►
Description
Don’t miss out! Join us at our upcoming hybrid event: KubeCon + CloudNativeCon North America 2022 from October 24-28 in Detroit (and online!). Learn more at https://kubecon.io. The conference features presentations from developers and end users of Kubernetes, Prometheus, Envoy, and all of the other CNCF-hosted projects.
The Future Of Reproducible Research: Powered By Kubeflow - Trevor Grant
Reproducibility has been a cornerstone of scientific research for the last 400 years;, often that means charts or a sample of data. With the advent of Github, sometimes now code and sample data too, unfortunately this still leaves a lot of “leg work” to the person reproducing (which is why this is often a task for graduate students). But Kubeflow, an Open Source, Cloud Native, Data Science Platform, changes that by making all steps from data cleansing to visualization quickly and easily reproducible which in turn makes iterative advances much easier and faster. In this talk, we’ll discuss a peer review article that was published not only with corresponding code, but with a Kubeflow Pipeline, so that anyone may download, check, and iteratively improve the results. While the paper itself is interesting- the talk will focus on why publishing not only code and data but full pipelines benefits not only grad students tasked with verifying results, but the entire academic community.
A
The
future
of
reproducible
research
powered
by
kubeflow
for
kubecon
and
cloud
native
con
europe
2022
about
me.
First
of
all,
thank
you
for
coming
to
my
talk,
as
is
customary
amongst
our
people.
The
second
slide
is
about
me
and
a
quick
highlight
reel
of
my
credentials.
My
name
is
trevor
grant.
I
am
from
the
humble
park
neighborhood
of
chicago
and
this
background
image.
Is
this:
the
skyline
at
the
top
of
the
sledding
hill
in
the
park?
You
get
this
cool
effect
when
the
sun
goes
down,
all
the
buildings
turn
orange.
A
At
any
rate,
I
thought
it
was
cool.
First
and
foremost,
though
I'm
the
pmc
chair
of
apache
mahout
and
while
the
not
the
second
most
important
thing,
a
fun
fact
is:
I'm
also
on
the
project:
mentorship
committee
of
apache
streams
and
apache
community
development,
and
I'm
also
a
member
of
apache
sdap,
incubating
all
cool
projects.
A
Please
check
them
out
and
from
all
this
apache
activity,
you
might
have
guessed,
I'm
also
an
apache
software
foundation
member,
but
wait
there's
more,
I'm
also
author
of
kubeflow
for
machine
learning
from
lab
to
production
available
from
o'reilly
media
and,
lastly,
since
I
believe
our
job
shouldn't
define
us.
Well,
I
guess
I
don't
have
my
jobs
listed.
I've
got
a
couple
of
my
hustles
there
at
the
bottom,
so
I
think
some
versions
of
the
schedule
saw
me
listed
as
working
for
ricto,
but
I
am
not
anymore.
A
A
There's
a
post
about
building
your
own
e-bike
retrieve
from
a
kid
I'm
hoping
the
next
day
or
two
to
have
a
blog
about
how
to
rig
out
of
lights
and
a
horn
kit
working
on
dash
displays
with
raspberry
pi's.
You
can
check
out
the
website
the
blog
the
red
bubble
store,
but
the
key
thing
is:
I'm
doing
this:
to
get
cars
off
the
road
not
to
make
money.
A
So
if
you
want
to
partner
on
something
hit
me
up,
if
you
want
to
see
all
my
work
and
launch
the
next
tesla
v
bikes
cool,
I
mean
maybe
give
me
a
nice
cushy
job
as
an
engineering
consultant,
but
mainly
I'm
just
stoked-
to
get
greenhouse
gases
decreasing
and
cars
off
the
road.
So
a
good
question
is
how
will
this
relate
to
kubeflow
and
the
answer
is
it
doesn't,
but
my
main
hustle,
the
last
couple
months
have
been
this
little
guy.
A
His
stage
name
is
merlin
and
since
I
don't
need
any
internet
rando,
stealing
his
identity,
let's
just
say
he
was
born
around
eight
weeks
ago.
Why
am
I
showing
you
this?
Well?
Obviously,
the
main
reason
is
to
get
some
oohs
and
oz
and
otherwise
endear
myself
to
you
the
audience,
but
also
normally
I
make
jokes
with
my
talks.
