►
Description
02:45 Streamlined machine learning workflows with Argo and Ploomber - Eduardo Blancas
18:45 Automating computer vision workflows with Onepanel and Argo - Rush Tehrani
40:30 Argo Workflows and Events Survey Results 2021 - Alex Collins
http://bit.ly/argo-wf-cmty-mtng
A
There
we
go
good
morning.
Thank
you
for
coming
to
the
argo,
workflows
and
events
community
meeting
here
on
the
17th
of
march
2021,
I'm
just
going
to
give
you
a
quick
overview
of
what
we're
going
to
do
today.
We've
got
two
presentations
today.
The
first
one
is
from
eduardo
blancas
and
it's
going
to
be
on
a
streamlined
machine
learning
using
our
go
with
argo
workflows
with
plumber.
A
A
They
are
basically
a
small
ecosystem
of
cloud
native,
highly
scalable,
workflow,
automation
tools,
argo
events
is
used
for
for
triggering
workflows,
typically
or
other
actions
on
kubernetes
and
workflows
is
typically
used
for
large
workflows
on
kubernetes
myself,
I'm
one
of
the
under
the
principal
engineer
on
argo,
workflows,
narco
events,
and
we
have
a
couple
of
other
argo
core
engineers
here
as
well.
If
you
want
to
ask
questions,
this
is
a
great
opportunity
to
pick
the
brains
of
people
who
work
on
this
every
day.
A
If
you've
got
some
colleagues
who
you
think
will
might
be
interested,
I
do
try
and
make
sure
that
all
the
time
stamps
are
in
those
videos.
So
you
can
just
fast
forward
to
the
particular
piece
of
interest
to
you
as
well.
Okay.
So
that's
all
the
preamble
done
with
all
the
introduction
done
with
eduardo.
Are
you
ready,
yeah,
yes,
okay,
I'll
hand
over
control
to
you
now.
B
All
right
so
thanks
everyone
for
joining
today,
and
thanks
alex
for
inviting
me
to
present
my
project.
So
as
alex
mentioned,
I
am
going
to
be
speaking
about
plumber,
which
is
a
project
that
I've
been
working
on
for
a
little
bit
over
a
year
and
specifically
how
plumber
integrates
with
argo
for
lean
machine
learning
workflows.
B
Just
some
background
about
me.
I've
been
doing
data
science
and
machine
learning
for
the
last
six
years
in
projects
spanning
working
with
government,
some
academic
research
and,
more
recently
in
industry
and
the
last
few
years
I've
been
focusing
on
the
deployment
of
machine
learning
in
business,
critical
settings
in
the
financial
industry.
B
B
We
usually
data
scientists,
we
usually
use
a
combination
of
jupiter,
notebooks
scripts
functions
and
frameworks
like
pytorch
or
scikit-learn,
and
when
we
want
to
take
our
mods
to
production,
we
we
use
a
production
already
system
like
our
workflows
in
this
case,
and
one
of
the
problems
is
that
the
difference
in
these
tools
force
us
to
change
our
code.
If
we
want
to
run
it
in
a
production
environment.
B
So
we
may
want
to
take
our
notebooks
and
scripts,
and
if
we
want
to
make
predictions
using
our
workflows,
we
may
have
to
change
the
code
right.
For
example,
if
we
have
a
node
we're
going
to
have
to
convert
it
into
a
script
and
maybe
expose
a
command
line
interface,
so
we
can
call
it
as
a
step
in
another
workflow,
and
this
is
a
really
risky
process,
because
the
moment
we
start
moving
different
pieces
to
our
pipeline,
the
pipeline
that
we
were
developing
locally
or
in
a
single
server.
B
We
may
break
things
and
it
usually
happens
that
when
we
start
moving
a
lot
of
things,
we
are
not
able
to
reproduce
our
results.
Maybe
we
have
a
certain
quality
metrics
about
our
model
and
now
we
compute
them,
and
they
are
no
longer
true
because
we
move
a
lot
of
things.
We
don't
know
exactly
at
which
point
we
broke
the
model
and
even
before
we
make
a
deployment,
we
may
want
to
use
a
system
like
our
workflows
for
training.
B
B
So
it's
it's
a
combination
of
these
two
ideas
deployment,
but
also
using
these
four
training
models
on
a
larger
scale,
and
as
I
mentioned
these
things,
when
we
start
modifying
our
code,
we
may
break
our
model
and
on
top
of
this,
this
is
not
something
that
we
don't
have
to
do
once
right.
Every
time
we
change
our
model.
Every
time
we
find
something
that
we
can
improve,
or
maybe
we
found
an
error
in
our
pre-processing
steps.
We
have
to
modify
the
pipeline
and
we
have
to
deploy
again.
B
B
Now
that's
the
motivation
behind
plumber.
The
idea
is,
that
is,
to
provide
see
a
simple
api
and
development
and
a
good
development
environment
so
that
we
can
use
whatever
tool
we
like
if
we
like
python
scripts,
if
we
like
jupiter
notebooks,
if
we
are
doing
deep
learning
with
pytorch
or
classical
machine
learning
with
scikit-learn,
whatever,
whatever
tools
we
like
and
make
us
the
most
productive,
we
should
use
those
and
then
do
not
force
us
to
make
a
lot
of
changes
in
order
to
run
this
in
production.
B
That's
the
that's
the
main
motivation
for
bloomberg
and
one
of
the
main,
the
main
features
of
bloomberg
is
to
offer
a
really
simple
api.
The
reason
is
that
when
we
are
doing
machine
learning,
we
don't
know
what's
going
to
work,
so
we
are
experimenting
a
lot
of
things.
We
try
different
models.
We
may
try
different
pre-processing
steps,
so
there
should
be
an
easy
way
for
us
to
modify
our
pipeline
and
see
if
we
are
making
progress.
It's
like
a
really
experimental
kind
of
projects
in
machine
learning.
So
this
is
the
api.
B
It's
actually
really
simple,
similar
to
argo
workflows.
We
just
have
a
general
file
and
the
only
two
things
that
we
have
to
specify
is
where's
our
source
code.
So
in
this
case
we
have
a
task
which
is
a
function
in
a
module
called
tasks
and
a
function
called
get
and
it
saves
an
output
here
and
we
just
repeat
the
same
logic
for
every
every
task
in
our
pipeline.
We
generate
some
features.
B
So
now,
just
to
make
things
more
clear,
I
am
going
to
show
a
demo
and
you
can
take
a
look
at
the
code
here
if
you
want
okay,
so
oh
first,
there's
there's
a
repo
with
all
the
examples,
and
actually
you
don't
have
to
install
anything.
So
I'm
showing
the
repo
with
examples.
You
can
click
here
or
here,
and
this
is
going
to
open
a
jupiter
lab
environment
and
you
can
just
try
the
examples
here
or
if
you
want
to
run
locally
you
can
you
can
use
these
commands
okay.
B
B
B
Okay,
so,
as
you
can
see,
the
top
one
is
the
training
pipeline,
they
look
really
similar
and
they
should
be
when
we
are
training
a
model.
We
get
historical
data.
We
generate
some
features
the
three
tasks
here
in
the
middle,
and
then
we
train
a
mod
when
we
want
to
make
predictions.
We
get
new
data,
maybe
data
that
was
generated
in
the
in
the
previous
month
or
week
or
even
one
day.
B
This
is
a
program
called
training
serving
sku,
and
this
happens
when
the
pre-processing
processing
steps
don't
match
and
plumber
offers
a
way
to
compose
pipelines.
So
you
you
can
make
sure
that
this
is
happening.
Okay,
so
I'll
just
keep
one
of
these
here
and
I'm
going
to
show
how
this
looks
like
in
code.
B
This
is
optional
and
the
reason
why
I'm
importing
tasks
from
a
different
file
is
because
I
want
to
use
this
for
training
and
serving,
but
you
could
just
have
everything
here
and
then.
Finally,
I
am
going
to
train
them
all
using
this
script
right
and
this
generates
this
pipeline.
Now,
if
we
take
a
look
at
this
serving
pipeline,
we
are
going
to
see
it's
really
similar
right.
This
is
a
training
pipeline.
This
is
a
serving
pipeline.
B
We
are
also
importing
the
features.
So
these
three
tasks
we
are.
Instead,
we
are
getting
some
new
data,
let's
say
data
from
the
last
month,
and
then
we
are
using
a
previously
trained
model
to
make
a
prediction.
So
we
are
saying,
use
this
model
and
save
the
predictions
here
now,
let's
take
a
look
at
the
actual
code.
This
is
just
the
declaration
of
the
pipeline.
B
I'm
going
to
open
the
task
that
gets
some
data.
It's
it's
really
simple.
All
you
have
to
do
is
to
declare
this
as
a
function
and
you
can
use
whatever
framework
you
want
inside
the
function.
The
only
requirement
is
to
follow
this
convention.
You
should
have
a
product
argument
and
you
should
use
this
this
value
to
save
your
output,
so
just
getting
some
sample
data
and
using
this
product
argument
to
save
my
output.
B
Okay
here,
a
really
similar
thing,
just
one
new
thing
that
we
have
an
upstream
argument
now
I
am
saying
that
the
sample
area
task-
the
one
here
has
to
use,
get
the
output
of
get
as
input.
So
all
I
have
to
do
is
say
upstream,
get
and
plumber
is
going
to
use
these
references
to
build
the
pipeline,
so
you
don't
have
to
explicitly
state
dependencies.
B
B
This
is
one
of
the
important
features
to
run
these
pipelines
locally.
Sometimes
before
we
run
these
inaugural
workflows,
we
may
want
to
make
sure
that
the
pipeline
actually
runs.
We
may
be
adding
some
features
or
adding
some
pre-processing
steps.
So
if
we
run
this
pipeline
with
a
sample,
let's
say
one
percent
of
the
data,
it's
gonna
run
really
fast
and
it's
gonna
make
sure
that
it
it
runs.
B
B
This
is
just
with
a
simple
command,
how
you
run
it
and
I'm
going
to
show
the
output
just
to
this
is
also
a
nice
feature
that
plumber
has
so
I'm
going
to
show
the
output
of
the
training
step.
So
let's
go
back
to
the
pipeline
declaration,
so
I'm
saying
I'm
going
to
use
this
script
and
then
generate
the
model
and
an
html
report.
B
After
this,
you
want
to
train
a
model,
but
you
also
want
to
know
if
the
model
is
good
right.
So
this
is
how
it
works.
Every
time
you
you
use
a
script,
that's
the
difference
between
using
a
function
and
a
script.
If
you
use
a
script,
plumber
is
going
to
convert
this
to
a
notebook
first
and
then
it's
going
to
execute
it.
So
you
have
your
fit
script
here
on
the
left
side,
and
here
you
have
the
output
version.
You
can
hide
the
code
if
you
want,
but
this
is
really
really
handy.
B
You
can
just
train
your
model
and
then
get
this
nice
report
back
and
take
a
look
at
the
numbers
and
the
metrics,
and
you
don't
have
to
save
any
any
other
files.
Any
extra
files.
You
can
have
a
single
standalone,
html
report,
so
this
has
been
really
convenient
for
for
me
when
I
train
multiple
models
and
I
want
to
find
which
is
the
best
one,
so
that's
just
how
it
works.
Luckily,
now
I'm
going
to
play
a
video
that
shows
how
to
it
runs
in
ago
just
to
make
things
smoother.
B
This
is
the
same
example
that
I've
been
showing
same
code.
So
the
first
thing
I
do
is:
I
run
this
supervisor
export
code.
This
is
another
package
that
we
are
developing
and
this
takes
care
of
exporting
to
argo.
So
you
can
see
I
just
run.
I
just
run
this
command
in
the
project
folder
and
then
it
generates
the
general
spec
to
run
to
use
in
argo
workflow.
So
you
can
see
on
the
left
side
that
I
got
the
the
jmo
spec
and
then
I'm
going
to
run
this
this
pipeline.
B
B
Then
it
should
run
the
task
somewhere
here
here.
Here
runs
a
task
and
then
it's
gonna
start
running
the
upcoming
task,
which
is
the
which
are
the
two
that
generate
the
features
here,
and
it's
really
simple.
So
you
only
have
to
export
from
this
export
command
and
it
generates
that
the
channel
spec
for
you.
B
Another
thing
that
I
forgot
to
mention
that
it's
also
really
useful
when
you
are
working
locally,
is
the
interactive
console?
So
if
you
do
plumber
interact,
you
get
a
an
interactive
command
interface
and
you
can
get
your
pipeline.
