An easy to use blogging platform with extra features for Jupyter Notebooks.
We are very pleased to announce the immediate availability of fastpages.
fastpages is a platform which allows you to create and host a blog for free, with no ads and many useful features, such as:
fastpages relies on Github pages for hosting, and Github Actions to automate the creation of your blog. The setup takes around three minutes, and does not require any technical knowledge or expertise. Due to built-in automation of fastpages, you don't have to fuss with conversion scripts. All you have to do is save your Jupyter notebook, Word document or markdown file into a specified directory and the rest happens automatically. Infact, this blog post is written in a Jupyter notebook, which you can see with the "View on GitHub" link above.
fast.ai have previously released a similar project called fast_template, which is even easier to set up, but does not support automatic creation of posts from Microsoft Word or Jupyter notebooks, including many of the features outlined above.
fastpages is more flexible and extensible, we recommend using it where possible.
fast_template may be a better option for getting folks blogging who have no technical expertise at all, and will only be creating posts using Github's integrated online editor.
The setup process of fastpages is automated with GitHub Actions, too! Upon creating a repo from the fastpages template, a pull request will automatically be opened (after ~ 30 seconds) configuring your blog so it can start working. The automated pull request will greet you with instructions like this:
All you have to do is follow these instructions (in the PR you receive) and your new blogging site will be up and running!
In this post, we will cover special features that fastpages provides has for Jupyter notebooks. You can also write your blog posts with Word documents or markdown in fastpages, which contain many, but not all the same features.
The first cell in your Jupyter Notebook or markdown blog post contains front matter. Front matter is metadata that can turn on/off options in your Notebook. It is formatted like this:
# Title > Awesome summary - toc: true - branch: master - badges: true - comments: true - author: Hamel Husain & Jeremy Howard - categories: [fastpages, jupyter]
All of the above settings are enabled in this post, so you can see what they look like!
>) will be displayed under your title, and will also be used by social media to display as the description of your page.
toc: setting this to
truewill automatically generate a table of contents
badges: setting this to
truewill display Google Colab and GitHub links on your blog post.
comments: setting this to
truewill enable comments. See these instructions for more details.
authorthis will display the authors names.
categorieswill allow your post to be categorized on a "Tags" page, where readers can browse your post by categories.
Markdown front matter is formatted similarly to notebooks. The differences between the two can be viewed on the fastpages README.
#collapse-hide flag at the top of any cell if you want to hide that cell by default, but give the reader the option to show it:
#collapse-hide import pandas as pd import altair as alt
#collapse-show flag at the top of any cell if you want to show that cell by default, but give the reader the option to hide it:
#collapse-show cars = 'https://vega.github.io/vega-datasets/data/cars.json' movies = 'https://vega.github.io/vega-datasets/data/movies.json' sp500 = 'https://vega.github.io/vega-datasets/data/sp500.csv' stocks = 'https://vega.github.io/vega-datasets/data/stocks.csv' flights = 'https://vega.github.io/vega-datasets/data/flights-5k.json'
# hide df = pd.read_json(movies) # load movies data genres = df['Major_Genre'].unique() # get unique field values genres = list(filter(lambda d: d is not None, genres)) # filter out None values genres.sort() # sort alphabetically
# select a point for which to provide details-on-demand label = alt.selection_single( encodings=['x'], # limit selection to x-axis value on='mouseover', # select on mouseover events nearest=True, # select data point nearest the cursor empty='none' # empty selection includes no data points ) # define our base line chart of stock prices base = alt.Chart().mark_line().encode( alt.X('date:T'), alt.Y('price:Q', scale=alt.Scale(type='log')), alt.Color('symbol:N') ) alt.layer( base, # base line chart # add a rule mark to serve as a guide line alt.Chart().mark_rule(color='#aaa').encode( x='date:T' ).transform_filter(label), # add circle marks for selected time points, hide unselected points base.mark_circle().encode( opacity=alt.condition(label, alt.value(1), alt.value(0)) ).add_selection(label), # add white stroked text to provide a legible background for labels base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode( text='price:Q' ).transform_filter(label), # add text labels for stock prices base.mark_text(align='left', dx=5, dy=-5).encode( text='price:Q' ).transform_filter(label), data=stocks ).properties( width=700, height=400 )
movies = 'https://vega.github.io/vega-datasets/data/movies.json' df = pd.read_json(movies) # display table with pandas df[['Title', 'Worldwide_Gross', 'Production_Budget', 'IMDB_Rating']].head()
|0||The Land Girls||146083.0||8000000.0||6.1|
|1||First Love, Last Rites||10876.0||300000.0||6.9|
|2||I Married a Strange Person||203134.0||250000.0||6.8|
|3||Let's Talk About Sex||373615.0||300000.0||NaN|
You can include markdown images with captions like this:
!(https://www.fast.ai/images/fastai_paper/show_batch.png "Credit: https://www.fast.ai/2020/02/13/fastai-A-Layered-API-for-Deep-Learning/")
Of course, the caption is optional.
> twitter: https://twitter.com/jakevdp/status/1204765621767901185?s=20 will render this:
> Warning: There will be no second warning! will render this:
Warning: There will be no second warning!
> Important: Pay attention! It's important. will render this:
Important: Pay attention! It's important.
> Tip: This is my tip. will render this:
Tip: This is my tip.
> Note: Take note of this. will render this:
Note: Take note of this.
> Note: A doc link to [an example website: fast.ai](https://www.fast.ai/) should also work fine. will render in the docs:
Note: A doc link to an example website: fast.ai should also work fine.
fastpages uses nbdev to power the conversion process of Jupyter Notebooks to blog posts. When you save a notebook into the
/_notebooks folder of your repository, GitHub Actions applies
nbdev against those notebooks automatically. The same process occurs when you save Word documents or markdown files into the
_posts directory, respectively.
We will discuss how GitHub Actions work in a follow up blog post.
We highly encourage you to start blogging with
fastpages! Some resources that may be helpful: