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From Tianqi Chen <tqc...@cs.washington.edu>
Subject Re: Request for suggestions- Supporting onnx in mxnet
Date Thu, 19 Oct 2017 18:55:38 GMT
As for where the code should sit, we have seen onnx's support for caffe2
sitting on a separate repo.  My suggestion would be put code under nnvm/top
and migrate into mxnet eventually when the top components get into MXNet,
hopefully by end of next month.

I have elaborated my point in the last email thread. This (going through
nnvm/top) is an important design decision both technically (compilation,
more hardware) and strategically (articulate our core set of operators and
influence the model exchange format).

I am glad to see the discussion happening and surely there is doubt, as
with every big step of changes.  But with the rapidly changing pace of deep
learning systems, this is the direction that we thought is most promising.
We can call for a vote if necessary among the committers for the design
decision if there is still debate on this issue. Or we can keep the
discussion open and start some effort around nnvm/top to see how it goes

Tianqi

On Thu, Oct 19, 2017 at 11:15 AM, Lupesko, Hagay <lupesko@gmail.com> wrote:

> Mu,
>
> You’re mentioning plans for a new model format and compiler, but I don’t
> recall seeing it shared/discussed on the dev list. Can you share these, so
> it is more accessible to folks to understand the plan and vision?
>
> Personally, I think it will be a shame to add ONNX support to MXNet, and
> have it implemented outside of MXNet. At the end of the day, it makes
> things difficult for MXNet users.
>
> Hagay
>
> On 10/19/17, 10:01, "Mu Li" <limu.cn@gmail.com on behalf of
> muli.cmu@gmail.com> wrote:
>
>     I'm speaking under my "MXNet contributor" hat.
>
>     It will be sad that our new model format and compiler is not supported
> by
>     our own contributors. It puts us in a bad position to reach out to
> outside
>     to ask for support.
>
>     If you really what to do it with the onnx <-> mxnet way, I suggest
> putting
>     the codes under https://github.com/aws.
>
>     Best
>     Mu
>
>     On Thu, Oct 19, 2017 at 9:51 AM, Lupesko, Hagay <lupesko@gmail.com>
> wrote:
>
>     > Since there seems to be a difficulty to reach a consensus here, and
> this
>     > is a new area, maybe a good compromise would be to contribute this
> under
>     > /contrib as experimental, with whatever way Roshani thinks makes
> sense.
>     > Once there is code in place, and MXNet users and contributors are
> able to
>     > check it out, we can consider future steps.
>     >
>     > Does this proposal make sense to folks?
>     >
>     > On 10/18/17, 23:01, "Tianqi Chen" <workcrow@gmail.com on behalf of
>     > tqchen@cs.washington.edu> wrote:
>     >
>     >     I want to offer one last thing in terms of technical details. I
>     > mentioned
>     >     two trends in the deep learning systems. There is one last thing
> that
>     > is
>     >     omitted. How should we build a good deploy end for deep learning
>     > models.
>     >
>     >     There is always a paradox to this problem:
>     >
>     >     - On one hand, the deployment end needs to be lightweight and
> portable.
>     >     - We want a lot of optimizations (memory layout compute) and
> feature
>     >     support, this makes the project big.
>     >
>     >     All the existing systems suffer from this problem. The solution
> is
>     > simple,
>     >     separates the optimization part from the actual runtime and
> compiles
>     > the
>     >     things down to a bare metal module. And this is the solution
> nnvm/top
>     >     compiler pipeline offer, which I believe will become a standard
>     > practice of
>     >     deployment and where all systems go to
>     >
>     >     Tianqi
>     >
>     >     On Wed, Oct 18, 2017 at 10:03 PM, Tianqi Chen <
>     > tqchen@cs.washington.edu>
>     >     wrote:
>     >
>     >     > OK, there is some miscommunication in here I guess.  We only
> need to
>     > do a
>     >     > "canonization" step in python API that goes a symbol to symbol
>     > translation
>     >     > layer. It can be done in purely in python, and there is no
> need for
>     > going
>     >     > "down" into c++ to do this.
>     >     >
>     >     > For example, the current nnvm.from_mxnet API takes Module or
> Gluon
>     > module
>     >     > and get you back nnvm/top graph in python.