You
know
back
variable,
so
you
can
tell
ask
which
are
the
tasks
in
this
pipeline.
B
B
B
So,
if
I'm
looking
for
early
adopters
and
contributors,
so
if
you
are
interested
in
being
part
of
this
project,
please
reach
out
to
me
you
can
pip
install
plumber.
The
code
is
on
github
and,
as
I
mentioned
before,
there's
a
repo
with
some
examples.
You
can
run
this
in
the
command
line.
You
can
also
use
binder,
which
is
going
to
set
up
a
jupyter
lab
environment
for
you,
and
you
can
try
that
with
no
setup
you
don't
have
to
install
anything.
So
that's
it.
I
think
we
have
a
few
minutes
for.
A
I
think
dan's
saying
great
presentation
so
eduardo
it
doesn't
sound
like
we
have
too
many
questions.
So
thank
you
very
much
for
coming
and
doing
that
presentation
for
us
today.
I
really
enjoyed
it
I'd
like
to
hear
about
the
different
use
cases,
particularly
machine
learning.
So
that's
great.
Thank
you
very
much.
A
C
Perfect,
hey
everyone,
I'm
rush
tirani
and
thank
you
for
having
me
and
the
one
panel
team,
and
and
thanks
alex
for
inviting
us
to
have
this
presentation
today,
I'll
be
talking
about
automating
computer
vision,
workflows
with
one
panel
and
argo
so,
and
just
so
you
know
just
so.
You
know
who
I
am
I'm
actually
one
of
the
founders
and
the
creators
of
one
panel,
one
of
the
developers,
and
we
have
other
developers
core
team
members
on
this
call
as
well.
C
So
just
to
know
what
to
give
you
an
idea
of
what
one
panel
is.
We
call
one
panel,
it's
an
open
and
extensible
ide
for
computer
vision
and
the
way
it
works
is
we
provide
built-in
tools
for
the
entire
ml
life
cycle
for
specifically
for
computer
vision,
and
then
one
thing
we
do
on
top
of.
That
is
not
only
you
have
these
built-in
tools.
D
C
We
have
you
know
one
panel
kind
of
covers
a
big
subset
of
it,
and
you
have
the
appropriate,
like
the
data
preparation,
that
you
have
to
prepare
kind
of
stage
of
the
whole
life
cycle,
where
you
use
computer
vision,
annotation
tools
or
annotation
tools.
You
do
automatic
annotation
in
that
in
this
phase,
and
then
you
also
do
data
augmentation
pre-processing
for
the
build,
and
then
the
next
step
is
actually
building
your
models.
C
And
in
that
case
you
are,
you
know
using
jupiter
lab
and
vs
code
workspaces
and
you
could
use
our
built-in
algorithms
or
bring
your
own
as
always,
and
you
can
also
bring
in
your
own
tools
as
well
into
this
whole
pipeline
again.
I'll
show
this
in
a
demo
and
then
finally,
you
not
one
of
the
other
steps,
is
training
and
tuning
your
models.
C
So
we
have
the
training
pipelines
and
the
parameting
tuning
pipelines,
which
allows
you
to
different
yeah
use,
different
hyper
parameters
for
your
models
as
your
two
as
you're
training
them,
and
then
you
can
again
run
these
on
multiple
gpus
in
parallel,
as
you
like,
and
then.
Finally,
you
can
actually
do
a
visualization
of
how
your
model
is
being
trained
as
training
and
and
how
it's
being
tuned
as
well.
C
And
then,
finally,
you
want
to
deploy
the
model
which
you
can
do
and
also
what
you
also
want
to
do.
Is
you
want
to
kind
of
orchestrate
this
entire
workflow
and
automate
it?
So,
for
example,
you
want
to
come
back
and
have
it
have
your
annotators
kind
of
do
the
annotation
use
the
model,
that's
been
trained
for
pre-annotation
and
so
on,
so
you
can
actually
automate
this
entire
workflow
that
we
have
in
place
as
well,
and
just
so
you
know
this.
This
whole
process
is
very
continuous
and
iterative.
C
C
C
We
also
use
it
for
data
pipelines
and
training
pipelines,
and
then
we
also
use
a
vargo
for
orchestrating
that
end-to-end
workflow
I
just
mentioned
earlier.
