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From YouTube: 008 ONNX 20211024 Krishnamurthy ONNX and Audit Considerations and benchmarking with QuSandbox
Description
LF AI & Data Day - ONNX Community Meeting, October 24, 2021
Auditing considerations for ONNX models and benchmarking with QuSandbox
Speaker: Sri Krishnamurthy (QuantUniversity)
A
Hello:
everyone,
my
name,
is
shri
krishnamurti
from
quant
university
and
in
today's
presentation
I'm
going
to
be
talking
about
auditing
considerations
for
onyx
models
and
benchmarking
using
the
q
sandbox.
Thank
you
again
for
the
opportunity
to
present
today.
As
you
all
know,
as
the
complexity
of
machine
learning
pipelines
have
grown,
people
are
talking
about.
How
do
we
assess
risks
in
these
models
and
that's
the
problem
we
are
focused
on?
We
are
risk
advisory
based
out
of
boston
and
we
are
focused
on
the
intersection
of
data
science,
machine
learning.
A
We
have
looked
at
a
lot
of
algorithms
and
we
have
audited
a
lot
of
algorithms,
and
one
of
the
challenges
we
see
is:
how
do
we
take
all
these
disparate
components
of
models
and
pipelines
and
assess
the
risk
comprehensively
to
facilitate
that
we
are
building?
What's
called
the
q
sandbox
to
enable
governance
and
risk
management
of
these
models,
and
we
are
also
part
of
lfai.
As
a
member,
we
have
been
able
to
get
a
lot
of
feedback
from
the
community
and
we
very
much
appreciate
that
in
order
to
think
about
algorithmic
auditing.
A
When
we
task
building
these
models,
there
are
so
many
choices.
People
can
build
them
with
enterprise
products
with
open
source
products
and
the
interoperability
becomes
a
major
challenge
and
that's
where
a
lot
of
industries
are
now
thinking
about
using
onyx
models
for
enabling
that
interoperability.
A
Now,
when
we
do
an
an
algorithmic
array,
we
look
at
various
themes
as
a
part
of
the
puzzle,
but
let's
kind
of
just
focus
on
the
model
ps
and
some
of
the
considerations.
I
thought
which
was
important
were
things
like
you
know:
how
do
you
benchmark
a
black
box
model
with
a
white
box
model
which
you
have
potentially
developed?
A
A
So
if
you
are
thinking
about
different
versions
of
the
model
and
making
sure
that
you
are
testing
them
for
different
versions,
what
do
you
think
about
scalability,
especially
if
your
model
is
going
to
be
deployed
in
an
api
environment,
and
then
you
have
to
factor
in
network
latencies,
and
you
also
have
to
factor
in
data
transfers
between
models?
What
kind
of
performance
are
you
going
to
be
potentially
getting,
then?
How
do
you
benchmark
api
behaviors
when
you
think
about
apis
which
are
potentially
going
to
be
leveraging
various
cloud-based
infrastructures?
A
How
can
you
factor
those
nuances
within
your
environment
and
then,
finally,
the
actual
model
testing
itself?
And
how
do
you
stress,
test
scenario
test
and
what
it
matters?
So
these
are
some
of
the
things
we
look
at,
as
we
kind
of
you
know,
try
out
models
and
in
this
demo,
I'm
just
going
to
give
you
a
big
picture
orientation
on
some
of
the
things
we
do
as
a
part
of
this
assessment.
A
So
what
you're
looking
at
is
a
screenshot
of
the
q
sandbox
and
we
take
this
holistic
view
and
we
break
it
down
into
individual
components,
depending
on
the
importance
of
that
particular
audit
and
the
key
components
that
needs
to
be
evaluated,
and
we
have
provided
some
of
the
tooling
so
that
you
can
bring
in
some
of
these
open
source
packages
and
try
out
various
things
in
the
development
environment.
But,
in
the
context
of
you
know,
you
reproducing
some
of
these
aspects
in
a
dockerized,
cloud-based
environment.
A
You'll
understand
like
what
are
the
the
capabilities
of
the
model
for
at
a
granular
level
and
that
auditor
can
potentially
go
in
and
write
reports
on
what
they
saw
and
where
are
the
various
areas
wherein
they
could
potentially
enhance
or
improvise
based
on
the
model.
So
let
me
show
you
a
live
demo
in
case
you're
interested
to
know
more
about
the
platform
happy
to
answer
questions
at
a
later
point,
but
in
today's
presentation
I'm
just
going
to
give
you
a
big
picture
orientation.
So
here
what
we
have
done
is
as
a
problem
setting.
A
We
have
taken
a
simple
mnist
model,
which
is
linux
model
available
in
a
public
repo
and
we're
going
to
be
benchmarking
against
a
python
implementation
of
the
model
and
a
matlab
implementation
of
the
model
and
the
goal
here.
Is
you
know
if
you
are
going
to
be
using
these
models
in
an
api
environment?
How
do
we
test
it
and
how
do
we
kind
of
you
know
factor
your
analysis
and
how
do
you
potentially
audit
these,
based
on
some
of
the
considerations
I
mentioned
to
you
earlier
so
here?
What
you're
seeing
is
the
you
know?
A
It's
a
summary
board.
If
you
will-
and
you
know-
as
we
kind
of
you
know,
look
at
the
the
summary
board
and
we
start
looking
at
the
the
code.
So
I'm
going
to
show
you
you
know
what
we
have
done
is
we
have
taken
the
the
whole
models
and
we
have
created
an
api
which
will
hit
different
api
implementations.
So
one
is
our
implementation
in
python
for
an
mnist
model,
and
these
are
onyx
model
which
are
being
wrapped
up
in
an
api
so
that
you
can
actually
invoke
the
non-response
themselves.
A
A
You'd
want
to
do
various
what-if
analysis,
and
you
will
potentially
also
use
this
as
a
data
generator
so
that
you
can
try
out
different
things
and
then
generate
different
kinds
of
sample
data
sets
so
that
you
can
try
out.
You
know
what
what
the
model
is
going
to
potentially
incorporate.
So
you
can
see
that
there
are
performance
differences
between
the
tf
implementation
and
the
onyx
implementation
for
for
these
kinds
of
models.
A
So
this
is
a
visual
implementation
and
then,
finally,
when
you
run
this,
you
know
jupyter
notebook,
it
generates
reports
and
you
could
potentially
go
in
and
then
look
at
the
actual
reports
themselves
and
you
can
go
in
and
you
know
edit
the
report
and
add
your
id
or
notes
to
it,
and
then
you
could
kind
of
you
know
summarize
the
benchmarking
of
that
particular
report.
So,
as
I
mentioned,
auditing
a
model
is
a
very
involved
effort
and
there
are
various
components
to
the
puzzle.
A
So,
in
addition
to
the
life
cycle
and
the
testing,
there
may
be
aspects
of
specific
algorithmic
assessments
that
need
to
be
done.
Security
reviews
may
need
to
be
done,
especially
if
you're
going
to
be
evaluating
security
for
a
model.
So
there
are
a
lot
of
considerations
to
be
done
and
I
think
the
having
a
comprehensive
effort
to
benchmark
different
models
and
build
a
framework
for
auditing
would
help
kind
of
understand
the
capabilities
of
different
models
and
facilitate
and
foster
the
notion
of
interoperability
and
reproducibility
of
different
models.
I'll
stop
at
that
and
take
any
questions.