►
From YouTube: Weekly Sync 2020-10-27
Description
Meeting Minutes: https://docs.google.com/document/d/16u9Tev3O0CcUDe2nfikHmrO3Xnd4ASJ45myFgQLpvzM/edit#heading=h.r641vlb8trei
A
B
B
A
B
E
B
A
F
A
B
It
has
never
failed
for
me
honestly.
I
tried
it
last
week
too,
but
I
rebased
with
my
master
and
ran
this
command
after
reinstalling
everything
and
I'm
getting
a
weird
error.
Reader,
okay,
you
know
I
I
don't
know
how
to
pick
like.
It's
not
helpful,
it's
something
I
don't
know
and
when
I,
when
I
ran
when
I
ran
python
setup
dot
by
unit
test,
the
test
ran,
and
but
the
log
for
test
is
better.
When
you
use
python
setup.but
set
up
dot
by
test
yeah
instead
of
python
setup
dot
by
unit
test.
B
D
A
A
B
B
G
A
H
A
A
E
F
E
A
E
A
So
yeah,
so
let's
try
running
python3,
and
actually
this
is
one
of
the
changes
I'm
just
making
in
the
model
tutorial
so
or
to
all.
The
tutorials
is:
is
that
sometimes
when
we
run
pip,
it
runs
the
wrong
version
of
pip.
So
if
we
run
it
as
python
so
pre-pen
to
this
command
so
go
to
the
very
beginning
of
this
command
and
try
python.
A
Well,
so
here
so
that
that
also
can
be
prone
to
error.
So
the
one
thing
that
always
works
is
if
we
go
all
the
way
to
the
front,
and
we
just
do
do
python,
3
dash
m
and
then
whatever
the
pip
command
is
so
python.
Python3
dash,
m
pip,
install
dash
e
dot
and
that
usually
grabs
the
right
version
of
things.
A
E
E
A
Yeah,
it
worked
all
right-
great,
that's
great!
Now,
I'm
just
gonna
check
the
tutorial
here
and
make
sure
that
that
got
cleared
up,
because
I
made
some
edits
recently
and
I
want
to
make
sure
that
that
is
is
clear,
because
I
think
that
changed
since
okay,
I
think
we
we
have
captured
that
in
a
new
version
of
the
tutorial
here.
So,
let's
see,
let's
see,
let
me
just
okay,
great
all
right
so
and
then,
let's
see
so
shoe,
so
let
me
just
document
this
here
and
I'll
present
again
so
yeah.
A
A
A
Yeah,
okay,
so
I'm
just
I'm
going
back
and
forth
on
that,
because
I
think
there
was,
I
noticed
sometimes
as
I
was
going
through
and
doing
this
that
sometimes,
if
you
specify
pipe,
if
you
have
multiple
versions
of
python
3
and
you
specify
python
three,
then
it
gives
you
the
wrong
one.
So
I'm
trying
to
figure
this
out
as
I
did
as
we
as
we
get
the
tutorials
tested
right
now,
but
we'll
make
sure
we
address
that
and
then
we
also
ran
into
okay
yeah.
A
A
E
A
E
E
A
Yeah,
okay,
so
let's
I'll
make
a
note
to
re-run
through
this,
so
I
I
we
have
split.
This
tutorial
has
now
been
split
into
sort
of
three
different
tutorials
at
this
point,
so
that
maybe
we'll
clear
things
up
but
I'll
make
sure
that
I
note
that
we
need
to
run
through
back
and
make
sure
that
the
constructions
in
the
code
are
match
up
so
instructions.
A
C
A
It,
oh
don't,
okay,
this
time,
so
this
time
don't
run
with
dash
m,
so
dash
m
says,
run
run
this
run
the
next.
Whatever
the
next
argument
is
as
a
module,
and
so
you
can
do
that
for
installed
modules
or
if
you
have
like
a
directory
in
your
current
working
directory
that
contains
an
init
py.
Then
it'll
run
that
as
if
it's
an
installed
module,
so
yeah
just
run
it
without
the
dash
m.