We
also
have
tensorflow
and
pi
torch,
which
are
the
deep
learning
frameworks
and
again
you
can
bring
in
your
own
tools
and
your
own
frameworks
in
here
as
well.
C
So
just
a
quick
overview
of
why
we
picked
argo
versus
some
other
options.
Really
our
requirements,
for
it
should
be
kubernetes
native.
That
was
a
hard
requirement
because
it
goes
with
our
goals,
proven
scalability.
You
know
organizations
actually
using
the
the
product
at
scale,
active
community
and
speed,
speed
of
bug,
fixes
and
new
features
which
you
can
see
by
looking
at
github
and
argo
checked
all
those
requirements.
C
Airflow
was
a
no-go
for
us
because
it's
not
kubernetes
native.
You
can
write
it
in
kubernetes
as
an
operator,
I
believe,
but
you
can't
declare
your
workflows
as
yaml
kubernetes
in
native
yaml
and
tekton
looked
great
overall,
but
it's
very
much
specifically
designed
for
cice.
Even
if
you
go
talk
to
the
developers,
that's
what
they
say
and
argo
is
more
general
purpose
as
a
more
general-purpose
workflow
engine.
C
So,
as
I
mentioned
earlier,
there's
you
know:
data
preparation,
there's
the
entire
life
cycle,
that's
in
one
panel
and
where
it
usually
starts,
is
with
annotation
and
just
to
give
you
an
idea.
Basically,
what
you
do
with
annotation
is
you're
trying
to
come
up
basically
annotate
and
label
issues
in
let's
say
for
object,
detection
or
semantic
segmentation
in
this
case
we're
trying
to
identify
potholes.
This
is
an
actual
real
use
case
and
we
are
annotating
that
in
cvat
to
make
that
happen.
C
Some
of
the
other
use
cases
for
one
panel,
just
out
of
the
community,
are
defects.
Detection
in
manufacturing,
object,
detection
for
robotic
arms
and
tree
damage.
Detection
is
another
one
where
you
know
insect
damage.
Detection
for
entries
base
be
caused
by
rhino.
Beetle
is
another
one.
So
a
lot
of
it
is
a
lot
all
about
object,
detection
and
semantic
segmentation.
This
is
a
semantic
segmentation
example.
C
If
you
look
at
this
one,
this
other
task
here
we're
trying
to
identify
guardrail
damage,
so
this
one
is
not
damaged,
but
there's
other
ones
that
are
here
that
are
damaged
this
one's
obviously
damaged
and
what's
great
about
this
whole
environment,
is
that
once
you
do
your
annotation,
you
can
actually
easily
train
it
from
from
right
in
your
cvat
environment.
This
is
enabled
by
a
python
sdk,
so
you
can
see
the
the
training,
workflows
and
pipelines
that
are
available
in
the
system.
C
C
Your
data
has
a
hyper
primary
tuning
which
again
I'll
show
you
in
more
detail
on
how
what
that
looks
like
and
then
finally,
is
the
metrics
writer
which
writes
some
metrics,
and
you
can
even
add
something
to
send
notifications
in
slack
when
a
training
is
complete
and
one
feature
that
we
have
that's
kind
of
nice
is,
you
can
actually
define
these
parameters
with
different
types.
So
argo
has
the
parameters,
but
we
also
add
these
different
types,
so
you
can
have
a
yaml
type
where
you
can
define
your
pre-processing
parameters.
C
In
this
case,
you
can
define
how
your
hard
parameter
tuning
is
done
like
in
the
search
space
and
all
the
all
the
information
in
regards
to
hyper
camera
tuning.
You
can
put
it
in
as
the
ammo,
and
we
also
have
this
north
pole,
node
selector
module,
where
you
can
add,
as
many
of
these
as
you
like,
and
have
each
task
in
your
pipeline
be
kind
of
run
on
different
different
machines.
For
example
pre-processing
you
don't
want
to
necessarily
run
it
on
cpus,
but
training.
C
So
if
you
execute
this
workflow,
it's
going
to
take
a
little
bit,
so
I've
actually
got
a
pre-launched
one
for
you,
so
we
can
take
a
look
at
it
together,
and
so,
as
I
mentioned
earlier,
you
have
the
pre-processing
hyper-primer
tuning.
