►
From YouTube: 2020-01-14: Weekly Sync
Description
Meeting Minutes: https://docs.google.com/document/d/1vKYEPtqKiwsFwhVKPmPub5ebMqN9HteBcbdFAuTXalM/edit#heading=h.du0souabgio
A
B
B
B
B
That's
always
completely
hosed.
I
have
no
idea.
I
tried
reinstalling
everything
several
times.
I
just
I
have
no
idea
what's
wrong,
but
yeah
so
did
you?
Have
you
tried?
Let's
see
I
was
thinking.
Maybe
you
could
share
your
screen
and
we
could
go
through
it
here
to
see
what's
up
and
then
we'll
try
to
document
that
because
I'm
sure,
if
you
ran
into
it-
and
I
ran
into
it-
somebody.
D
A
B
C
E
B
Okay,
so
because
okay
yeah,
so
then
I
guess
in
this
case,
let's
not
try
that
again,
how's
it
going
again,
oh
yeah,
so
we
got
ogen
and
we
got
himanshu
on
here
as
well,
we're
going
through
saksham's
issue
right
now
with
the
development
environment.
It's
like
something
something
seems
to
be
a
miss
here,
because
he
can't
train
can't
train
the
model.
It's
saying
that
there's
no
repos
in
the
source,
but
I
can
do
it
on
my
machine.
B
B
B
B
B
B
B
B
B
C
B
B
B
E
A
B
Okay,
so
now,
if
you
guys
want
to
try
those
commands
to
pull
it
down,
we
can
see
if
that
works
on
everybody
else's
system,
so
I'll
post
up
this
guy.
Here
too,
then,
why
don't
you
also
try
to
pull
that
down
saksham
and
let's
see,
watch
your
screen
and
see
if
that
works
there
and
then
I'll
watch
the
ci
test
too.
Here,
let's
see
okay
and
then
I'm
gonna
think
about
what
could
be
going
wrong
with
the
docker
container.
A
B
B
B
B
B
Oh
no
entry
point
not
found
just
because
of
the
classification
command
because
it
doesn't
have
colons
in
it.
So
it's
trying
to
load
it.
So
that's
which
is
we
need
to
get
rid
of
that,
but
I
think
I
can't
remember
what
his
name
is.
The
other
yash
is
is
gonna
work
on
that,
I
think
so,
let's
see
so
we
just
need
to
add
like
next
to
oh
and
actually
I
think
it
changed.
The
model
predict
anyways,
didn't
it
or
no.
B
B
So
if
you
change
the
model,
classification
parameter
to
be
label
colon
into
colon
one
and
actually
check
this
out.
Do
control
x,
control
e
control,
x
or
ctrl
x
and
then
e
with
the
up
arrow.
First,
let's
see,
I
wonder
if
it'll
work
for
you
yeah
yeah,
so
that
opens
your
thing
in
the
editor.
Sometimes
it's
easier
to
fly
around
there.
I
just
somebody
just
told
me
this,
so
I
thought
I
would
share
so
label
model.
Wait.
You
went
too
far,
I
think
so
yeah
so
label
and
then.
B
B
B
What
the
hell,
what
the
okay,
the
only
thing
I
can
think
of
at
this
point
is
the
hash
values
of
those
files
are
incorrect.
What
happens
if
you
do
sha
384
some
space
train
images,
you
byte,
can
you
repeat
that
so
do
sha,
384
sum
all
one
word
and
then
space
and
then
one
of
those
file
names
or
both
of
the
file
names.
Both
the
train
file
names.
You
just
use,
train
hyphen,
star,
wait.
They
start
with
train,
so
just
do
train
hyphen
star
and
then
hit
enter.
B
B
B
B
B
B
Yeah,
so
what
happened?
Yeah?
What's
up?
Okay?
What
is
up
with
this
all
right?
Let's
do
let's
do
like
right
above
that
make
a
new
line
right
above
that,
if
statement
at
the
same
indentation
level
yeah
and
do
just
like
print
repo
dot
features
and
call
the
features
method,
so
dot
features
open,
paren,
close
paren
and
then
let's
see
yeah
so
parentheses,
sorry
not
brackets.
B
D
B
B
B
D
B
B
B
E
B
B
B
A
B
B
B
B
All
right
so
we'll
just
we'll
just
wait
for
this
to
start
up
and
let's
jump
to
somebody
else
for
now
and
see
if
we
can
loop
back
on
this
well,
you
tried
in
git,
pod
you're
gonna
need
to
figure
out
how
to
like
add
your
branch,
and
you
know
you'll
need
to
do
like
get
remote
ad
and
stuff
to
add
your
branch
and
then
pull
it
down,
but
yeah,
okay.
So,
let's
see
okay,
so
who
else?
Who
else
is
on
the
call
here?
I
think
we
have
somebody
new.
B
B
Document
stuff
about
the
code
based,
and
so
if
you
guys,
let's
see
if
you
guys
want
to
add
things
to
this
area
here.
Basically,
so
I
changed
the
the
contributing.md
to.
I
put
it
on
the
website
to
make
it
a
little
more
consistent
since
the
rest
of
the
documentation.
Is
there-
and
I
added
this
section
about
like
code
based
layout
and
notes
so,
and
this
is
like
a
lot
of
stuff.
B
Oh,
I
wish
I
wish.
I
I
keep
copy
pasting
this
or
something
like
you
might
want
to
write
it
in
here.
We'll
just
add
it
to
the
main
documentation,
but
so
on
this
note
of
how
do
cli
commands
work.
This
is
something
that
would
go
here
right
like
how
does
the
cli
work
and
the
answer
is
so
the
answer
on
this
one
check
out:
oh
well,
this
is
up
to
date
with
master
now.
So
let
me
make
the
text
a
little
bigger
here.
B
B
B
This
is
from
before
that.
So
it's
all
it's
eventually.
What
we
should
do
is
is
con
consolidate
that
code,
those
code
buses,
so
that
we
use
we
we
can
make.
We
just
like
assign
a
config
object,
and
that
would
be
like
all
of
the
arguments
that
go
to
this
thing.
But
this
is
all
holdovers
because
it
hasn't
gotten.
You
know
it
hasn't,
got
an
update
to
use
that
more
clean
way
of
doing
it.
B
So
the
way
that
it
works
is
you
basically
say
you
derive
from
this
command
class
and
you
implement
the
run
method
so,
and
what
this
allows
us
to
do
is
it
makes
it
really
easy
to
write,
commands
that
have
similar
arguments
without
rewriting
a
bunch
of
code
and
that's
why
it
works
this
way.
So
if
we
look
at
like
dffml
cli
ml
dot
py,
so
let
me
open
diff,
no
utils
commands.
B
So
there's
like
a
couple
commands
in
this
commands
file
that
basically
like
make
it
easier
for
anytime.
You
do
something
with
sources
you
would
just
like
derive
from
this
subclass.
That
has
the
sources,
and
then
it's
already
filled
out
this
arg
and
if
you
guys
have
seen,
I
can't
remember
how
long
everybody's
been
around,
but
if
you
saw
the
args
and
config
methods,
you
would
know
that
you
have
to
redefine
all
these
every
time
you
use
a
class
that
has
the
same
type.
B
That's
uses
source
and
it
uses
a
model,
and
all
you
have
to
do
is
basically
just
subclass
use
two
base
classes
right
and
now
anytime,
you
need
a
a
command
that
does
machine
learning
stuff.
You
can
now
just
derive
from
ml
command,
which
is
just
a
combination
of
something
that
uses
a
model
and
something
that
uses
a
source.
B
B
Does
that
make
any
sense
so
far,
okay,
cool
and
so,
and
if
you
want
to
see
what
it
I
mean,
this
is
what
they
actually
look
like
right.
They
basically
just
say:
okay,
I'm
a
train
command
right.
This
is
the
train
command
and
then
where's
the
run
method.
It's
kind
of
it's.
This
is
the
same
way
that
the
operations
work
is
they
implement
a
run
method
and
that
op
decorator
actually
just
creates
a
class
with
a
run
method.