A
And
basically
that's
why
we
throw
in
this
setup
common
file
when
we
do
the
dfml
service
dev
create
you
know,
whatever
kind
of
plugin
type
you
want
to
create,
but
that
is
less
than
clear
sort
of,
because
the
standard
way
to
do
things
have
everything
in
that
setup
py.
So
we're
going
to
change
that
in
the
future.
But
for
now
we
end
up
with
this.
A
This
situation,
where
we
have
a
setup,
py
and
a
setup
common
py,
so
you'll
notice,
when,
if
you
open
the
setup
py
file,
so
if
you
open
that
up
you'll
notice
that
we
have
that
this,
this
little
blob
the
boilerplate
to
load
commonalities
and
that
basically
does
a
it
allows
we
import
directly
from
that
file
in
the
same
directory
as
this
file,
which-
and
so
now
we
end
up
with
this.
You
know
the
setup
comment
imported
as
a
module
named
common.
A
So
that's
how
we're
getting
access
to
the
setup
kwrk.
So
now
we
need
to
basically
what
we
need
to
do
is
we
need
to
call
that
setup
tools,
setup
so
from
setup
tools,
import
setup
at
the
top
there.
We
need
to
call
setup
and
we
need
to
pass
it.
The
keyword
arguments
from
common,
so
just
so
at
the
bottom
of
the
file,
call
the
setup
function
and,
within
the
parentheses,
to
the
call
to
setup,
do
star
star,
common.kw
args,
and
what
that
does
is
it'll.
A
A
Parentheses
so
call
the
the
setup
function,
so
you
actually
want
to
so
make
a
call
to
setups.
So,
let's,
let's
forget
about
this
line
right
now
for
for
a
second,
so
basically,
what
or
yeah
just
just
that
line
so
just
make
a
call
to
call
the
function
named
setup
so
just
set
up
open
parentheses,
close
parentheses.
A
Okay
and
so
that
function
on
the
third
line
of
this
file,
you're
so
setup
tools
from
setup
tools,
we're
importing
this
setup
function,
and
so
let
me
let
me
actually
also
write
a
note
that
we
need
to
add
some
more
explanation
around
this,
so
explain
set
up
file
all
right,
so
this
setup,
so
we
still
have
one
more
thing.
We
need
to
do
here,
okay,
all
right,
okay,
so
now
we
need
to
pass
to
setup.
We
need
to
pass
it
all
of
these
keyword
arguments.
A
Are
you
familiar
with
keyword
arguments
so
like
the?
Basically,
when
you
call
you
know
how
you,
when
you
call
a
function,
sometimes
and
it'll,
have
you
pass?
You
know
you
have
some
things
that
are
positionals
and
then
you'll
have
some
things
where
it
says.
You
know
this
string
equals
this
other
thing
right.
This
variable
right,
yeah,
so
those
are
keyword,
arguments
and
what
we
can
do
is
we
can
take
a
dictionary
and
we
can
use
a
dictionary
since
a
dictionary
is
a
key
value.
Mapping
of
you
know
usually
strings
to
values.
A
Is
you
know
so?
This
is
this
is
something
that
that
you'll
see,
but
I'm
I'm.
I
have
become
aware
that
I've
became
very
used
to
it
and
I
think
a
lot
of
us
have
come
very
used
to
it,
but
it's
not
something
that
everyone
knows
and
it's
not
something.
We
should
probably
be
doing
right
off
the
bat
here,
because
it
does
not.
You
know
it's
not
it's
not
it's
not
intuitive.
If
you
haven't
seen
it
so
we'll
we'll
want
to
change
that
yeah.
A
Yeah
yeah
like
rant
yeah,
the
importing
using
import,
live
with
a
specific
file
and
and
the
keyword
argument.
Expansion
are
pretty
not
commonly
seen
things,
so
there
is
one
we
do
have
a
problem
here,
because
I
think
I'm
seeing
that
you're
using
python
3.6,
which
is,
does
not
have
data
classes
so
that
that's
a
problem,
so
we
we
need
to
have.