What's
really
interesting
about
this,
this
hyperparameter
tuning
or
this
this
procure
task
is
obviously
you
have
the
logs,
as
you
would
get
those
with
argo,
but
there's
this
concept
of
what
we
call
interactive
sidecars,
which
allows
you
to
bring
in
different
visualization
tools.
C
C
You
have
the
tensorboard,
which
is
kind
of
showing
your
metrics
as
it's
as
things
are
training
and
that
and
then
you
can
also
bring
in
what
we
have.
We
call
glasses,
which
is
already
integrated,
and
this
actually
allows
you
to
see
that
how
to
use
your
resource
view
usage
for
that
particular
task
as
it's
as
it's
training.
C
In
the
end,
when
this
f
is
finally
trained,
it
creates
a
model
like
the
best
model
out
of
this
possible
models
that
you
could
you
can
different,
depending
on
different
parameters.
It
creates
the
best
model
for
you
and
you
can
actually
take
that
model
and
bring
it
back
into
the
annotation
tool.
I
just
showed
earlier
and
what's
great
about
our
platform,
is
you
can
essentially
grab
stuff
from
object,
storage
and
push
them
into
any
kind
of
any
of
these
workspaces?
C
C
C
And
select
that
model
for
automatic
annotations
in
case
in
this
case,
it's
a
mask:
rcnn
object,
detector.
Now,
instead
of
your
people
going
in
and
doing
this
manually,
every
time
you
can
use
the
same
model
that
you're
training
for
object,
detection
or
semantic
segmentation
to
also
pre-annotate
your
data,
so
that
costs
that
cuts
a
lot
of
that
manual
processes
down,
and
you
know
that
again
cuts
down
the
whole
end-to-end
process
that
we
have
here.
C
C
We
use
our
google
workflow
to
actually
deploy
these
and
pause
these
and
resume
these,
and
you
can
actually
switch
this
between
cpus
and
gpus,
which
is
again
if
you're,
like
let's
say
using
jupiter
lab
to
do
your
you're
building
your
code
in
jupiter
lab.
You
can
switch
to
a
gpu
machine
and
do
your
test
there.
C
And
we
just
created
a
workflow
template
in
one
panel.
It's
a
python
defined
workflow
template,
so
it
just
got
created
by
that.
Just
that
cooler
example
I
just
showed
earlier-
and
now
it's
also
running
the
workflow
as
well,
and
then
that
last
but
not
least,
we
can
actually,
like,
I
said,
automate
the
entire
workflow,
and
this
is
like
a
quick
example
of
that
this
is
a
pre-annotation
workflow.
C
So
what
it
does
is
it
just
launches
a
workspace
which
I
showed
the
annotation
workspace
that
I
showed
earlier
and
you
can
create
a
cvat
and
create
the
tasks
and
set
the
data
for
the
task
and
then
use
the
model
that
you
just
trained
to
pre-annotate
the
data.
So
when
your
annotators
show
up
the
next
day,
everything's
ready
to
go,
they
can
just
make
adjustments
and
then
do
the
whole
training
process
again
automatically.
C
C
Building
this,
so
one
of
the
main
challenges
is
running
this
on
multiple
platforms.
You
have
to
deal
with
different
ways
to
handle
auto
scaling.
Eks
has
its
own
ways
of
doing
it.
Gke
and
aks
are
a
little
bit
easier
to
deal
with.
You
have
to
have
different
gpu
daemon
sets,
depending
on
your
the
provider.
They
have
their
own
different
demon,
sets
and
also
storage
classes,
and
so
on,
so
eks
was
a
little
bit
harder
than
the
rest
of
them.
C
Another
interesting
challenge
for
us
was
those
interactive
side.
Cars
I
showed
earlier.
How
do
you
do
those,
and
how
do
you
make
sure
that
they're
garbage
collected
at
the
end,
so
we
use
istio
and
basically,
we
had
to
make
sure
and
then
we
create
kubernetes
resources
deployments,
and
you
know,
services
and
virtual
services
to
make
those
happen.
C
So
what
we
did
there
is
we
actually
use
that
menu
gateway
in
between
in
between
our
s3
apis
and
then
just
to
use
a
menu,
eight
gateway,
so
we're
using
s3
across
the
board,
but
then
minor
gateway
in
between
makes
it
possible
to
go
to
azure,
blob,
storage
and
gcs
at
the
same
time,
and
last
but
not
least,
our
back
and
network
policy
for
namespace
isolation.
