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Comment by amrrs | Hacker News Reader

I think tensorflow dominates industry purely because of its capability of exporting the model into a coreml Android model or easy of moving it to production in a GCP environment or in whatever form. Pytorch might have to build a good production pipeline around it to catch up in this game.

With fastai module that's built on Pytorch learning and developing Deep Learning solutions have become a lot easier. So there's a real game on now

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And simply TensorFlow was there earlier, so people implemented stuff in it. I think there's more inertia in industry, whereas researchers may more easily switch frameworks between two papers.

And Keras was there too which made a lot of people us Keras with TF. I doubt if TF would have risen to this popularity hadn't it been to Keras.

Exactly this. Keras made TF easy.

You can do the same with PyTorch, right? Just export the model as .onnx and import it with whatever inference engine you like.

This sounds like in theory and makes you curse in practice. Trained TF model -> deployment using TF Lite is the most robust pipeline currently as far as I know.

There are lots of improvements going into pytorch for mobile at the moment, but for the moment I'll wait and see how it turns out - I didn't have much fun with caffe2 when "train in pytorch and deploy with caffe2" was the storyline FB pushed (e.g. problems with binary size and slow depthwise convolutions) so not too eager to migrate back at the moment.

It's difficult because the graph is defined running Python code, which can basically do anything. As I understand it you have to actually run the model and sort of introspect it to work out what the ONNX graph should be.

Whereas the Tensorflow API actually creates a static graph that can be easily converted to ONNX.

Edit: They talk about this problem in the article:

> Although straightforward, tracing has its downsides. For example, it can’t capture control flow that didn’t execute. For example, it can’t capture the false block of a conditional if it executed the true block.

> Script mode takes a function/class, reinterprets the Python code and directly outputs the TorchScript IR. This allows it to support arbitrary code, however it essentially needs to reinterpret Python.

I've done this with OpenVino and it works well enough. There are some gotchas with Onnx, some layers break or have weird bugs (upsample in particular used to be a problem).

researchers are like Francois Chollet & many more even jeff dean and it is the greatness of the community that it's pretty open to optimization "Why do researchers love PyTorch? Simplicity. It’s similar to numpy, very pythonic, and integrates easily with the rest of the Python ecosystem. For example, you can simply throw in a pdb breakpoint anywhere into your PyTorch model and it’ll work. In TensorFlow, debugging the model requires an active session and ends up being much trickier. Great API. Most researchers prefer PyTorch’s API to TensorFlow’s API. This is partially because PyTorch is better designed and partially because TensorFlow has handicapped itself by switching APIs so many times (e.g. ‘layers’ -> ‘slim’ -> ‘estimators’ -> ‘tf.keras’). Performance. Despite the fact that PyTorch’s dynamic graphs give strictly less opportunity for optimization, there have been many anecdotal reports that PyTorch is as fast if not faster than TensorFlow. It's not clear if this is really true, but at the very least, TensorFlow hasn't gained a decisive advantage in this area."

common people do want to appreciate & adopt the things that seems fit to the knowledge sphere at present from the researchers. tensorflow approaches are better and respected each and everyone of the community as well in exchange enlightened us new ways of understanding of ml solutions. it have turned into a family “If you want to go fast, go alone. If you want to go far, go together.” and given the assets alphabet have a common man can turn into researchers! e.g. >> https://learn.grasshopper.app take this for example "Learn to code anywhere. Grasshopper is available on iOS, Android, and all web browsers. Your progress syncs seamlessly between devices." << this is the status quo ! it's a gift of a lifetime for generations !

This feels like it was written by GPT2-Small.

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