1.
Computable
Content:
Lessons
Learned
Paco
Nathan
@pacoid
Director,
Learning
Group
@
O’Reilly
Media
2017-­‐05-­‐25

2.
An
observation
2

3.
Oriole
3

4.
One
approach
4

5.
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

6.
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

7.
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

8.
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

9.
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?

10.
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

11.
Long-­‐term
goal:
make
learning
materials
more
powerful
by
integrating
compute
engines
+
data
services
11

12.
Project
Jupyter
12

13.
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

14.
Projects
14
▪ 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

15.
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
15

16.
A
suite
of
network
protocols
16
Code%runs%
in%a%REPL
Kernel
Edi0ng%+%
Results
Notebook
Network
Protocol

17.
History,
Context
17

18.
Notebook
metaphor
Wolfram
Research
introduced
notebooks
in
1988
for
working
with
Mathematica
18

19.
Related
work
19

20.
Literate
programming
Don
Knuth
literateprogramming.com/
Paraphrased:
Instead
of
telling
computers
what
to
do,
tell
other
people
what
you
want
the
computers
to
do
20

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
21

22.
Best
Practices
22

23.
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
23

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
24

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)
25

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
26

27.
Authoring
and
Scale-­‐Out
27

28.
Launchbot.io
28

29.
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
29

30.
“A
notebook,
a
container,
and
~20
minutes
of
informal
video
walk
into
a
bar…”
30

31.
System
architecture
31
Tutorial
Middleware
Cluster

32.
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
32

33.
33
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