That's
a
big
deal.
C
C
So
thanks
for
listening,
we
have
our
github
repository
listed
over
here
in
our
documentation.
Is
that
docs
dot
one
panel
dot
ai?
And
if
you
like,
what
we're
doing
and
you
find
it
useful,
give
us
a
star
we're
always
looking
for
contributors
and
if
you
have
anything
any
features
you
want
to
ask
that.
Have
us
develop
for
you
that
we
can
find,
we
will
find
useful
for
the
entire
community
and
let
us
know
if
you
need
any
ques.
E
C
Yeah,
that's
a
very
good
question.
We
did
look
at
cat
tip.
I
nni
has
a
big
big
community
around
it,
so
we
decided
to
go
with
that,
but
captive
is
always
an
option
and
I
know
it's
more.
It's
definitely
more
kubernetes
native
then
we
put
could
potentially
bring
catapin
as
well.
We
do
want
to
have
multiple
tools,
so
I
think
that's
that's
something
we'll
probably
look
at
later,
but
we
did
look
at
it
and
just
picked
nni
because
it
also
has
it
has.
C
A
Rush,
thank
you
very
much
for
that.
That's
great
presentation
really
interesting.
I
really
you
know
this
already,
but
I
was
really
interested
in
your
kind
of
sidecar
model.
This
kind
of
you
got
an
interactive
sidecar
and
I
wondered
if
anybody
else
had
been
using
something
like
this
with
with
their
workloads.
I
would
definitely
be
interested
in
typically
any
kind
of
workflow.
I
kind
of
want
to
get
that
feedback
fast,
especially
if
that
workflow
takes
a
while
to
execute
and
that
workflow
itself
can
often
tell
me
better
than
the
infrastructure.
A
What
it's
doing
you
know
where
it's
progressing.
If
it's
having
issues
if
tests
are
failing,
you
know
I
want
to
know
if
those
tests
are
failing
as
soon
as
they
fail.
Not
you
know
wait.
The
entire
suite
has
ended,
and
I
wonder
if
anybody
else
had
similar
use.
Cases
that
were
an
interactive
sidecar
would
be
useful
to
them.
A
That
sounds
like
a
no,
so
it's
just
me
in
that
case
fine.
Okay,
let's
moving
on
to
the
last
topic
for
today,
let
me
see
if
I
can
find
the
right
window
here.
Is
the
argo
workflows
survey
so
about
a
month
and
a
half
ago
we
sent
out
four
separate
surveys.
I
believe
one
to
about
our
go
workflows,
one
about
argo
events,
one
about
arko
cd
and
one
about
argo
rollouts,
which
henrik
was
involved
in
the
last
two
of
those
now.
A
This
is
obviously
not
the
best
forum
to
share
at
the
argo
cd
results,
but
it
is
a
good
forum
to
talk
about
argo,
workflows,
naga
events,
I'm
not
going
to
talk
about
argo
events
today
and
the
reason
I'm
not
going
to
talk
about
them
is
is
because
I
have
not
really
collated
the
results
yet
and
the
reason
I've
not
collated
the
results
yet
is
because
we
didn't
have
quite
so
many
respondents
for
that
survey.
I
think
we
had
less
than
20
respondents
and
I'm
kind
of
cautious
about
making
conclusions
on
those
numbers.
A
So
today,
we're
just
going
to
focus
on
the
ongo
workflow
survey
results
and
I'm
just
really
interested
in
getting
people's
thoughts
on
this
as
well.
If
anything
jumps
out
to
them
as
being
interesting,
and
maybe
I
have
a
few
questions
for
people
who
are
listening
today
as
well
about
this,
so
we
did
send
out
the
survey.
We
got
60
responses,
which
is
fantastic
and
we
computed
an
nps
score
on
that.
A
That's
net
promoter
score
and
that
came
out
at
66,
which
is
classified
as
I
think
exceptional,
so
we're
very
pleased
to
find
out
that
people
thought
you
know
would
be.
You
know.
The
average
rating
was
exceptional
people
very
pleased
to
recommend
the
product
to
us.