A
A
F
A
F
A
A
A
A
A
E
A
Yeah,
so
what
happens
is
to
avoid
having
to
do
that?
We
basically
export
everything
from
from
the
top.
So
so,
if
you
you
can
import
from
dfml,
you
can
import
everything
without
doing
the
dot
dot
dot,
because
or
else
it
sort
of
gets.
You
know
out
of
hand
now
this
may
not
be
the
best
course
of
action,
but
we
may
want
to
get
rid
of
this,
but
that's
the
reason
why
you're
seeing
this
is
because
you
created
the
folder
within
that
directory
and
when
I.
E
A
A
A
Yeah,
so
it's
trying
to
go
because
as
soon
as
you
import
dfml,
it
says:
okay
go
to
the
top
of
the
dffml
directory
and
now
find
every
single
python
file
in
there
and
export
every
unique
class
name,
and
since
we're
in
that
directory,
it's
it's
gonna,
it's
gonna
end
up,
exporting
it
and
so
yeah.
So
this
is
something
that
we
maybe
want
to.
I
don't
know
what
what
does
everybody
think
about
this,
because
at
first
I
was
thinking
it
really.
You
know
it
it
sort
of
makes
the.
I
believe.
A
A
B
B
A
B
B
A
E
A
Just
yeah
as
long
as
it's
not
within
this
directory
right
now,
yeah
it
can
be
within
this
directory
even
but
it
probably
should
go
in
its
own
directory,
not
here
either
just
because
this
is
your
main
source
tree
for
dffml,
and
you
don't
want
to
accidentally
sort
of
add
that
project.
You
know
this
is
sort
of
like
we
created
a
new
project,
and
so
we
we
use.
We
would
probably
want
to
store
it
somewhere
else,
but
it
will
work
there.
A
A
A
Yeah,
so
you
you
would
wanna
yeah,
so
so,
basically,
okay
and
and
yeah.
So
let
me
let
me
sort
of
reset
here
for
a
second.
So
when
we
create
a
new
model,
when
we
create
a
new
model,
what
we
did
was
we
ran
this
dffml
service
dev
create
model,
and
then
we
we
have
a
new
directory
containing
a
model
right
and
now,
if
we
want
to
add
that
model
to
dffml
what
we,
what
we're
going
to
do
is
we're
going
to
take
it
and
we're
going
to
put
it
within
the
model
directory
yeah.
E
A
Master,
let
me
just
make
sure
that
that
is
correct,
so
packaging,
a
model
so
yeah
at
the
end
of
the
new
packaging.
So
if
you
go
on
the
master
docs
and
you
look
at
the
the
packaging
tutorial
for
models,
it's
it
mentions
that,
basically,
you
know
we've
we
have
this
directory
with
our
package
model
right
that
we
can
export
and
and
and
give
you
know,
we
can
upload
that
to
pi
pi
and
other
people
can
use
our
model.
A
But
if
we
wanted
to
add
that
model
to
dffml
the
way
that
and
the
way
that
the
dffml
source,
the
git
source,
that
we're
all
working
on
is
structured,
is
that
we
have
the
we
have
whatever
plugins.
If
you
look
at
the
top
level,
there's
directories
like
model
service
source
and
and
then
within
the
dffml
directory.
A
A
They
are
pretty
nice,
okay,
good,
so
you
know
I'll
take
another
look
at
them,
but
basically
it's
it
start
now
starts
like
okay.
How
would
we
use
a
model
right
and-
and
we
basically
do
the
iris
stuff
from
tensorflow
and
we
write
a
little
python
file
and
we
and
we
run
through
you,
know
the
command
line
usage
and
then
we
go
to.
How
do
you
write
a
model?
And
then
we
just
focus
on
you
know
the
one.
A
E
A
A
So
then
the
very
final
thing
is:
we
talk
about
uploading
to
pi
pi,
and
so,
if
you
upload
to
pi
pi,
then
now
anybody
could
go
and
install
your
package
right
and
you
don't
have
to
maintain
your
package
as
if
it's
you
know
another
package
within
you
know
you
don't
have
to
contribute
it
to
dfml
source.