A
I
took
a
stand-up
class
with
a
friend
in
early
february
with
the
express
plan
to
make
jokes,
but
I
didn't
get
them
written
and
I
didn't
get
to
test
them
out
at
open,
mic
nights
and
you
might
be
thinking.
What's
that,
I
thought
you
were
just
naturally
funny.
I'm
not.
I
put
a
lot
of
work
into
making
funny
jokes
for
my
talks
a
couple
years
ago.
I
did
a
talk
for
os
odsc
east.
A
I
had
a
bang
and
type
5
that
went
with
that
talk
and
if
any
of
y'all
are
thinking
about
doing
talks,
which
you
should
doesn't
matter
what
level
you're
at
all
the
way
from
junior
dev
to
c-suite,
you
should
be
giving
talks,
and
my
best
advice
is
to
have
five
minutes
worth
of
jokes.
You
can
do
and
an
open
mic
and
then
also
do
them
at
local,
meetups.
A
Okay,
at
any
rate,
the
point
being
is,
I
didn't
get
around
to
writing
any
amazing
jokes,
but
well
here's
what
today's
talk's
gonna
look
like,
I'm
a
little
over
a
week
late.
Turning
in
the
recording
so
some
to
do
slipped
in
production
like
me,
coming
up
with
a
fun
new
title
for
this
slide,
and
also
you
might
have
noticed
that
I
didn't
realize
style.
Is
this
deck
very
much
but
okay?
A
But
the
overview
of
today's
talk,
we're
just
finishing
the
introduction.
Now.
Next,
we'll
lay
out
some
motivation
on
why
all
this
content
is
important
through
clever
use
of
memes
and
wikipedia
articles
we'll
provide
an
example
of
a
pipeline.
We
publish
for
an
article
in
a
peer-reviewed
journal
and
then
I'll
break
you
with
some
calls
to
action,
and
possibly
we'll
have
time
for
conclusions
and
q
and
a's.
A
But
one
thing
I
know
that
I
am
horrible
at
is
estimating
time,
so
I
would
guess
there
will
either
be
no
time
at
the
end
or
there'll
be
way
too
much
time.
There's
only
one
way
to
find
out
by
staying
tuned.
Also,
I
should
be
in
a
chat
room
that
you'll
have
access
to
answering
questions,
so
don't
feel
compelled
to
wait
till
the
end.
A
A
But
now,
let's
progress
on
to
memes,
so
in
theory,
you're
already
motivated
by
this
topic
as
a
coupon
attendee,
since
you've
passed
up
so
many
other
high
quality
talks
to
see
this
one
or
as
an
internet
rando.
You
found
this
on
youtube,
but
since
you're
watching
this,
instead
of
practicing
accordion
or
whatever
other
fun
thing
you
could
be
doing.
A
A
The
so
we
see
here
a
super,
a
simple
google
image
search
for
reproducible
research
memes
will
indicate
how
serious
of
a
problem
the
lack
of
reproducibility
is
or,
if
you
like,
to
commune,
consume
information
by
journal
articles
as
opposed
to
internet
memes.
Here
are
some
other
articles.
I've
stumbled
across
to
illustrate
my
point
note.
The
wikipedia
one
doesn't
count,
however,
what
I
will
be
doing
is
using
the
wiki
wikipedia
article
as
a
framework
for
a
high-level
overview
of
the
problem.
A
A
Psychology
and
medicine
have
the
biggest
problems
with
reproducibility,
but
other
social
and
natural
sciences
are
also
affected.
We'll
talk
about
meta
science
in
a
few
slides,
but
the
punch
line
being
if
you're.
If
you
aren't
convinced
this
is
an
issue,
you
can
google
replication
crisis
and
see
tens
thousands,
tens
of
thousands
of
articles,
videos,
blogs
and
memes
about
why
it
is
so.
First,
let's
talk
about
some
of
the
causes
and
why
we
have
the
crisis
again
basic,
basing
a
lot
of
this
on
the
wikipedia
article,
but
the
commodification
of
science.
A
What
does
that
mean?