When
we
looked
at
the
kind
of
roles
and
use
cases,
it
probably
shouldn't
surprise
anybody.
This
was
dominated
by
software
engineers,
90
of
it.
A
We
did
wonder
if
we'd
see
more
and
data
scientists
amongst
this,
and
it
kind
of
maybe
hints
to
the
fact
that
the
people
we
got
responding
to
the
survey
were
maybe
operators
of
argo
workflows
rather
than
users
of
arco
workflows,
but
there's
not
very
much
we
can
do
about.
I
don't
think,
and
the
use
cases
with
the
same
kind
of
six
use
cases
that
kind
of
dominated
in
2020,
so
data
processing,
use
cases,
machine
learning
and
and
an
amount
of
ci
cd
and
infrastructure
automation.
A
One
of
the
things
we
try
and
get
feedback
on
a
regular
basis
is
about
new
features
that
we
introduce.
So
we
typically
look
to
develop,
features
that
are
popular
and
have
clear
use.
Cases
and
popular
features
are
basically
the
ones
that
have
got
the
most
thumbs
up
on
in
our
issues
list.
So
these
are
some
of
the
features
we
developed
over
the
last
year,
and
you
know
we
weren't
surprised
to
see
things
like
workflow
templates
at
the
top
of
the
list,
and
we
were
we
were
I.
A
So
it's
kind
of
harder
to
develop
those
features
out.
You
know
if
they're,
not
so
popular,
so
it's
good.
You
know
it's
good
to
kind
of
always
feedback
on
features
you're
using
on
that,
and
you
may
have
noticed
that
memorization,
I
think,
appeared
in
roughly
august.
We
haven't
really
kind
of
progressed
that
feature
as
a
great
deal,
and
I
think
this
kind
of
reflects
that
we
were
quite
interested
at
people
running
at
scale
so
into
it.
A
We
run
we're
probably
getting
on
for
350
installations,
350
clusters,
where
we
install
our
go
workflows
for
a
variety
of
different
use
cases,
and
we
we've
seen
a
lot
more
people
using
our
foes
at
very
large
scale.
You
know
workflows
with
ten
thousand
twenty
thousand
and
thirty
thousand
pods
of
them
simon.
I
think
you
saw
a
larger
one
recently.
D
A
A
A
A
So
there
are
other
workflow
managers
out
there,
but
they're,
not
cloud
native,
and
so
our
go
workflows.
You
know
dominates
us
as
the
best
one
that
was
there
to
do,
and
people
also
found
it
was
really
easy
to
use.
So
that
was
another
key
point,
and
the
final
thing
is
that
people
like
the
interrupt
with
other
tools
inside
the
argo
ecosystem,
particularly
I'm
using
argo
cd,
and
we
do
that
ourselves.
A
We
use
argo
cd
to
insta,
to
install
and
manage
arca,
workflows
and
I'll
go
work
for
templates,
and
you
know
core
components
are
managed
using
argo
cds,
installation
just
a
little
bit
of
next
steps.
I
think
I've
mentioned
this
previously
in
this
discussion.
We
need
to
determine
figure
out
what
we're
going
to
do
about.
Like
python
supports.
Do
we
need
to
promote
this
solution
more
or
do
we
or
do
we
need
to
provide
more
first
class
support
for
python
in
the
software,
and
you
know
what
areas
documentation
specifically
improving,
how?
A
A
Okay,
a
bit
of
silence
there:
okay!
Well,
that
was
the
last
topic
for
today
in
the
community
meeting.
Thank
you
all
for
coming
along
today.
If
you
do
want
to
ask
more
questions
or
follow
up,
you
come
and
hunt
us
down
on
slack
to
ask
those
questions
and
we'll
we'll
get
back
to
this,
and
hopefully
I'll
have
the
recording
available
to
share
probably
about
this
time
tomorrow.
A
Certainly
by
the
end
of
the
week,
if
you're
interested
in
presenting
or
coming
along,
we're
really
keen
to
have
people
come
along
and
talk
about
the
stuff
they've
been
doing
and
about
argo,
workflows
and
interesting
use
cases
they've
been
doing.
If
you
want
to
present,
you
know
just
look
us
up
on
slack,
we're
also
looking
for
people
to
come
along
and
write
guest
blog
posts.