If
you
don't
want
to
right,
because
you
know,
obviously
we
will
make
you
write
tests
and
we
will
make
you
do
xyz
and
document
it,
and
you
may
not
feel
like
doing
that.
A
A
If
you
want
to
contribute
this
to
the
dffml
source
code,
then
you
need
to
to
to
take
that
to
place
the
top
level
directory,
which
is
the
one
you
created
that
dfml,
that
hyphen
model
hyphen
myslr
into
model,
slash
slr
within
the
dfml
source
tree
and
then
you
submit
a
pro
request
is,
is
what
the
little
note
says
and
so
and
that's
what
we're
talking
about
where
that
is
where
we
maintain.
So
within
the
root
of
our
source
tree.
E
E
A
A
A
G
A
A
A
So
you
type
slash
now
hit
tab.
Tab
and
it'll
show
you
all
the
stuff
there
yeah
so
now
you
can
see
and
that
sort
of
it's
it
tab
will
do
an
auto
complete
if
it
finds
it.
So
you
can
do
dffml,
hyphen
and
then
hit
tab
and
it'll
auto
complete
the
rest
of
it
all
right
or
wait.
Wait
not
that
one
you
want
to
do.
E
A
E
A
A
B
E
A
A
Yes,
yeah,
so
that's
that's
that's
generally,
and
if
you
look
in
so,
if
you
look
in
the
the
main
source
directory-
and
maybe
here
let
me
go
on
github
and
show
this
that.
So,
if
you
look
so
this
is
our
this
is
our
you
know
the
the
root
of
our
source
right,
so
the
top
level
directory
of
our
our
source
code.
And
so,
if
we
look
in
you
you'll
notice
like
so,
we
have
dffml
here
right
and
this
is
the
main
package
and
so
within
dffmo.
A
A
And
now,
if
you
look
in
the
top
level,
our
source
tree
right,
here's
the
main
package,
here's
things
that
that
come
as
extras
as
their
own
packages
right,
so
we've
got
model,
we've
got
operations,
we've
got
service
and
you
see
the
http
service
and
then
my
sql
source
right,
and
so
this
is
dffml
hyphen
source,
hyphen
mysql.
This
dfml
hypen
service,
hyphen
http.
A
And
if
you
come
in
here
to
the
model
directory
you'll
see,
you
know
this
is
sort
of
the
naming
convention
is
based
on.
You
know
what
it,
what
is
the
name
of
the
library
that
we're
wrapping,
because
usually
we're
wrapping
some
library
and
exposing
it
within
you
know
our
dffml
way
to
provide
access
to
it.
A
You
know-
and
so,
if
you
came
in
here-
and
you
wanted
to
look
at
like-
let's
see
we
can
look
at
dolph
or
pi
is
pretty
pretty
good
one
to
look
at,
and
so
so,
if
you
come
in
here,
you'll
notice.
This
is
a
similar.
This.
This
looks
like
the
directory
structure
that
you
saw
when
you
created
that
that
plug-in
right
when
you
ran
dfml
service
dev
create
a
dfml
model.
A
A
That's
probably
something
we
want
to
change,
but
right
in
here,
you'll
find
here's.
Here's
the
the
dow
for
pi
model
that
we're
wrapping
and
it's
very
you
know
very
similar
sort
of
thing,
and
it's
got
some
example
usage
and
here's
it's
you
know
here's
its
body
right.
They
actually
predict
train
methods
and
so
yeah.
So
this
is
this.
This
is
the
way
you're
gonna.
A
Then
you're
going
to
create
a
new
package
and
then
it's
going
to
go
under
you
know
if
it's
a
model
it'll
go
under
model
right.
So
if
you're
wrapping
some
kind
of
you
know
new
you're,
providing
a
model
that
uses
some
underlying
machine
learning
library,
that's
you
know
from
some
library.
That's
not
on
this
list.
Then
you're
going
to
create
a
new
one
of
these
directories.