That
is
the
function
that
we're
decorating.
B
So
in
this
case
we
basically
say:
okay
enter
the
sources,
then,
to
the
main
context
of
the
source
enter
the
main
context
of
the
model
check
out
the
source
context,
check
out
the
model
context
and
then
run
the
train
right
accuracy
very
similar
predict
gets
a
little
weird,
but
basically
the
way
that
you
tell
how
what
what
command
gets
run
is
by.
So,
if
you
look
in
cli.cli,
this
is
the
main
class
that
does.
B
This
is
the
main
entry
point
for
this
command
line
interface
and
if
you
define
like
you've
seen
over
here,
if
we
define
like
an
arg
like
arg
underscore
log,
and
then
you
say:
okay,
what
is
this
argument?
That'll
end
up
in
the
self.log
property
of
the
class,
so
that
was
another
thing
to
make
it
easier
so
that
you
basically
you
say
what
are
my
arguments
and
then
within
the
run
method
you
just
can
access
them
with
self.whatever
and
it
simplified
writing
these
commands
very
greatly
right.
B
So
then,
if
you
want
something,
that's
not
an
argument,
but
is
like
a
sub
command.
You
just
say:
what
is
the
property
equals,
something
that
derives
from
command
right,
and
so
then,
if
I
say
so,
if
I
say
dffml,
it's
going
to
just
print
out.
These
are
all
the
sub
commands
right,
which
is
what
we're
seeing
over
here
and
then.
F
B
Yeah
polymorphic
or
whatever
that
is
right,
and
so
the
issue
with
this
becomes
that
all
these
very
nested
config
options
aren't
really
captured.
We
could
capture
them,
but
the
problem
becomes
like
okay,
so
we
have
all
these
different
models.
Then
this
was
originally
one
of
the
things
that
happened.
It's
basically
like
okay.
I
wanted
to
list
out
all
the
different
command
line
options
for
all
the
different
models.
F
B
B
Become
this
massive
massive
thing-
and
I
don't
know
if
you
guys
have
ever
used
like
docker
docker
machine,
but
they
hit
that
problem
and
you
would
dump
their
help
message
and
it's
like.
Oh,
it's
like
400
lines,
long,
it's
insane
because
they
did
the
same
sort
of
thing
where
they
have
these
plugins
and
the
plugins
have
different
options.
It's
a
mess!
So
that's
why
it's
all
on
the
docs
website
so
but
yeah!
This
is
how
you
figure
out
what
cli
command
is
getting
run.
B
So
if
you
do
dfml
train
that
becomes
the
train
command
and
like
if
we're
looking
at
the
data
flow
stuff,
dfml
data
flow,
then
the
data
flow
is
actually
imported
from.
Let's
see,
where's
I'll
open
it
here,
so
dfml
cli
data
flow,
so
the
data
flow
commands
are
all
defined
above
here
right.
Okay,
that's
the
display
one
so
run
repos
is
this:
is
the
run
command
and
then
you
can
do
it
on
the
set
or
you
can
do
it
on
all
of
them?
B
To
define
a
sub-command
with
a
built-in
function
as
a
name,
you
can
just
preface
it
with
an
underscore,
and
so
obviously
this
is
all
the
type
of
thing
that
needs
to
be
documented,
which
is
why
I
started
writing
that
little
code
notes
thing
and
actually
this
might
go
under
the
notes
for
subsystems,
but
this
is
all
actually
stuff
that
needs
to
change
long
term,
because
we
have
that
existing
config
infrastructure
and
those
like
auto
args
and
config
methods
that
can
extrapolate
from
any
class.
That's
decorated
with
that
config
method.
B
F
B
F
B
B
So
this
is
the
example
of
right.
We
took
one
data
flow.
We
tagged
on
this
other
data
flow
here
to
the
side.
We
made
them
one,
we
merged
them
right
and
so
the
way
that
this
worked
is
we
basically
just
said
dfml
dataflow
merge
and
then
we
piped
that
to
the
dataflow
diagram
command
and
we
said:
okay,
your
data
flow
is
coming
from
standard
in
what
I
had
been
trying
to
say
was
that
we
could
implement.