>     >     >
>     >     > All we are asking for is to decomposing it into
>     >     >
>     >     > def mxnet_to_onnx(module):
>     >     >    nnvm_graph, params = nnvm_from_mxnet(module)
>     >     >    onnx = nnvm_to_onnx(nnvm_graph, params)
>     >     >    return onnx
>     >     >
>     >     > This allows nnvm_from_mxnet to be reused for other purposes,
> like
>     >     > compiling API to deployable modules
>     >     >
>     >     > Tianqi
>     >     >
>     >     > On Wed, Oct 18, 2017 at 9:55 PM, Lupesko, Hagay <
> lupesko@gmail.com>
>     > wrote:
>     >     >
>     >     >> Tianqi:
>     >     >> Thanks for detailing the trends. I fully agree that ONNX is
> just a
>     > graph
>     >     >> serialization format – nothing more, nothing less. I also
> think we
>     > all
>     >     >> agree that this simple mechanism holds lots of value to DL
> users
>     > since it
>     >     >> allows them to move between frameworks easily (e.g. train with
>     > MXNet,
>     >     >> deploy on a mobile device with Caffe2, or the other way
> around).
>     >     >> As you said, In Memory IR is different than serialization
> formats
>     > such as
>     >     >> ONNX. They are designed to make the runtime execution as
> efficient
>     > as
>     >     >> possible, leveraging software and hardware optimizations.
> They are
>     > indeed
>     >     >> complex, and where the “meat” is.
>     >     >> (BTW ONNX regards itself as an “IR” format, but not in the
> same
>     > sense as
>     >     >> NNVM).
>     >     >>
>     >     >> At the end of the day, Roshani is aiming to deliver a simple
>     >     >> functionality to MXNet users: (1) take an ONNX file, and load
> it
>     > into MXNet
>     >     >> so you get a graph+weights you can work with (2) Given a
> trained
>     > model,
>     >     >> save it as an ONNX file. Since MXNet users do not interact
> with NNVM
>     >     >> directly, but rather interact with MXNet API (MXNet Module),
> isn’t
>     > the
>     >     >> simplest thing to do is just to construct the Module “on the
> fly”
>     > using
>     >     >> MXNet API? Taking the other approach, we will go from the top
> level
>     > MXNet
>     >     >> “load” API, go “down” to NNVM to construct the graph, go
back
> up to
>     > MXNet
>     >     >> to expose it as a Module. This seems to complex and does not
> add any
>     >     >> benefit. In whatever way we construct the MXNet Module
> object, NNVM
>     > will
>     >     >> always be the underlying in memory IR that is being executed,
> so
>     > why not
>     >     >> take the simpler route?
>     >     >>
>     >     >> Hagay
>     >     >>
>     >     >> On 10/18/17, 19:42, "Tianqi Chen" <workcrow@gmail.com on
> behalf of
>     >     >> tqchen@cs.washington.edu> wrote:
>     >     >>
>     >     >>     Hi Chris:
>     >     >>
>     >     >>     There is no intention to move things away from mxnet. The
>     > reduction of
>     >     >>     lines of code by having a better design in general, and
>     > usually, you
>     >     >> write
>     >     >>     less redundant code by benefiting from better design. As
> I may
>     > quote:
>     >     >> "the
>     >     >>     best design is not achieved not when there is nothing to
> add,
>     > but when
>     >     >>     there is nothing to be taken away."
>     >     >>
>     >     >>     MXNet has always benefited from this philosophy and
> improves
>     > with the
>     >     >> new
>     >     >>     designs and proper modularization. For example, we see
> such
>     > reduction
>     >     >> and
>     >     >>     convenience happening when migrating from MXNet's legacy
> op to
>     > the
>     >     >>     NNVM's mechanism. The new mechanism now enables things
> like
>     > sparse
>     >     >> aware
>     >     >>     support and other stuff which would be much harder to
> support.
