Computable Content

Computable
Content:
 
Lessons
Learned
Paco
Nathan
@pacoid

Director,
Learning
Group
@
O’Reilly
Media

2017-‐06-‐21

Jupyter
use
@
O’Reilly
Media
▪ Embracing
Jupyter
notebooks
at
O’Reilly 
oreilly.com/ideas/jupyter-‐at-‐oreilly

▪ Learning
alongside
innovators,
thought-‐by-‐thought,
in
context 
oreilly.com/ideas/oreilly-‐oriole-‐learn-‐alongside-‐innovators-‐
thought-‐by-‐thought-‐in-‐context

▪ Oriole
online
tutorials 
safaribooksonline.com/oriole/

▪ How
do
you
learn? 
oreilly.com/learning/how-‐do-‐you-‐learn
5

For
example
▪ A
unique
new
medium
blends
code,
data,
text,
 
and
video
into
a
narrated
learning
experience 
with
computable
content

▪ Purely
browser-‐based
UX;
zero
installation
 
required

▪ Substantially
higher
engagement
metrics

▪ Opens
the
door
for
live
coding
in
assessments
6
oreilly.com/learning/regex-‐golf-‐with-‐peter-‐norvig

Motivations
O’Reilly
needed
a
way
for
authors
to
use
Jupyter
notebooks
to
create

professional
publications.
We
also
wanted
to
integrate
video
narration

into
the
UX.
The
result
is
a
unique
new
medium
called
Oriole:

▪ Context
as
a
“unit
of
thought”

▪ Code
and
video
sync’ed
together

▪ Each
web
session
get
its
own
Docker
container
in
the
cloud

▪ 100%
HTML
experience,
no
download/install/config
needed

▪ Jupyter
notebooks
used
in
the
middleware

▪ Leverage
interactive,
data-‐driven
graphics
7

Outcomes
8
▪ Tutorials
are
now
much
quicker
to
publish
than

“traditional”
books
and
videos

▪ Less
time
required
for
innovators
in
programming,

data
science,
devops,
design,
etc.
–
who
tend
to
be

really
busy
people

▪ Audience
gets
direct,
hands-‐on,
contextualized

experience
across
a
wide
variety
of
programming

environments

Limitations
9
▪ Notebook
kernels
run
REPLs,
so
older
languages

were
not
feasible

▪ Brief
code
blocks
with
tangible
outcomes
–
 
precludes
business
topics,
systems
engineering,
etc

▪ What
materials
will
fit
within
a
Docker
container?

Third
iteration
of
Jupyter
@
O’Reilly
10
1. notebooks
as
supplemental
material
to

other
published
work

2. notebooks
published
as
HTML,
as
articles

3. computable
content,
containerized

notebooks
+
video
narratives

4. hosted
notebooks

Long-‐term
goal:

make
learning
materials
more
powerful
by

integrating
compute
engines
+
data
services
11

Project
Jupyter
▪ The
evolution
of
iPython
notebooks,
applied

to
a
range
of
different
programming
languages

and
environments

▪ https://jupyter.org/

▪ https://github.com/ipython/ipython/wiki/
IPython-‐kernels-‐for-‐other-‐languages
13

Projects
15
▪ JupyterHub 
github.com/jupyterhub/jupyterhub

▪ Jupyter
in
Education 
groups.google.com/forum/#!forum/
jupyter-‐education

▪ JupyterLab 
github.com/jupyterlab/jupyterlab

▪ Jupyter
Kernels 
github.com/ipython/ipython/wiki/
IPython-‐kernels-‐for-‐other-‐languages

A
suite
of
network
protocols
Think
of
Jupyter,
at
its
core,
as
a
suite
of

network
protocols:

Jupyter
is
to
the
remote
semantics
of
a
REPL

as…

HTTP
is
to
the
remote
semantics
of
file
share
16

A
suite
of
network
protocols
17
Code%runs%
in%a%REPL
Kernel
Edi0ng%+%
Results
Notebook
Network
Protocol

Notebook
metaphor
Wolfram
Research
introduced

notebooks
in
1988
for
working
 
with
Mathematica
19

Literate
programming
Don
Knuth 
literateprogramming.com/

Paraphrased: 
Instead
of
telling
computers
what
to

do,
tell
other
people
what
you
want

the
computers
to
do
21

Speech
acts
PyCon
2016
Keynote,
Lorena
Barba

youtu.be/ckW1xuGVpug?t=35m11s 
(video)

figshare.com/articles/
PyCon2016_Keynote/3407779 
(slides)

Highly
recommended:
speech
acts
 
(based
on
Winograd
and
Flores)
 
as
theory
here
22

The
following
lessons
learned
in
using
Jupyter

notebooks
+
video
for
learning
materials
apply

well
in
many
situations
for
data
science
teams

working
across
an
organization
24

Teaching
with
Jupyter
–
1
of
2
▪ focus
on
a
concise
“unit
of
thought”

▪ invest
the
time
and
editorial
effort
to
create
a
good
intro

▪ keep
your
narrative
simple
and
reasonably
linear

▪ “chunk”
the
text
and
code
into
understandable
parts

▪ alternate
between
text,
code,
output,
further
links,
etc.

▪ code
cells
should
be
brief
(<
10
lines),
must
show
output
25

Teaching
with
Jupyter
–
2
of
2
▪ load
data+libraries
from
the
container,
not
the
network

▪ clear
all
output
then
“Run
All”
–
or
it
didn’t
happen

▪ video
narratives:
there’s
text,
and
there’s
subtext...

▪ pause
after
each
“beat”:
smile,
breathe,
let
people
follow
you

For
JVM
people:
stop
thinking
only
about
IDEs,
Ivy,
Maven,
etc.
(ibid,
Knuth1984) 
BUILD
UBER
JARS,
LOAD
LIBS
FROM
CONTAINER,
NOT
THE
NETWORK! 
(apologies
for
shousng)
26

Sharing
is
caring
In
data
science,
we
see
the
benefits
to
teams
for
shared

insights,
storytelling,
etc.

Meanwhile
domain
expertise
is
generally
more
important

than
knowledge
about
tools

There’s
a
value
for
developers
to
use
notebooks
in
lieu
of

IDEs
in
some
cases
–
what
are
those
cases?

GitHub
now
renders
notebooks,
so
they
can
be
used
for

documentation,
reporting,
etc.

Digital
Object
Identifiers
(DOI)
can
be
assigned
through

Zenodo,
making
notebooks
citable
for
academic
publication
27

Authoring
and
Scale-‐Out
28

Achieving
scale
▪ Launchbot.io
allows
a
notebook
author
to

build
a
container
that
includes
the
required

Jupyter
kernel,
installed
libraries,
datasets,

etc.

▪ Install
Docker
on
your
laptop

▪ Backend
uses
Git
and
DockerHub
to
manage

containers

▪ For
scale,
deploy
to
DC/OS
or
a
cloud
30

“A
notebook,
a
container,
and
~20
minutes
 

of
informal
video
walk
into
a
bar…”
31

System
architecture
32

Tutorial

Middleware

Cluster

O’Reilly
Strata

NY,
Sep
25-‐28 
SG,
Dec
4-‐7

O’Reilly
ArXﬁcial
Intelligence

NY,
Jun
26-‐29 
SF,
Sep
17-‐20

JupyterCon

NY,
Aug
22-‐25
33

34
Learn
Alongside 
Innovators
Just
Enough
Math Building
Data

Science
Teams
Hylbert-‐Speys How
Do
You
Learn?
periodic
newslewer
with
updates,
 
events,
conference
summaries…

liber118.com/pxn/ 
@pacoid

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