B
B
Yeah,
okay,
so
let's
do
that?
I'm
glad
we
realized
that
so
that's
good,
so,
okay
yeah!
So
I
think
then,
in
that
case,
but
this
is
the
thing
right,
so
we
were
talking
about
and-
and
we
were
talking
about
how
you
did
you
understand-
I
think
it
I
feel,
like
I
remember
seeing
that
I
was
able
to
communicate
successfully
the
thing
about
the
repo
deaf
right.
B
F
B
So
for
everybody
else
that
hasn't
been
deep
in
this
area.
Basically,
let's
see
when
we
run
these
data
flows
with
with
okay,
so
with
a
data
source
right,
we
iterate
over
the
data
source
and
we
say
four
repos
in
source
dot.
You
know
with
features,
and
I
give
all
you
all
the
repos
with
those
features
right
and
that
internally
calls
that
source.repos
method
which
just
basically
dumps
out
everything
that
could
be
in
that
data
source.
B
F
B
You
know
the
string
representation
so
url
and
a
url
is
of
data
type
string.
That's
like
it's
primitive
right,
and
so
all
of
these
different.
All
of
these
all
of
these
little
blocks
here,
except
for
the
the
the
purple
or,
let's
see
the
more
pink
ones,
are
these
input
pieces
of
data
and
the
more
purple
ones.
E
B
B
B
B
D
B
Flow,
but
to
do
that,
you
can't
really
pass
currently.
You
can't
just
pass
the
certain
inputs
matching
definitions
directly
into
that
subflow,
which
would
be
helpful
because
then
you
could
just
say:
okay,
like
you
know,
to
kick
start
this
thing.
The
first
thing
I
do
is
I
run
this
subflow,
which
is
the
current
case
with
what
we're
working
on,
but
so.
B
B
B
A
B
D
E
B
And
then
you're
gonna
actually
use
that
feature
data
to
train
it
through
the
model
right.
So
when
anybody,
if
somebody
just
gives
you
a
package
name
now,
you
can
go
generate
all
the
numbers
that
are
associated
with
that.
That
would
actually
let
you
go
take
that
package
and
run
it
through
a
trained
model.
So
that's
the
point
of
this
and
yeah.
Okay.
So
did
what?
What
else
do
we
need
to
talk
about?
On
that
front?
There.
B
B
B
B
B
B
B
B
To
write
that
up,
but
you're
definitely
the
one
who's
approaching
understanding
that
very
quickly
and
the
other
person
who
really
understood
that
was
arvin,
but
he
hasn't
been
around
for
a
while.
I
think
he
had
to.
He
had
to
go
finish
up
school
and
get
a
job,
but
he
had
done
a
lot
of
stuff
on
the
data
flow
side
of
things.
B
B
D
C
B
B
B
D
D
F
B
B
E
B
B
B
B
Definition
act
as
a
network
of
implementations,
so
you
could
just
like
point
it
at
this
web
server.
That
knows
how
to
do
you
know
xyz
things.
You
mapped
that
onto
the
operation,
equivalence
and
now
you
you
have
access
to
all
of
those
things
without
having
to
you
know,
rewrite
the
python
api
code
to
call
those
right
so
to
let
us
integrate
with
other
things
as
well.
B
The
longer
term
goals
for
the
data
flow
side
of
things.
I
don't
think
I
told
you
guys
that
yet
I
I
know
yash
had
been
on
a
while
ago
when
I
talked
to
him
and
I
let
him
and
sudarson
know
all
about
that
stuff.
But
I
don't
and
I
know
arvin
knew,
but
I
don't
think
I
had
told
you
guys
yet
and
I
haven't
gotten
the
chance
to
write
it
all
down
so
anyways.
So
let's
talk
about
himanshu.
Did
you
want
to
talk
about?