>     >     >>
>     >     >>     The nnvm/tvm stack comes brings the same benefit(if not
> more)
>     > and it
>     >     >> will
>     >     >>     only add more features to MXNet itself. Offering more
> hardware
>     >     >> backends and
>     >     >>     optimization, allowing us to write less code and spent
> less
>     > time to
>     >     >>     optimize for each backend by going through TVM
>     >     >>
>     >     >>     Tianqi
>     >     >>
>     >     >>     On Wed, Oct 18, 2017 at 7:15 PM, Chris Olivier <
>     > cjolivier01@gmail.com
>     >     >> >
>     >     >>     wrote:
>     >     >>
>     >     >>     > Reduce code base of mxnet? By increasing scope of the
> dmlc
>     > modules?
>     >     >> Is the
>     >     >>     > intent to make mxnet a thin language wrapper around a
> group
>     > of dmlc
>     >     >>     > modules?
>     >     >>     >
>     >     >>     >
>     >     >>     > On Wed, Oct 18, 2017 at 6:58 PM Tianqi Chen <
>     >     >> tqchen@cs.washington.edu>
>     >     >>     > wrote:
>     >     >>     >
>     >     >>     > > To better answer Hagay's question, I would like to
> dive
>     > down a
>     >     >> bit deeper
>     >     >>     > > on the relation between MXNet, NNVM and model exchange
>     > format
>     >     >> like ONNX.
>     >     >>     > >
>     >     >>     > > There are two major trends in deep learning systems
> now:
>     >     >>     > >
>     >     >>     > > - Common serializable formats, like ONNX and CoreML,
> that
>     > defines
>     >     >> the
>     >     >>     > model
>     >     >>     > > exchange format.
>     >     >>     > > - The in-memory graph IR for quick optimization and
> JIT.
>     > NNVM,
>     >     >>     > Tensorflow's
>     >     >>     > > XLA falls into this category.
>     >     >>     > >
>     >     >>     > > The exchange formats are great, it only poses a layer
> of
>     >     >> conversion,
>     >     >>     > which
>     >     >>     > > is good for exchange. The real meat still comes from
> the
>     >     >> compilation and
>     >     >>     > > JIT pipeline you have to offer. For that, we will
> need an
>     >     >> in-memory IR,
>     >     >>     > > because of the cost of constructing, serialize could
> be
>     > high for
>     >     >> the
>     >     >>     > > exchange formats like protobuf.  And usually, the
> exchange
>     >     >> formats are
>     >     >>     > > designed in a minimalistic fashion, making it less
> easy to
>     > extend
>     >     >> more
>     >     >>     > > information to support in-depth optimization like
> automatic
>     >     >> quantization,
>     >     >>     > > accelerator support.
>     >     >>     > >
>     >     >>     > > The current MXNet relies on NNVM for in-memory IR
>     > manipulation
>     >     >> but does
>     >     >>     > not
>     >     >>     > > contain a compilation component that compiles to
the
>     > hardware
>     >     >> backends.
>     >     >>     > > Doing export to an exchange format and then back
into
> NNVM
>     > run the
>     >     >>     > > compilation poses too much burden that JIT compiler
> could
>     > pay.
>     >     >> Using the
>     >     >>     > > same in-memory graph IR as the compilation stack
give
> much
>     > more
>     >     >> advantage
>     >     >>     > > in terms of this.
>     >     >>     > >
>     >     >>     > > The newly introduces nnvm/top and compiler offers
> in-memory
>     > graph
>     >     >>     > > optimization and compilation and offers more hardware
>     > backend
>     >     >> directly
>     >     >>     > via
>     >     >>     > > TVM. We already see promising results in edge
> deployments
>     > with a
>     >     >> much
>     >     >>     > lower
>     >     >>     > > overhead of runtime. We will further benefit quickly
> from
>     > more
>     >     >> graph
>     >     >>     > > optimizations that it has to offer.
>     >     >>     > >
>     >     >>     > > Building support around this new paradigm offers
us
>     > advantage of
>     >     >> being
>     >     >>     > > future compatible and takes full benefit of the
> points I
>     >     >> mentioned above
>     >     >>     > >
>     >     >>     > > Tianqi
>     >     >>     > >
>     >     >>     > >
>     >     >>     > >
>     >     >>     > > On Wed, Oct 18, 2017 at 4:57 PM, Lupesko, Hagay <
>     >     >> lupesko@gmail.com>
>     >     >>     > wrote:
>     >     >>     > >
>     >     >>     > > > Roshani – this is an exciting initiative,
ONNX
> support on
>     > MXNet
>     >     >> will
>     >     >>     > > > enable more users to ramp up on MXNet, which
is
> great.
