The phased approach makes sense. AOT sounds exciting! Thank you! Regards, Dom
> On Aug 17, 2017, at 8:10 PM, Tianqi Chen <[email protected]> wrote: > > Let me be a bit more specific. There will be three modes that such > integration is possible > > - Allow TVM function to declare a function that can be called imperatively > - Allow MXNet symbolic graph to be compiled to a standalone TVM module(no > mxnet runtime dependency) that can be deployed to the device you like with > support for OpenCL/Metal/ javascript etc. > - This is called AOT mode and is usually good for deployment, this is > what we will be PR next week > - Allow mix TVM compiled function with Gluon operator, and possibly shift > some of mxnet runtime to work with TVM runtime to support OpenCL/Metal > directly in MXNet. This should be the longer term and needed if we want to > do training > > Hope this clarify your question > > Tianqi > > > > On Thu, Aug 17, 2017 at 5:58 PM, Dominic Divakaruni < > [email protected]> wrote: > >> Congratulations!! >> >> Can you share what you are thinking with regards to how you propose to >> integrate TVM into MXNet? >> >>> On Thu, Aug 17, 2017 at 12:52 PM Minjie Wang <[email protected]> wrote: >>> >>> Great news! This is a huge step towards a highly efficient deep learning >>> system that is portable on different hardware. Thanks Tianqi and the >>> efforts of all the contributors. >>> >>> On Thu, Aug 17, 2017 at 3:41 PM, Tianqi Chen <[email protected]> >>> wrote: >>> >>>> Hi Guys: >>>> I am super excited to announce DMLC/TVM our deep learning >> compilation >>>> stack. There will be followups on mxnet to add the official support >> soon. >>>> To check what it is, see the announcement >>>> >>>> http://tvmlang.org/2017/08/17/tvm-release-announcement.html >>>> >>>> >>>> Tianqi >>>> >>> >>> >>> >>> -- >>> Minjie Wang >>> *New York University | Computer Science* >>> 715 Broadway, New York, NY, 10009 >>> >> -- >> >> >> Dominic Divakaruni >> 206.475.9200 Cell >>