A
B
B
B
B
B
B
B
B
B
B
B
Is
right
where
you
do
that
you
define
you,
create
a
list
like
on
the
fly,
with
like
a
little
for
loop
or
whatever,
so
for
some
reason
it
was
leaving
a
certain
property
as
a
generator,
but
it
wasn't
just
leaving
the
property
as
a
generator.
It
had
like
set
the
property
to
a
list
with
the
first
element,
as
the
actual
generator
for
that
list,
which
was
really
weird.
So
we
have
this
like
a
little
hack
around
that
now,
but
but
yeah.
So
we
need
to.
We
should.
B
We
can
kind
of
serialize
and
deserialize
any
of
the
classes,
and
so,
if
we
can
figure
out
at
some
point
how
to
unify
that
that
config
code,
that
would
make
things
like
you
know
it
would
make
things
like
this
easier
right,
because
the
thing
is
right
with
the
cli
commands.
They
don't
use
that
config
that
auto
config
stuff
right.
C
B
B
B
D
A
A
A
A
D
B
E
A
B
B
A
D
A
B
D
D
B
B
D
B
B
And
I
think
the
other
thing
is
that
you
know
if
you're
gonna
do
those
applicable
features,
methods
those
right
now,
you're
just
gonna,
need
to
make
sure
to
override
that
there
was
it
with
the
existing
api.
We
could
have
easily
added
the
string
data
type
in
there
and
done
one
of
those
like
what
was
that,
like
hash
bins
or
something
for
the
column
to
basically
add
string
all
they
were
doing.
D
B
F
B
Will
work
on
mlp
for
for
tensorflow
and
then
yeah.
It
tends
to
look
pre-trained
models
and
then
you
were
also
talking
about
giving
the
user
the
option
to
select
what
accuracy
means
with
psychic
models:
yeah,
okay-
and
so
let's
open
an
issue
for
this.
So
that's
that's!
You
know.
We
want
to
track
that.
B
All
right
so
model
scikit,
oh
and
I
was
able
to
successfully
demo
the
whole
project
to
somebody
over
the
weekend
is
the
first
time
I
actually
got
to
give
a
live
demo.
I've
given
a
presentation
before,
but
but
this
is
the
first
time
somebody
said
hey.
Can
you
use
your
thing
for
that
and
I
was
able
to
you
know,
pull
it
down
and
be
like
pip
install
everything
worked,
and
then
we
ran
it
on
their
data,
and
you
know
it
actually
got
90
accuracy.
So
we're
like
great.
B
You
know
we
were
using.
I
think
one
of
the
scikit
one
of
the
sidekick
models,
so
that
was
really
awesome
right.
It
was
like,
oh,
you
know
success.
We
can
pull
down
this
thing
and
people
can
use
it
for
machine
learning.
That
really
don't
understand
much
other
than
knowing
how
to
use
the
command
line,
and
you
know
export
their
cs
excel
files
to
csv.
So
that
was
a
big
win
right
there.
It
was
exciting
to
see
yeah
and
I
was
really
I
was
also
you
know.
B
D
F
B
Yeah
it
can
be,
it
can
be
a
little
nerve-wracking,
but
it's
really
cool
when
it
runs,
and
it's
just
like.
Okay,
I
did
all
of
this
stuff
at
the
same
time,
for
you
enjoy
like
it's
great,
let's
see
so
give
the
user
the
option
to
select
what
they
want.
I'm
going
to
add
an
issue
for
this
and
then
all
right,
great
yeah,
and
we
got
those
clustering
models
in.
I
want
to
go
use
those
myself.
I
want
to
see
how
that
works.
That's
going
to
be
fun.
Let's
see!
B
B
B
Let's
try
that
list
command
and
then
you
can,
you
know,
do
the
head
and
whatever
to
cut
it
off,
see
here
it's
doing
the
same
thing.
It
looked
like
that
was
a
list
yeah
that
one
list,
repos
yeah
grab
that
and
let's
see
and
you're
gonna
need
to
obviously
cut
off
those
little
carrots
at
the
front.
There.