A
A
A
A
A
E
A
E
A
A
So
basically,
what
we
could
do
is
maybe
add
some
that
the
using
the
anomaly
detection
models-
we
could
say
you
know,
throw
out
anomalous
data
points
and
then
only
use
the
non-anomalous
ones
for
the
data
set
for
training
a
model,
so
this
would
will
fit
very
well
into
that.
So
what
what
I
guess?
The
first
question
asked
here
right
like
we
just
talked
about
directory
structures.
You
know,
have
you
have
you
done
this
sort
of
thing
before
and
if
so,
is
it
something
where
you
use
like
scikit
or
something?
A
E
A
A
E
E
E
A
A
Okay,
I
think
this
sounds.
I
think
this
sounds
like
a
great.
You
know,
first
first,
one
to
first
wanted
to
get
you
familiar
with.
This,
will
get
you
familiar
with
the
contribution
process
and
and
and
and
and
give
us
give
us
something
that
we
needed.
So
this
is
great,
so,
okay,
here's
here's!
What
you're
gonna
do
you're
gonna
basically
go
through
and
you're
going
to
so
number
one.
A
A
They
don't
go
work
on
it
too
right
and
then
we
keep
working
on
pull
requests
and
if
you
don't
have
any
activity
on
it
for
more
than
21
days,
we'll
assume
that
you
abandoned
it,
and
you
don't
have
time
to
tell
us
what's
going
on
and
we'll
assume
that
someone
else
should
start
working
on
it,
and
so
basically,
that's
that's.
What
you're
going
to
do
for
yourself
is
instead
of
you,
know,
picking
up
an
issue
and
commenting
on
it.
A
You
just
create
create
your
own
issue
for
what
you're
going
to
do
so
that
we
know
you're
doing
it
and
so
that
you
know
someone
else
comes
along
and
decides
you
know:
hey
I'm
going
to
do
an
anomaly
detection
model.
They
see
that
oh
wait,
you're
already
working
on
that.
Let
me
do
something
else,
so
create
an
issue
talk
about
what
you're
going
to
do
and
then
so,
since
you're
working
working
since
you're,
only
using
numpy
you'll
want
to
add
to
model
size
scratch,
since
that
is-
and
this
is
poorly
named.
A
E
A
E
E
A
A
A
Stop
before
you
get
to
this
step,
because
I'm
we're
we're
changing
the
way
some
of
this
stuff
is
getting
tested
and
right
now,
the
current
way
that
was
all
getting
tested
beforehand
or
before
this
was
was.
We
were
writing
separate
test
cases
and
stuff,
but
we've
recently
introduced
some
new
new
testing
infrastructure
to
test
these
doc
strings,
and
so
so,
once
you
get
before
you
get
here
pause
and
then
ping
ping
ping
me
because
I'm
the
one
sort
of
working
on
that
right
now,
so
all
right.
A
A
Don't
don't
worry
about
it,
no
nobody's
on
a
schedule
here,
so
we're
all
just
we're
all
just
working
on
this,
I'm
I
mean
I'm
trying
to
get
this
thing
out.
Actually
I'm
trying
to
get
this
thing
out
by
friday,
because
my
boss
is
about
to
ask
me
what
what
did
you
do
this
month
and
I'm
hoping
the
answer
is:
make
the
make
the
release
so
because
my
other
main
deliverable
sort
of
got
got
it
it
got
delayed.
A
So
basically,
if
I
don't
do
say
this,
then
it
looks
like
I
haven't
done
much
this
month
and
I've
been
I've
been
working.
So
so
I'm
really
trying
to
get
this.
This
release
out
to
this
release
out
by
friday,
but
other
than
that
we
basically
you
know
it's
not
it's
not
like
we're
on
any
sort
of
schedule.
A
What
we
really
try
are
going
to
try
to
do
is
basically
just
you
know
as
stuff
gets
added,
it
gets
released
and
you
know
it
sort
of
just
moves
forward
and
and
whenever
stuff
gets
added
people
get
access
to
it
right.
So
there's
no
there's
no
rush.