Phil
marowski
argues
in
his
2011
book
science
smart,
that
science
is
for
sale
in
a
market
like
other
goods,
and
it
has
commodified
quality
assurances
have
collapsed
and
that
is
to
say
that
as
private
companies
push
to
fund
science,
they're
really
not
investing
enough
in
their
qa
practices.
A
But
it's
not
just
capitals
on
driving
bad
science.
Academia
which
is
notoriously
insulated
from
capitalistic
pressures,
still
pushes
faculty
to
publish
or
perish
that
is
publish
papers
or
look
for
a
new
job,
and
there
is
publication
bias
against
reproducing
prior
work,
which
is
probably
why
grad
students
end
up
gets
getting
stuck
doing
it
more
on
this
later,
then,
there
is
the
case
of
straight
up
fraud
and
deception.
A
This
includes
researchers
not
being
blinded
to
the
control
versus
experimental
groups,
as
well
as
cherry
picking
results
and
all
the
other
things
that
lost
you
points
in
high
school,
but
most
hilariously.
There
was
a
survey
done
among
2000
psychologists
in
2012,
where
90
of
the
respondents
admitted
to
using
at
least
one
questionable
research
practice
in
a
published
work.
A
Then
there
are
statistical
issues.
When
I
was
in
grad
school,
I
had
a
part-time
job
tutoring
at
the
local
community
college,
for
a
statistics
class
being
taught
there.
The
professor
had
students
attempt
to
reproduce
results
from
some
study.
This
girl
came
in.
She
was
working
on
an
article
related
to
physical
therapy,
since
that
was
the
field
she
wanted
to
get
into.
She
was
having
a
hard
time
getting
the
answers.
The
author
came
up
with
to
short
circuit,
a
long
story
which
I've
forgotten
a
lot
of
the
finer
details
of
anyway.
A
The
girl
couldn't
replicate
because
she
was
using
the
correct
standard
deviation
formula
and
the
authors
had
used
the
wrong
one,
the
population
one
in
their
work
and
since
their
results
would
have
been
significant
either
way,
I
don't
think
they
were
being
fraudulent.
I
think
they
were
just
bad
as
statistics
other
times.
Statistics
can
cause
issues
where
there's
only
a
handful
people
being
studied.
A
I
figured
someone
failed
to
reject
a
null
hypothesis
at
95
percent
confidence
interval,
while
there's
still
a
120
chance.
That
was
just
the
luck
of
the
draw
of
the
test
subjects.
That's.
Why
also
why?
Your
statistics
teacher
always
hard
that
you
never
prove
anything
with
statistics.
You
just
fail
to
disprove,
also
known
as
failing
to
reject
the
null
hypothesis,
but
we
grow
up
and
we
forget
these
little
bits
of
semantic
pearls
of
wisdom.
A
So
the
primary
consequence
of
concern
when
someone
produces
an
incorrect
result
is-
and
no
one
detects
it-
is
that
there's
a
risk
that
the
incorrect
result
will
be
canonized
and
other
results
will
be
built
upon
it,
but
there
are
other
consequences
too.
In
so
far
as
political
repercussions.
I
can
speak
more
to
the
issue
in
us
than
europe,
but
anecdotally.
A
A
There
is
a
legitimate
reproducibility
crisis,
but
some
actors
will
exploit
the
crisis
and
say,
for
instance,
that
a
study
from
the
60s
and
70s
that
showing
car
pollution
was
a
primary
cause
of
acid
rain
and
the
data
set's
no
longer
available
ergo.
The
study
can't
be
reproduced
or
go
it's
invalid.
Ergo
car
pollution
doesn't
really
cause
acid
rain.
It
never
did.
I
think
you
can
see
some
of
the
logical
missteps
there,
but
that's
the
short
version
of
it,
but
politicians
in
theory
in
a
republic
are
supposed
to
mirror
the
concerns
of
their
citizens.
A
Someone
starts
crowdsourcing
how
about
house
or
I'm
sorry,
someone
starts
crowing
about
a
study
showing
the
sky
is
blue,
can't
be
replicated
so
people
start
thinking
that
the
sky
isn't
really
blue.