>     >     >>     > > >
>     >     >>     > > > Tianqi – a few questions and thoughts about
your
> note:
>     >     >>     > > > - “More hardware backends to mxnet” –
MXNet users
> get the
>     > same
>     >     >> benefit
>     >     >>     > of
>     >     >>     > > > HW support implementing ONNX import on top of
MXNet
>     > symbolic,
>     >     >> right?
>     >     >>     > > > - “NNVM Compiler now received contributions
from
> AWS, UW
>     > and
>     >     >> many other
>     >     >>     > > > folks in MXNet community.” – agreed it is
ramping
> up, but
>     > when
>     >     >> you look
>     >     >>     > > at
>     >     >>     > > > the data, it is clear that it is very early
on for
> NNVM.
>     >     >> Looking at the
>     >     >>     > > > repo, it has overall 223 commits, 0 releases.
> Compare it
>     > to
>     >     >> MXNet with
>     >     >>     > > 6136
>     >     >>     > > > commits and 32 releases. It seems to be still
early
> on for
>     >     >> NNVM, and
>     >     >>     > for
>     >     >>     > > a
>     >     >>     > > > more reliable initial implementation building
the
> import
>     > on top
>     >     >> of
>     >     >>     > MXNet
>     >     >>     > > is
>     >     >>     > > > easier, faster and safer. MXNet has lots of
users
> already
>     > using
>     >     >> the
>     >     >>     > > > Symbolic API which hopefully mean that is a
mature
> API
>     > that is
>     >     >> not
>     >     >>     > likely
>     >     >>     > > > to have breaking changes or major issues.
>     >     >>     > > >
>     >     >>     > > > I’m supportive option 1 proposed by Roshani
> (building
>     > serde on
>     >     >> top of
>     >     >>     > > > MXNet symbolic), but to do it as an encapsulated
>     > implementation
>     >     >> detail,
>     >     >>     > > so
>     >     >>     > > > the implementation can be migrated to NNVM or
> another
>     >     >> implementation in
>     >     >>     > > the
>     >     >>     > > > future, if at that point it seems like the right
> thing to
>     > do.
>     >     >>     > > >
>     >     >>     > > > Interested in hearing other opinions though…
>     >     >>     > > >
>     >     >>     > > > Hagay
>     >     >>     > > >
>     >     >>     > > > On 10/18/17, 14:13, "Tianqi Chen" <
> workcrow@gmail.com on
>     >     >> behalf of
>     >     >>     > > > tqchen@cs.washington.edu> wrote:
>     >     >>     > > >
>     >     >>     > > >     I am strongly recommending going through
the
>     > nnvm/top. One
>     >     >> major
>     >     >>     > > > reason in
>     >     >>     > > >     here is that the support of nnvm/top layer
NOT
> ONLY
>     > mean
>     >     >>     > > compatibility
>     >     >>     > > > of
>     >     >>     > > >     model format with onnx. These are the major
> benefits:
>     >     >>     > > >
>     >     >>     > > >
>     >     >>     > > >     - More hardware backends to mxnet, including
> opencl,
>     > metal,
>     >     >>     > Raspberry
>     >     >>     > > > Pi,
>     >     >>     > > >     web browser. These things are automatically
> enabled
>     > by going
>     >     >>     > through
>     >     >>     > > > this
>     >     >>     > > >     layer. In general, we design nnvm/tvm stack
to
>     > resolve the
>     >     >>     > challenge
>     >     >>     > > of
>     >     >>     > > >     current mxnet's weakness in terms deploying
to
> more
>     > hardware
>     >     >>     > > backends.
>     >     >>     > > >
>     >     >>     > > >     - More frontend capabilities, nnvm's gluon
> style IR
>     > ingests
>     >     >> now
>     >     >>     > from
>     >     >>     > > >     CoreML, ONNX and in future keras. Supporting
> those
>     > will
>     >     >> reduce the
>     >     >>     > > > amount
>     >     >>     > > >     of engineering effort needed.