You
know
we're
around
and
you
can
ask
us
all
any
questions
you
have
and
get
her
or
you
know
in
this
meeting.
So
sounds
good
cool.
Do
you
have
any
other
questions
in
general
here?
C
G
C
A
A
A
A
A
B
A
A
With
the
new
the
new
testing
of
the
tutorial,
so
basically
what
it's
trying
to
do
is
it's
going
through
and
it's
reading
all
of
the
rst
files
and
it's
checking
if
they
have
the
test.
If
that
little
colon
test
colon
in
them,
which
means
that
you
know
there's
some
code
block
in
there
that
we're
going
to
actually
run
as
a
test
and
then
it's
going
to
create
unit
test
test
cases
for
those
code
blocks
or
for
that
for
that
file.
A
A
A
A
A
A
A
D
A
B
A
B
A
Them
are
transcript,
yeah
so,
and
I
think
I
think
you
know
I
think
so
yeah.
I
think
the
first
step
here
is
basically
going
to
be
to
go
through
and
say:
okay
for
stuff
doing
weird
stuff,
with
sockets
and
pipes,
which
is
basically
the
http
test
stuff
and
the
the
documentation.
You
know
running
commands
that
involve
pipes,
which
is
that
one
that
you're
looking
at
there,
I
would
just
do
add,
add
unit
test
skip
to
them
so
that
we
know
which
ones
they
are,
and
they
don't
block
your.
A
You
know
your
your
view
here
of
the
ones
that
are
not
related
to
that,
because
I
I
don't
know,
I
think
that's
probably
step
one
here
is
sort
of
you
know
give
yourself
give
yourself
a
clean
view
of
the
of
the
stuff.
That's
you
know,
that's
that's
that's
easily
fixable
and
then
go
fix
that
stuff
right
and
then
we'll
sort
of
do
we'll
deal
with
these
more
complex
cases.
B
A
Yeah,
let's
see
cash
downloader
on
pack
yeah,
I
and
then
actually
so,
okay,
so
after
you've
done
that,
can
you
add
the
base
package
to
the
ci
as
a
ci
target,
because
that
will
be,
you
know,
that's
made.
That's
one
of
the
key
things
here
is
that
we
need
to.
We
need
to
get
this
running
within
the
ci
so
that
we
we
have
a
constant
view
of
this,
so
basically
skip
skip
the
ones
like
yeah
for
windows.
D
A
B
D
A
Oh,
maybe
it's
this
here
see
the
the
line.
Eight,
the
one
above
where
you
were
so
in
line
eight
line,
eight
in
files
and
df
from
all
commit
it's
basically
an
r
glob
with
a
star
star,
slash.
I
think
it's
because
we
have
here
jump
back
to
your
just
it's
okay.
If
you
just
look
at
the
just
look
at
the
stack
trace,
you
see
in
the
stack
trace,
it
says:
doctest
cache
yeah
that
guy
right,
it
says
star,
star
star.
A
E
A
A
File,
this
is
in
a
temporary
file.
Yes,
it's
creating
it's
it's
running
the
doc
test
for
it,
so
basically
it
extracts
the
it
extracts
the
lines
from
the
the
doc
string
and
there's
a
doc
test
module
within
python.
That's,
I
believe,
what's
running
yeah
yeah
they're
at
the
top,
the
stack
trace
lib,
slash
doctor.
A
B
A
Yes,
let's
see
yeah
yeah
these
ones,
I
think
yeah.
Those
are
all
related
to
http
test,
which
is
my
testing
library
that
I
need
to
go
fix
for
this.
So
if
you
can
just
comment
them
or
let's
see,
let
me
let
me
see
what
could
I
do.
I
think
I
could
yeah.
I
don't
want
to
skip
them,
but
I
think
if
you
could
add,
if
you
could
add
the
unit
test
skips
over
those
tests,
that
would
be
like
you
know,
there's
a
unit
test.skip
if.
A
B
D
A
B
A
A
A
A
A
A
A
H
D
B
B
A
B
A
Oh
god,
yeah
it's
a
mess,
let's
see
yeah,
I
think
oh
there's.