Then
the
sky
is
in
blue
sped's
all
over
twitter
and
facebook,
and
24-hour
news
cycles
pick
it
up
with
some
networks,
claiming
that
science
has
proven
the
skies
in
blue
and
other
networks,
claiming
how
the
sky
is
always
blue
and
clouds
and
nighttime
don't
exist,
and
anyone
who
disagrees
or
thinks
otherwise
is
a
long
list
of
mean
words,
things
really
spin
out
of
hand.
A
We
see
this
play
out,
unfortunately,
almost
weekly,
it
seems
anymore,
but
the
name
calling
doesn't
stop.
With
the
talking
heads
on
cnn
and
fox
news,
the
replication
crisis
gained
the
most
attention
in
psychology.
A
professor
from
princeton
said.
Anyone
that
calls
out
research
that
can't
be
replicated
is
a
methodological
terrorist
and
that
criticism
should
only
be
expressed
in
private
or
by
contacting
the
journals
directly.
A
A
So,
let's
going
back
to
the
odds
of
at
the
five
percent
confidence
interval,
you
have
a
120
chance
of
just
randomly
selecting
people
that
will
show
that
that
effect
exists
when
it
doesn't
so
they're
saying
okay.
Well,
then,
instead
of
the
five
percent
being
the
gold
standard,
the
one
percent
should
be
the
gold
standard,
and
that
would
make
it
so
there's
only
one
100
chance
of
just
seeing
results
by
chance.
A
A
A
The
replication
effort
remedies
in
essence
call
for
more
funding
of
replication
studies
for
students
to
be
required
to
do
more
replication
studies.
I
remember
having
to
do
replication
studies
in
grad
school.
It
was
not
fun
for
many
reasons
which
I
will
spouse
later
in
the
talk.
I,
the
authors
usually
didn't
fully
specify
what
their
data
prep
steps
were,
or
maybe
they
didn't
do
qa
on
their
own
experiment
or
their
data
is
private
and
it
would
be
too
costly
or
impossible
to
reproduce
the
data
set.
A
The
first
thing
in
this
bucket
is
called,
they
say,
use
triangulation,
not
reproducibility,
and
it's
a
good
option.
The
idea
is,
if
x
is
true,
we
should
be
able
to
demonstrate
x
in
a
number
of
ways
in
case
of
research.
Then,
if
some
other
research
asserts
x
is
true,
instead
of
trying
to
reproduce
their
result
or
their
research,
you
should
find
some
other
method
for
testing.
Whether
x
is
true.
A
Another
remedy
in
this
bucket
is
to
stop
using
linear
models
for
everything.
The
reality
of
the
situation
is
not
everything
has
a
linear
relationship
and
researchers
should
feel
more
free
to
try
and
use
other
models.
Besides
linear
regression,
I'm
guessing
a
lot
of
folks
in
this
crowd
would
agree
with
that.
A
Then
there
is
the
remedy,
especially
related
to
publication
bias.
The
replication
should
seek
to
revise
and
extend
current
theories,
not
merely
replicate
them.
For
instance,
if
you
replicate
a
well-known
study,
but
then
you
also
add
an
interesting
finding,
that
would
be
a
more
publishable
paper
than
simply
four
pages
of
saying
yep.
A
It
worked
the
way
the
original
person
said
it
did,
and
finally,
the
remedy
most
cli
most
closely
aligned
to
our
proposed
remedy:
open
science,
open
science
is
sort
of
an
umbrella
term,
around
open
data,
publishing
your
code
to
get
and
other
things,
we're
expanding.
That
and
saying
don't
just
publish
your
code
but
publish
your
full
pipeline.
A
Now
a
slide
based
on
my
experience,
trying
to
repo
reproduce
other
people's
work,
so
everything
doesn't
come
straight
off
a
wikipedia
article.
I
read
as
a
grad
student,
I
remember
reproducing
academic
papers.
The
authors
always
seem
to
leave
out
entirely
any
of
the
data
prep
they
did
or
they
would
try
to
confuse
you
with
lots
of
formulas
and
candidly.
I
did
a
lot
of
that
too,
when
I
was
doing
working
on
my
mba.
A
A
But
the
programmer
who
I
love
most
of
all,
the
one
who's,
the
worst
about
throwing
trash
over
the
fence
to
be
sorted
out
is
past
trevor
me
from
the
future
hates
the
me
of
today.