>     >     >>     > > >
>     >     >>     > > >     - Future compatibility. We all agree that
the
> future
>     > being
>     >     >> migrated
>     >     >>     > > to
>     >     >>     > > >     gluon's API. NNVM/top tries to look ahead
by
> directly
>     >     >> adopting the
>     >     >>     > > > symbolic
>     >     >>     > > >     API to be gluon.
>     >     >>     > > >
>     >     >>     > > >
>     >     >>     > > >     I would also like to correct some of the
> mentioned
>     > facts
>     >     >> with
>     >     >>     > regard
>     >     >>     > > to
>     >     >>     > > >     nnvm/tvm stack
>     >     >>     > > >
>     >     >>     > > >     1.   Nascent project with few contributors
>     >     >>     > > >
>     >     >>     > > >     NNVM Compiler now received contributions
from
> AWS, UW
>     > and
>     >     >> many
>     >     >>     > other
>     >     >>     > > > folks
>     >     >>     > > >     in MXNet community. NNVM itself is already
> being used
>     > by
>     >     >> MXNet.
>     >     >>     > > >     MXNet's internal IR is migrating toward
gluon,
> and its
>     >     >> final form
>     >     >>     > > being
>     >     >>     > > >     nnvm/top
>     >     >>     > > >
>     >     >>     > > >     3.   Does not support all operators that
exist
> in
>     > MXNet
>     >     >> Symbolic
>     >     >>     > API
>     >     >>     > > >
>     >     >>     > > >     Neither NNVM/top or onnx support all operators
> that
>     > exist
>     >     >> in mxnet
>     >     >>     > > > symbolic
>     >     >>     > > >     API. The end goal here is mainly to make
> nnvm/top onnx
>     >     >> compatible,
>     >     >>     > > > which is
>     >     >>     > > >     a more reasonable goal.
>     >     >>     > > >
>     >     >>     > > >     4.  No CI Pipeline and testcases
>     >     >>     > > >
>     >     >>     > > >     NNVM already contains a compiler contains
> unittests
>     > and ci
>     >     >> tested
>     >     >>     > > with
>     >     >>     > > >     integration  https://github.com/dmlc/nnvm,
> with a CI
>     >     >> pipline that
>     >     >>     > is
>     >     >>     > > > well
>     >     >>     > > >     tested on CPU and GPU cases for front-ends.
>     >     >>     > > >
>     >     >>     > > >     Tianqi
>     >     >>     > > >
>     >     >>     > > >
>     >     >>     > > >     On Wed, Oct 18, 2017 at 1:41 PM, Roshani
> Nagmote <
>     >     >>     > > > roshaninagmote2@gmail.com>
>     >     >>     > > >     wrote:
>     >     >>     > > >
>     >     >>     > > >     > Hi guys,
>     >     >>     > > >     >
>     >     >>     > > >     >
>     >     >>     > > >     > I am working on supporting ONNX <
>     >     >> https://github.com/onnx/onnx>
>     >     >>     > > > pre-trained
>     >     >>     > > >     > models in Apache MXNet and would like
to seek
> your
>     >     >> opinion on the
>     >     >>     > > > choice of
>     >     >>     > > >     > implementation. I also have created
a GitHub
> issue
>     >     >>     > > >     > <https://github.com/apache/
>     > incubator-mxnet/issues/8319>.
>     >     >>     > > Supporting
>     >     >>     > > > ONNX
>     >     >>     > > >     > in
>     >     >>     > > >     > MXNet will enable users to move between
> frameworks
>     > with
>     >     >> their
>     >     >>     > > > models, this
>     >     >>     > > >     > will also enable MXNet project to be
a part
> of the
>     > ONNX
>     >     >> open
>     >     >>     > > > standard and
>     >     >>     > > >     > steer the direction of ONNX.
>     >     >>     > > >     >
>     >     >>     > > >     >
>     >     >>     > > >     > For those who don’t know ONNX, ONNX
is an open
>     > source
>     >     >> format for
>     >     >>     > AI
>     >     >>     > > > models
>     >     >>     > > >     > which enables models to be transferred
between
>     >     >> frameworks. Refer
>     >     >>     > to
>     >     >>     > > >     > https://github.com/onnx/onnx for more
> details.
