for you: the simple and easy-to-use little tool called GitHub Stats.
Let’s dive right in to what we can get out of it.
This interactive tool is really easy to use. Follow the three steps below and you’ll get what you want in real-time:
1. Head to the GitHub repo of the tool
2. Enter as many projects as you need to check on
3. Hit the Update button beside each metric
In this article we are going to compare three most popular machine learning projects for you.
#github #tools #github-statistics-react #github-stats-tool #compare-github-projects #github-projects #software-development #programming
This is a Git-101 for Jupyter users that are not familiar with Git / GitHub. It’s a hands on tutorial & is meant to be comprehensive. Feel free to skip a section if you are already familiar with the steps. At the end you’ll be able to,
If you don’t have a GitHub account please create one here.
>> git config --global user.name "Mona Lisa" >> git config --global user.email "firstname.lastname@example.org"
A GitHub repository is like your supercharged folder in the cloud. You can store files (notebooks, data, source code), look at historical changes to these files, open issues, discuss changes and much more. People typically create one repository per project.
Let’s go ahead & create a repository on GitHub. Once created, you’ll see a page like below, copy the highlighted repository URL.
Let’s clone the GitHub repository on our machine by running following command on the terminal. It will create projectA directory on our machine which is linked to amit1rrr/projectA repository on GitHub. Replace
[https://github.com/amit1rrr/projectA.git](https://github.com/amit1rrr/projectA.git) with your own repository URL from the above step —
#code-review #jupyter-notebook #version-control #github #git
I have an older ’09 Macbook Pro and had trouble while trying to run the latest Keras to use for my latest Flatiron school neural network projects. The older Mac only updates to El Capitan, so it’s unable to run the latest versions of Keras in Jupyter notebooks. To get around this, I have been trying to integrate the use of Google Colab with my workflow. Colab is a cloud-based notebook and has the needed updates to run Keras and has come in handy. Trying to learn the small differences between Jupyter notebooks and Colab in order to keep everything flowing smoothly and all my changes synced has been challenging. Here is a little workflow that I’ve found useful in managing my notebooks and Github repositories. It’s not the most elegant but I have found it to be useful and it gets the job done for now, while I continue to learn more about using Colab and all of it’s connections to Github.
If you already have a repository you want to work with, then move to step two of this process to create or open a Colab notebook. If you need to make a whole new repository to start with, go to Github and create a new one. Click the green “New” button under the repository section.
Click the green button to create a new repo.
Add a name for the repository on the next screen, and click the box to initialize a README. You can choose to add a license to the repository, though I am still learning about all of the different types there are and won’t go into detail about that in this article. Here’s a good link I found about the differences between some of them for you to check out. https://www.fastcompany.com/3014553/what-coders-should-know-about-copyright-licensing. Then click “create repository”.
The next screen will be a sort of blank slate repository which shows your README file which you can leave alone for now and head over to Google Drive.
#google-colab #python #jupyter-notebook #mac #github