Just
like
I
loathe
trevor
of
2019,
but
especially
when
I
open
up
some
code
that
trevor
19
was
working
on
and
spent
three
days
trying
to
figure
out
what
the
hell
he
did
and
why
I
recently
ran
in.
I
recently
had
another
run
in
with
past
trevor,
which
I
will
use
as
an
illustrative
example
in
this
now.
A
Oh,
what
we
did
so
I'm
gonna
give
a
short
version
of
this
talk,
but
if
you're
in
valencia
definitely
head
over
on
wednesday
to
see
holden
carroll
give
the
full
version
of
this
talk.
It's
also
chapter
nine
in
a
book
that
I'll
be
plugging
later
on
and
a
peer-reviewed
article,
which
I'll
also
be
plugging
later
on.
A
A
A
A
So
march,
2020
people
are
dying:
spain,
italy
and
new
york.
Hospitals
are
being
overrun
with
coved.
Now
you
can
see
here.
The
dates
are,
maybe
you
can't
really
see
it,
but
the
date
was
march.
28
2020.
This
guy
is
wearing
plastic
wraps
with
some
sort
of
cootie
shield
looks
silly
now,
but
it
was
scary
back.
Then
nobody
really
knew.
What's
going
on,
nobody
knew
yeah,
not
everyone
had
covet.
It
took
a
long
time
to
get
a
test
results
back.
This
was
another
one.
From
march
28th
of
2020
pcr
tests
take
three
days
return.
A
A
We
kind
of
take
for
granted
that
you
can
just
get
a
15-minute
test
down
but
like
that
wasn't
the
case
in
the
2020s
and
but
that
was
compounding
the
issues
with
hospitals
being
overrun,
because
everyone
who
comes
to
the
hospital
thinking
they
have
covet
doesn't
actually
have
covet.
But
you
had
to
sit
there
for
three
days
waiting
for
your
tests
to
come
back
here.
We
see
something
for
march
23rd,
creativity
and
creativity
and
finding
new
ways
to
rapidly
detect
covid
with
equipment
already
available
at
the
hospital
was
a
premium.
A
A
More
on
why
ct
scans
are
great,
or
at
least
people
were
thinking.
They
were
especially
back
in
the
early
days
of
covid,
but
ct
scans
have
issues
the
biggest
ones
being
the
radiation
that
they
hit.
A
typical
thoracic
ct
scan,
which
is
a
cc
scan
of
your
chest
region,
which
is
how
they
would
diagnose.
Covid,
gives
you
about
6.1
millisieverts
of
radiation,
there's
a
chart
to
put
that
in
perspective.
A
It's
not
horrible,
but
it's
pretty
high
for
diagnostic
procedure
and
then
another
metric
to
put
in
perspective
over
the
course
of
your
entire
life.
You're
really
only
supposed
to
get
about
400
millisieverts
of
radiation,
so
yeah
now
low-dose
ct
scans
have
been
around
since
the
90s
in
early
odds,
and
they
were
developed
for
diagnostics,
explicitly
for
detecting
lung
cancer
or
early
lung
cancer.
A
Now
you
can
get
computers
with
500
gigabytes
of
ram,
but
they're
kind
of
pricey
to
rent
and
we're
wizards
of
open
source.
So,
let's
do
something
else
now
the
data
source
that
I
used
was
there
was
this
brazilian
radiologist
who
got
ct
scans
from
10
patients
from
moveon
china,
post
them
coronacases.org.
A
Since
then
they
have
messed
with
the
metadata.
So
you
can't
pull
these
these
images
anymore.
You
can
see
them
at
coronacases.org,
but
if
you
try
to
download
them,
they
don't
work
right
anymore,
so
or
at
least
in
my
experience,
but
that's
a
ground
glass
occlusion
that
I
think
I'm
not
a
radiologist.
So
let
me
also
be
very
upfront
about
that.
A
A
A
So
the
pipeline
the
gist
of
what
happens
here.
S3
buckets,
have
dicom
images,
pi,
dicom
downloads
or
I'm
sorry.
The
s
the
dicoms
are
loaded
onto
a
persistent
volume
claim.
