[HN Gopher] Show HN: Txeo - A Modern C++ Wrapper for TensorFlow
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Show HN: Txeo - A Modern C++ Wrapper for TensorFlow
Txeo is a lightweight and intuitive C++ wrapper for TensorFlow,
designed to simplify TensorFlow C++ development while preserving
high performance and flexibility. Built entirely with Modern C++,
Txeo allows developers to use TensorFlow with the ease of a high-
level API, eliminating the complexity of its low-level C++
interface.
Author : rdabra
Score : 33 points
Date : 2025-02-21 16:40 UTC (6 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| klaussilveira wrote:
| Love the ergonomics:
| https://github.com/rdabra/txeo/blob/main/examples/txeo_predi...
| pjmlp wrote:
| Yes, C++ can be made as nice as C#, Kotlin and such.
|
| That is what made me appreciate it with modern frameworks like
| OWL and Turbo Vision back in the day.
|
| Unfortunately too many folks insist in C style coding, which
| kills ergonomics like those.
|
| Additionally now at C++20 [0], there are plenty of improvements
| for Python like coding.
|
| [0] - C++23 is pretty much WIP
| SunlitCat wrote:
| > Python like coding
|
| Please noooooooooo! I don't want to have to watch out how
| many indentations I've made, just for making sure I don't get
| any weird errors! (Just kidding!)
|
| Do you have any (quick) examples what you mean with python
| like?
| kevmo314 wrote:
| If this had come five years ago perhaps TensorFlow could've stood
| a chance against PyTorch. Switching from TensorFlow to PyTorch
| was such a breath of fresh air, I definitely could have used
| something like this.
| ipsum2 wrote:
| Why? TensorFlow has been abandoned by Google. Open source uses
| PyTorch, and internally at Google, all new model development is
| done in Jax. Only TensorFlow-Serving and tfdata are still used
| parts of TensorFlow.
| mindcrime wrote:
| The project looks fairly active, based on the commit history:
|
| https://github.com/tensorflow/tensorflow/commits/master/
| ototot wrote:
| It could be the code still stay there, but the direction has
| been changed.
| saidinesh5 wrote:
| What about embedded/mobile environments?
|
| Are these other APIs as good as leveraging the hardware
| accelerators via hardware vendor provided drivers as tensorflow
| these days? (For nnapi etc ...).
|
| I haven't touched these apis recently but back in 2020 or so
| the easiest way to use models like yolo was via tflite on those
| systems.
| synergy20 wrote:
| it's not abandoned nor deprecated, google still depends on it
| to run the models in production. Google just splits the model
| development into JAX. They're companions to each other and
| equally important down the road.
| albertzeyer wrote:
| I have worked both with the TensorFlow C++ API and the TensorFlow
| Python API. While the TF Python API is basically only a wrapper
| around the TF C++ API, it adds a lot of things on top, e.g. many
| higher-level functions you would want to use to define neural
| networks, etc. If you know PyTorch, think about torch.nn. Most
| crucially, calculating the gradients, i.e. doing
| backprop/autograd, was also purely implemented in Python. Even to
| define the gradient per each operation was done in Python. The
| C++ core did not know anything about this. (I'm not exactly sure
| how much this changed with eager mode and gradient tapes
| though...)
|
| So, that makes implementing training with only the C++ API quite
| a big task. You first need to define all the gradients, and then
| implement backprop / autograd.
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