>     >     >>     > > >     >
>     >     >>     > > >     >
>     >     >>     > > >     > To implement the import/export functionality
> in
>     > MXNet, I
>     >     >> propose
>     >     >>     > to
>     >     >>     > > > expose
>     >     >>     > > >     > a MXNet python module “serde”(name
taken from
>     > Apache Hive
>     >     >>     > project)
>     >     >>     > > > with the
>     >     >>     > > >     > following methods supporting different
> formats:
>     >     >>     > > >     >
>     >     >>     > > >     > sym, params = mxnet.serde.import(other_
> format_file,
>     >     >>     > > > other_format=‘onnx’)
>     >     >>     > > >     >
>     >     >>     > > >     > other_format_file =
> mxnet.serde.export(mxnet_sym,
>     >     >> mxnet_params,
>     >     >>     > > > ‘onnx’)
>     >     >>     > > >     >
>     >     >>     > > >     >
>     >     >>     > > >     > The implementation under the hood can
be done
> in
>     > two ways:
>     >     >>     > > >     >
>     >     >>     > > >     >
>     >     >>     > > >     > 1) Implement at the MXNet layer by
parsing
> the ONNX
>     >     >> model(in
>     >     >>     > > protobuf
>     >     >>     > > >     > format) and turn into MXNet Symbolic
> operators and
>     > build
>     >     >> MXNet
>     >     >>     > > model
>     >     >>     > > >     > directly. Similarly, I can convert
the MXNet
> model
>     > to
>     >     >> ONNX format
>     >     >>     > > at
>     >     >>     > > > this
>     >     >>     > > >     > layer.
>     >     >>     > > >     >
>     >     >>     > > >     >
>     >     >>     > > >     > 2) The DMLC community has released
the
> nnvm/tvm
>     > complier
>     >     >> and an
>     >     >>     > > >     > intermediate representation of the
models,
> refer:
>     >     >>     > > >     > http://www.tvmlang.org/2017/
>     > 10/06/nnvm/tvm-compiler-
>     >     >>     > > > announcement.html
>     >     >>     > > >     > <http://www.tvmlang.org/2017/
> 10/06/nnvm-compiler-
>     >     >>     > announcement.html
>     >     >>     > > >
>     >     >>     > > >     >
>     >     >>     > > >     > Based on the conversation on the GitHub
issue
>     >     >>     > > >     > <https://github.com/apache/
>     > incubator-mxnet/issues/8319> I
>     >     >>     > opened,
>     >     >>     > > Mu
>     >     >>     > > >     > mentioned that MXNet would use nnvm/tvm
as the
>     > backend in
>     >     >> the
>     >     >>     > > future.
>     >     >>     > > >     >
>     >     >>     > > >     >
>     >     >>     > > >     > We could hook into this layer to implement
the
>     >     >> import/export
>     >     >>     > > > functionality.
>     >     >>     > > >     > nnvm/tvm has ONNX 0.1 version import
> implemented.
>     >     >>     > > >     >
>     >     >>     > > >     > For import,
>     >     >>     > > >     >
>     >     >>     > > >     >    1.
>     >     >>     > > >     >
>     >     >>     > > >     >    I will need to enhance nnvm/tvm’s
importer
> to
>     > support
>     >     >> ONNX 0.2
>     >     >>     > > >     >    2.
>     >     >>     > > >     >
>     >     >>     > > >     >    Implement nnvm/tvm->mxnet symbolic
> operators.
>     >     >>     > > >     >
>     >     >>     > > >     > For export:
>     >     >>     > > >     >
>     >     >>     > > >     >
>     >     >>     > > >     >    1.
>     >     >>     > > >     >
>     >     >>     > > >     >    mxnet->nnvm/tvm ( nnvm/tvm provides
this
>     > implementation
>     >     >>     > already)
>     >     >>     > > >     >    2.
>     >     >>     > > >     >
>     >     >>     > > >     >    I will need to Implement nnvm/tvm>onnx.
>     >     >>     > > >     >
>     >     >>     > > >     >
>     >     >>     > > >     > These are the pros and cons I see in
the above
>     > approaches:
>     >     >>     > > >     >
>     >     >>     > > >     >    1.