Pi
dicom
turns
them
into
a
numerical
matrix,
which
is
loaded
into
spark
and
loaded
into
an
rdd
which
is
then
wrapped
by
the
reason
we're
using
mahout
we'll
talk
about
a
bit
later.
Mahout
has
a
distributed,
stochastic
singular
value
decomposition
which
in
essence
allows
us
to
invert
the
matrix
and
get
two
matrices
out
which
allows
us
to
do
our
denoising,
s3
buckets.
A
A
You
don't
have
read,
write
many
options
on
persistent
volume
claims
and
I'm
going
to
assume
that
this
is
a
kubecon
and
everyone
here
is
somewhat
familiar
with
kubernetes.
You
know
what
that
means
spark
when
it's
writing
it's
going
to
be
writing
from
every
executor.
So
you
have
to
have
an
s3
bucket
to
read
and
write
from,
and
that's
why
we
use
s3
buckets.
You
do
take
a
performance
hit
when
you're
using
s3
buckets
just
as
a
note,
but
if
you've
got
to
use
spark,
that's
usually
a
good
solution.
A
A
There
can
be
a
number
of
fun
and
unique
challenges
about
doing
spark
on
kubernetes
I'll.
Let
you
figure
that
out
in
your
own
adventure.
Mahout
is
a
library
that
goes
on
top
of
spark
sparks
support
for
matrices.
It
does
have
matrix
support
but
not
distributed
matrix.
Support
and
mahou
does
have
distributed
matrix
support
and
I'm
a
maintainer
of
mahou.
So,
of
course,
I
wanted
to
rope
that
into
this
and
do
my
talk
in
my
book
and
my
journal
article
and
everything
else.
So
there
we
go
distributed,
singular
value.
A
A
A
That's
that
the
a
big
point
of
this
is,
if
you
want
to
know
how
to
do
this
with
kubeflow.
That's
a
chapter
9
example
in
the
book
that
some
friends
and
I
wrote
if
you
want
to
read
the
article
about
why
I
was
doing
all
these
things.
It's
free
and
open
article
on
noble
research
feel
free
to
check
it
out
and
yeah
so
and
once
again,
or
if
you
just
want
to
see
the
talk,
go
check
out.
A
Holden's
talk
on
on
wednesday
at
17
25,
if
you're
in
valencia
or
I'm
sure,
it'll
exist
somewhere
on
this
youtube
channel
as
well.
I
yeah
okay.
So
what
when
I
was
working
in
a
ritto,
I
dusted
off
the
old
code
and
made
sure
it
still
worked.
It
does
one
issue
that
I
hit
was
the
data
set
had
changed,
but
but,
as
I
was
talking
about
earlier,
the
metadata
dicoms
didn't
load
right.
A
So
let's
say
that
you
have
some
but
here's
another
thing.
Let's
say
you
have
some
deep
learning
that
you
wanted
to
use
to
detect
kobit
cool
with
kubeflow.
You
can
just
add
a
step
at
the
end.
That
does
that.
But
what,
if
you
wanted
to
detect
lung
cancer
cool,
you
can
just
add
a
step
at
the
end
with
that.
What,
if
you're
a
hospital
that
like
given
a
low
dose
ct
scan
you
want
to
check
for
cova
and
lung
cancer
and
tuberculosis.
A
You
can
just
add
three
steps
at
the
end
and
what,
if
you
have
an
idea-
and
you
want
to
see
if
denoising
helps
neural
necks
nets,
detect
some
sort
of
malady?
You
can
feed
an
arbitrary
image.
Have
your
algorithm
look
for
the
melody
at
the
raw.com
and
then
again
on
denoise.com,
the
point
being
since
I
published
a
pipeline,
you
could
spend
your
time
extending
my
results
as
opposed
to
trying
to
figure
out
how
the
heck
my
code
ever
worked
in
the
first
place,
so
calls
to
action.
A
Assume
replication
so
assume
someone
somewhere
someday.
Possibly
you
will
need
to
replicate
your
study,
be
a
cool
person,
help
them
by
documenting
all
the
steps
in
your
paper.
