7 ms·

It has but not as good as tensorflow support. This is one thing i miss truly in pytorch. Otherwise pytorch is wonderful.

applecrazy 7y ago
Doesn't CoreML abstract 90% of the actual model format/training source away? Last time I played with it in Xcode, it was painless to pull a pretrained TF model and use as-is.
- Q6T46nT668w6i3m 7y ago
  The number of operations supported by CoreML or Onyx is limited.
  - akhilcacharya 7y ago
    What's an example of a missing operator?
  - ghop02 7y ago
    Core ML 3 adds a ton of new operations, including control flow support. https://heartbeat.fritz.ai/whats-new-in-core-ml-3-d108d352e50a https://heartbeat.fritz.ai/whats-new-in-core-ml-3-d108d352e5...
    - throwawaybbqed 7y ago
      What about Android?
      - pjmlp 7y ago
        Android has its own incompatible world with NN and NDK fun. https://developer.android.com/ndk/reference/group/neural-networks https://developer.android.com/ndk/reference/group/neural-net... Or you get to use Tensorflow Lite and target both platforms, also with a smaller feature set than its big brother. https://www.tensorflow.org/lite/guide/get_started https://www.tensorflow.org/lite/guide/get_started
        m0zg 7y ago
        But, crucially, larger feature set than the likes of CoreML etc. You don't get access to the NPU that way though, at least not on iOS. The only acceleration option there seems to be Metal. Which isn't bad, but also not the most power efficient thing the hardware supports. Still though, it's the only game in town if you don't want to have insane un-debuggable headaches everywhere you deploy to device. Plus it also supports embedded Linux boards, and pretty much all current TPU-like things available there.
m0zg 7y ago
It has? Where? Who uses ONNX for anything? I doubt it even can work, period. The moment you do anything other than a bare bones classifier (which nobody really runs on devices - you need more complex models to solve real world problems) you run into ops unsupported by your inference framework, and that's if ONNX is supported by its tooling in the first place. In fact you could also run into unsupported ops during export as well: that is, it is somewhat likely that you won't even be able to export your model unless it consists entirely of the ops ONNX standard implements. The rest can be exported as opaque ops, but your inference tooling will not know what to do with those for sure.
- sairahul82 7y ago
  I agree with you only basic models works !!
- tsbinz 7y ago
  It can work for more than a bare bones classifier. It's certainly not painless and sometimes you need some manual work to translate your model but work it does ...
- throwawaybbqed 7y ago
  For mobile, the situation seems worse than a year ago. TF Mobile mostly worked. The current situation with TF lite is a joke .. tried a real-world Pytorch -> ONYX -> TF/TF lite and it has been weeks of misery.

applecrazy 7y ago

Doesn't CoreML abstract 90% of the actual model format/training source away? Last time I played with it in Xcode, it was painless to pull a pretrained TF model and use as-is.

Q6T46nT668w6i3m 7y ago
The number of operations supported by CoreML or Onyx is limited.
- akhilcacharya 7y ago
  What's an example of a missing operator?
- ghop02 7y ago
  Core ML 3 adds a ton of new operations, including control flow support. https://heartbeat.fritz.ai/whats-new-in-core-ml-3-d108d352e50a https://heartbeat.fritz.ai/whats-new-in-core-ml-3-d108d352e5...
  - throwawaybbqed 7y ago
    What about Android?
    - pjmlp 7y ago
      Android has its own incompatible world with NN and NDK fun. https://developer.android.com/ndk/reference/group/neural-networks https://developer.android.com/ndk/reference/group/neural-net... Or you get to use Tensorflow Lite and target both platforms, also with a smaller feature set than its big brother. https://www.tensorflow.org/lite/guide/get_started https://www.tensorflow.org/lite/guide/get_started
      - m0zg 7y ago
        But, crucially, larger feature set than the likes of CoreML etc. You don't get access to the NPU that way though, at least not on iOS. The only acceleration option there seems to be Metal. Which isn't bad, but also not the most power efficient thing the hardware supports. Still though, it's the only game in town if you don't want to have insane un-debuggable headaches everywhere you deploy to device. Plus it also supports embedded Linux boards, and pretty much all current TPU-like things available there.

m0zg 7y ago

It has? Where? Who uses ONNX for anything? I doubt it even can work, period. The moment you do anything other than a bare bones classifier (which nobody really runs on devices - you need more complex models to solve real world problems) you run into ops unsupported by your inference framework, and that's if ONNX is supported by its tooling in the first place. In fact you could also run into unsupported ops during export as well: that is, it is somewhat likely that you won't even be able to export your model unless it consists entirely of the ops ONNX standard implements. The rest can be exported as opaque ops, but your inference tooling will not know what to do with those for sure.

sairahul82 7y ago
I agree with you only basic models works !!
tsbinz 7y ago
It can work for more than a bare bones classifier. It's certainly not painless and sometimes you need some manual work to translate your model but work it does ...
throwawaybbqed 7y ago
For mobile, the situation seems worse than a year ago. TF Mobile mostly worked. The current situation with TF lite is a joke .. tried a real-world Pytorch -> ONYX -> TF/TF lite and it has been weeks of misery.