B
F
B
B
A
B
B
Yeah
you
can
do
that
yeah
that
one
will
work
or
if
you
just
want
to
install
it
one
off.
You
can
do
the
pip
install
dash
e
source
idx
and
then
obviously
with
the
prefix
or
not
it
goes
yeah.
I
started
just
saying
everybody
should
install
to.local,
because
the
user
flag
sometimes
doesn't
work.
I
noticed
and
then
and
then
also
yeah.
Installing
globally
is
a
mess.
The
user
flag
sometimes
doesn't
work
so
that
prefix
seems
to
work
more
than
not
so
I
just
put
that
everywhere
in
the
docs.
B
E
B
B
B
D
A
B
B
B
A
B
B
F
B
B
C
A
B
B
We
could
try
printing
it
out
to
see
what
the
hell
is
going
on
there
like.
If
it's
parsing
that
stuff
correctly,
I
mean
I
kind
of
assume
it
is.
I
think
it's
a
problem
with
this
method
here,
because
I
bet
you
if
you
listed
the
one
source
and
then
you
listed
the
other
source,
it
would
show
up
correctly
if
you
listed
them
one
by
one,
but
I
guess
we
should
try
that
okay,
oh,
this
is
gonna,
probably
create
a
problem
for
my
machine:
oh
jesus,
oh
god,
okay,
yeah!
B
Okay,
geez
all
right.
Well,
I'm
gonna
have
60
000.
Okay.
There
we
go
all
right,
so
this
self
one
is
zero,
all
of
a
sudden.
So
what
happens?
If
we
do?
Okay-
let's
do
this
here
and
then
let's
do
I
swear.
I
tested
this.
It's
probably
self-zero
is
what
it's
supposed
to
be,
but
I
swear
I
tested
it
and
it
was
supposed
to
be
self
one.
B
B
B
E
B
17.6
yeah
and
I
have
3.7.3
either
I
was
relying
on
a
bug
or
there
is
a
bug
now,
because
this
is
obviously
working
on
my
machine
and
not
working
on
3.7.6
so,
and
this
is
also
but
the
other
thing
is
that
this
async
context
manager
lists
context,
god
that
is
a
long
class
name.
I
apologize
it's
what
it
is
async
helper,
so.
B
B
B
B
B
D
B
B
B
B
Will
work
for
everything,
because
this
is
this
is
basically
right.
This
is
where
the
generic
abstraction
stuff
comes
in,
because
this
is
this
is
the
part
right
here.
This
sources
class
is
what's
abstracting
all
of
the
the
background
data
sources,
whether
it
be
csv
or
json
or
whatever.
So
all
sources
will
now
be
fixed
right.
If
you,
if
you
combined
like
a
mysql
database
and
a
csv
source,
then
it'll,
it
should
all
be
fixed
at
this
point,
unless
I
interpreted
your
question
wrong.
B
B
A
B
B
B
B
B
E
B
A
that's
an
open
question
right
now,
but
so
yeah.
What
we'd
want
to
do
is
we'd
want
to
create
this
source
where,
as
it's
iterating
through
all
the
entries
in
it,
it
does
some
pre-processing
right
and
to
start
with,
the
initial
idea
would
be
just
do
like
you
could
specify.
Okay,
for
this
feature
that
you're
getting
out
of
this
source.
I
want.
B
Apply
this
pre-processing,
which
would
be
something
like
run
this
specific
operation
right
and
then
you
would
say,
use
the
use
the
featured
data,
as
you
know,
whatever
the
input
to
so,
for
example,
the
array
divide
operation
might
take
two
inputs
right.
It
might
be
like
you
know
the
array
and
then
the.
B
C
D
D
B
A
B
B
A
B
D
B
B
E
B
Okay,
great,
let's
do
that
so
it'll
probably
be
tomorrow
for
me,
but
so
let's
see
so
let
me
push
this
up
docks.
Okay,
all
right!
I
will
see
you
guys
have
a
great
night
and
thanks
for
working
this
all
out.
This
is
great
and
let
me
know
if
there's
anything
we
missed
on
twitter,
then,
okay,
all
right
thanks.