That
would
be
that's
one
call
to
action
and
you
might
say
well
that's
why
I
publish
my
code
and,
I
would
say,
okay,
cool
and
if
this
were
an
academic
conference,
I
let
it
go
with
that,
but
by
virtue
of
the
fact
that
you're
a
kubecon,
I'm
going
to
assume
a
that
you
get
this
joke
and
b
that,
by
extension,
understand
why
publishing
code
isn't
enough.
A
In
case
you
don't.
Let
me
very
explicitly
lay
out
some
reasons:
the
environment,
being
the
environment
being
different.
Perhaps
whatever
package
you
use
to
calculate
standard
deviation
was
doing
it
wrong
or
worse,
yet
what
if
they
fix
it
later
or
what?
If
it
used
to
be
correct,
but
then
some
regression
was
introduced.
The
point
being
is
software
is
all
living,
not
documents,
but
it's
a
living
thing
and
it
can
change
over
time.
A
A
This
is
wild
and
will
make
you
forever
doubt
the
accuracy
of
any
of
your
analysis
ever
so,
maybe
don't
look
it
up,
but
low
background
steel,
cool
stuff
and
a
good
reason
of
why
sometimes
computers
don't
work
the
way
you
would
hope
they
would
so.
I
touched
on
this
in
the
comic
in
the
last
slide,
but
your
laptop
environment
is
like
a
snow
clay
snowflake.
It's
very
special
and
unique,
like
the
billions
of
other
laptops
or
snowflakes.
A
No
one
will
ever
create
another
environment
quite
the
same
ever
again,
so,
instead
of
using
just
requirements.text
and
calling
it
a
day
make
a
docker
container,
which
will
also
version
lock,
in
addition
to
breaking
changes
that
might
be
introduced
between
tensorflow
1,
4
and
2.0,
there's
also
a
thousand
little
things
that
happen
in
the
background
of
your
computer,
what
version
of
cuda
drivers
are
you
running?
What
version
of
flavor
of
linux?
Are
you
running,
etc,
etc?
So
docker
containers
help
make
your
code
a
lot
more
reproducible.
A
Assuming
someone
will
want
to
replicate
your
work
and
that
they
don't
have
access
to
your
machine.
Kubeflow
provides
a
nice
framework
for
reproducing
results.
Now
that
said,
what
is
kubeflow
and
how
will
it
help
from
the
website?
Kubeflow,
the
kubeflow
project
is
dedicated
to
making
deployments
and
machine
learning
workflows
on
kubernetes,
simple,
portable
and
scalable.
Our
goal
is
not
to
recreate
other
services
but
to
provide
a
straightforward
way
to
deploy
best
of
breed
open
source
systems
for
ml
to
diverse
infrastructures.
A
So
if
you're,
using
kubeflow
just
for
doing
reproducible
research,
it's
probably
been
overkill
candidly
kufu
does
bottle
serving
you
can
bolt
on
feature
stores,
metadata,
tracking
and
host
of
other
things.
But
if
your
goal
is
just
for
reproducing
research,
you
probably
won't
need
all
that.
However,
it
is
easily
deployable
on
various
cloud
providers,
so
there
may
be
some
value.
Add
there
and
again,
I
am
no
longer
associated
with
rikdo,
but
they
have
a
cloud
deployment
that
runs
for
about
fifty
percent.
It's
not
fifty
cents
an
hour
or
you
can
use
ubuntu's
charm
kubeflow.
A
Those
are
gonna,
be
probably
your
two
easiest
ways
to
get
kufo
up
and
running
and
then
the
pipeline
itself.
You
can
just
download
as
a
zip
file
and
publish
also
to
get
repository,
so
this
advice
was
earned
over
an
untold
number
of
hours.
Trying
to
do
couple
installs,
it's
a
it
can
be
a
very
painful
process
and
your
time
is
probably
best
spent
if
you've
got
the
50
cent.
A
So
in
conclusion,
reproducibility
is
a
cornerstone
of
science.
So
do
everyone
else
a
favor
and
make
your
sciencing
uber
reproducible
for
more
information
on
kubeflow?
Obviously
you
should
buy
our
book.
We
make
tens
tens
of
cents
on
for
each
copy
sold
also
check
out
the
paper
and
if
you
feel
like
doing
some
cool
stuff
with
dicom
images,
you
can
extend
my
research
and
cite
me
and
I
don't
get
paid
for
that
either.