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onnxruntime/docs/tutorials/mobile/limitations.md
Cassie a0f3e30de6
Docs update: updated nav, get started sections, home page, apis (#9060) 
* initial setup and rename "how to" to "setup"

* move API to main nav

* move api to main nav

* add get starated, rework nav order

* rename to install move mds out of install section

* update api nav and home page

* add install docs and python qs updates

* python get started work

* remove c and obj c for now

* move java, python, and obj-c docs under api folder

* move java api html to iframe (ugh)

* remove api docs w/o details, move api text getstar

* remove api docs wo detail updates get started

* remvoe iframes

* move eco system to main nav

* fix api buttons

* added more examples moved intro to ORT

* fix links

* fix get started titles

* fix get started titles

* fix more links

* fix more links

* more link fixes

* fix nav remove inferencing and training subnav

* fix top nav remove inference and training nav

* fix title

* fix tutorials nav hierarchy

* fix python api button

* add tenorflow keras example

* fix quickstart toc

* add imports fix spacing

* fix links

* update nav and python get started page

* move ort training example, add coming soon for iot

* update C# get started

* fix spacing on quantization

* Add some js get started content

* fix formatting

* fix typo

* removed onnx-pytorch and onnx-tf

* updated pip install torch and added links iot page

* added pytorch tutorial heirarchy

* updated web to docs soon added release blog link

* add web link
2021-09-15 16:23:42 -05:00

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Limitations Deploy ONNX Runtime Mobile Tutorials 7

Limitations

A minimal build has the following limitations:

  • No support for ONNX format models
    • Model must be converted to ORT format
  • No support for runtime optimizations
    • Optimizations are performed during conversion to ORT format
  • Limited support for runtime partitioning (assigning nodes in a model to specific execution providers)
    • Execution providers that statically register kernels (e.g. ONNX Runtime CPU Execution Provider) are supported by default
      • All execution providers that will be used at runtime MUST be registered when creating the ORT format model
    • Execution providers that compile nodes are optionally supported
      • currently this is limited to the NNAPI and CoreML Execution Providers
        • see here for details on using the NNAPI or CoreML Execution Providers with ONNX Runtime Mobile.

We do not currently offer backwards compatibility guarantees for ORT format models, as we will be expanding the capabilities in the short term and may need to update the internal format in an incompatible manner to accommodate these changes. You may need to regenerate the ORT format models to use with a future version of ONNX Runtime. Once the feature set stabilizes we will provide backwards compatibility guarantees.