>     >     >>     > > >     >
>     >     >>     > > >     >    Import/export at mxnet layer
>     >     >>     > > >     >
>     >     >>     > > >     > Pros:
>     >     >>     > > >     >
>     >     >>     > > >     >    1.
>     >     >>     > > >     >
>     >     >>     > > >     >    Stable APIs currently used by users.
>     >     >>     > > >     >    2.
>     >     >>     > > >     >
>     >     >>     > > >     >    Larger Apache MXNet community of
> contributors.
>     >     >>     > > >     >    3.
>     >     >>     > > >     >
>     >     >>     > > >     >    CI pipeline to catch bugs.
>     >     >>     > > >     >    4.
>     >     >>     > > >     >
>     >     >>     > > >     >    Comparatively less time to implement
and
> put it
>     > in the
>     >     >> hands
>     >     >>     > of
>     >     >>     > > > the
>     >     >>     > > >     >    users.
>     >     >>     > > >     >
>     >     >>     > > >     > Cons:
>     >     >>     > > >     >
>     >     >>     > > >     >    1.
>     >     >>     > > >     >
>     >     >>     > > >     >    In the future we may have to reimplement
> at the
>     >     >> nnvm/tvm
>     >     >>     > layer,
>     >     >>     > > > in case
>     >     >>     > > >     >    MXNet moves to the nnvm/tvm
> backend(assuming it
>     > will
>     >     >> move).
>     >     >>     > > >     >
>     >     >>     > > >     >
>     >     >>     > > >     >
>     >     >>     > > >     >    1.
>     >     >>     > > >     >
>     >     >>     > > >     >    Import/export at nnvm/tvm layer
>     >     >>     > > >     >
>     >     >>     > > >     > Pros:
>     >     >>     > > >     >
>     >     >>     > > >     >    1.
>     >     >>     > > >     >
>     >     >>     > > >     >    Less engineering work in case mxnet
moves
> to
>     > nnvm/tvm
>     >     >>     > > >     >    2.
>     >     >>     > > >     >
>     >     >>     > > >     >    nnvm/tvm would become a hub to convert
to
>     > different
>     >     >> formats.
>     >     >>     > > >     >    3.
>     >     >>     > > >     >
>     >     >>     > > >     >    nnvm operators are more in parity
with
> mxnet’s
>     > gluon
>     >     >> APIs this
>     >     >>     > > > could be
>     >     >>     > > >     >    useful in case Gluon becomes the
only
> standard
>     > that
>     >     >> MXNet will
>     >     >>     > > > support.
>     >     >>     > > >     >
>     >     >>     > > >     > Cons:
>     >     >>     > > >     >
>     >     >>     > > >     >    1.
>     >     >>     > > >     >
>     >     >>     > > >     >    Nascent project with few contributors
>     >     >>     > > >     >    2.
>     >     >>     > > >     >
>     >     >>     > > >     >    Does not support all operators that
exist
> in
>     > MXNet
>     >     >> Symbolic
>     >     >>     > API
>     >     >>     > > >     >    3.
>     >     >>     > > >     >
>     >     >>     > > >     >    No CI Pipeline
>     >     >>     > > >     >    4.
>     >     >>     > > >     >
>     >     >>     > > >     >    Current Apache MXNet project does
not use
>     > nnvm/tvm
>     >     >> backend
>     >     >>     > > >     >    5.
>     >     >>     > > >     >
>     >     >>     > > >     >    mxnet->nnvm/tvm backend needs
more testing
> and
>     > user
>     >     >> feedback.
>     >     >>     > > >     >
>     >     >>     > > >     >
>     >     >>     > > >     > Any suggestions on both of these approaches?
> From
>     > user's
>     >     >>     > > > perspective, this
>     >     >>     > > >     > will be an implementation detail that
is not
>     > exposed.
>     >     >>     > > >     >
>     >     >>     > > >     > Thanks,
>     >     >>     > > >     >
>     >     >>     > > >     > Roshani
>     >     >>     > > >     >
>     >     >>     > > >
>     >     >>     > > >
>     >     >>     > > >
>     >     >>     > > >
>     >     >>     > >
>     >     >>     >
>     >     >>
>     >     >>
>     >     >>
>     >     >>
>     >     >
>     >
>     >
>     >
>     >
>
>
>
>

Mime
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