One
thing
I
wanted
to
say
is
that
I
changed.
I
recently
changed
the
service
dev
install
command.
We
previously
had
it
so
that
remember
we
had
gone
through
some
discussion
about
you
know.
If
it
fails
to
install
a
package,
then
it's
hard
to
see
which
package
you
know
it
failed
to
install.
So
we
started
installing
each
package,
one
by
one.
A
I
was
running
on
various
different
systems
and
I
found
that
you
know
that
new
feature
flag.
So
when
you
do
pip
install
use
feature
2020
resolver
that
it
has
been
yelling
about
recently,
there's
been
a
lot
of
discussion
on
an
issue
thread
under
the
pip
repo,
basically,
and
it
has
to
do
with
all
of
these
machine
learning
packages
and
how
they
have
different
conflicts
with
versions
with
each
other,
and
essentially
you
know
the
we.
We
definitely
hit
this
issue,
because
this
is,
you
know
exactly.
B
A
A
A
Basically,
what
I
did
here
is,
I
I
you
know
we
were
running
it
once
per
and
then
I
instead
just
ran
it
once
and
then
I
went
through
and
I
did
invalidate
caches,
and
I
said
so.
Basically,
if
we
expect
things
have
changed
from
an
importing
perspective,
then
try
to
import
or
try
to
see
if
you
can
import
this
package
name
and
if
you
can't,
if
you
can't
find
the
module,
then
append
it
to
the
list
of
failed
packages.
A
So,
okay,
yeah,
okay.
I
have
another
meeting
coming
up
here
in
a
second
I'm
technically
right
now,
but
so
let
me
just
okay.
So
yes,
you're
gonna
go
through
and
you're
gonna
d,
you're
you're
clear
on
next
you're.
You
you,
you
feel,
like
you've
got
a
yeah
okay,
try
to
run
tests
in
ci,
so
saksham
you're
going
to
do
the
new
versions
of
pytorch
and
pivision
for
the
annotations,
oh,
and
we
want
to
talk
about
ops
source,
not
working
as
it
should
be
all
right.
So.
A
So
it
takes
an
operation
implementation
and
then
it
takes
the
arguments
now.
Okay,
when
we
a
enter,
we
let's
see
yeah.
We
basically
just
pass
the
arguments
to
the
data
flow
yeah.
We
basically
add
the
arguments
to
the
data
flow.
We
run
the
data
flow
with
this
single
value
and
then
we
grab
the
outputs
using
the
get
single
operation.
A
C
A
Yeah,
I
mean,
I
think,
if
you
want
to
do
that,
I
think
it's.
The
logical
thing
to
do
here
is
to
create
some
operations
that
just
kind
of
like
how
we
have
model
predict.
You
know
maybe
create
some
operations
around
sources.
So
and
you
can
do
it,
you
know
we
have
the.
We
have
the
ability
right
now,
so
you
can
basically
just
write
up.
You
know
you
write
your
python
file
and
then
you
just
you,
can
reference
it
with.
A
A
A
A
Instead,
so
so,
if
you
look
at
this
example,
basically
he
writes
this
file
operations.py
and
he
puts
some
operations
in
the
file
and
then
when
he
runs,
the
dataflow
create
command
and
the
dataflow
run
command.
He
basically
just
says
operations
colon
the
operation
name
within
the
file,
and
that
way
he
doesn't
have
to
create
a
new
plug-in
or
and
register
them
as
entry
points
and
stuff.
A
And
it
will
figure
out
yeah,
we'll
we'll
make
sure
that
we
figure
out
the
the
best
way
to
do
it.
You
know
from
there.
Okay,
all
right,
cool
thanks,
everyone!
Sorry!
I
have
to
run
right
now,
but
you
know
we'll
we'll
try
to
just
if
there's
anything
else,
try
to
ping
me
on
gitter
and
we'll
figure
it
out
so
or
next
week.
So
all
right
cool
thanks.
Everyone
have
a
go
on.