this post was submitted on 25 Nov 2023
1 points (100.0% liked)
Machine Learning
1 readers
1 users here now
Community Rules:
- Be nice. No offensive behavior, insults or attacks: we encourage a diverse community in which members feel safe and have a voice.
- Make your post clear and comprehensive: posts that lack insight or effort will be removed. (ex: questions which are easily googled)
- Beginner or career related questions go elsewhere. This community is focused in discussion of research and new projects that advance the state-of-the-art.
- Limit self-promotion. Comments and posts should be first and foremost about topics of interest to ML observers and practitioners. Limited self-promotion is tolerated, but the sub is not here as merely a source for free advertisement. Such posts will be removed at the discretion of the mods.
founded 1 year ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
I get access to some awesome data loading and preprocessing tools with the pytorch backend then I swap to tensorflow for quantization for tflite model with almost no fuss.
It was somewhat annoying going from torch to onnx to tflite previously. There's a bunch of small roadbumps that you have to deal with.
Yeah, unifying these tools feels like the best way to go for me too. I also like JAX for a similar reason because there are 50 different libraries with different use cases and it is easy to mix parts of them together, due to the common infrastructure. Like Keras losses + flax models + optax training + my custom libraries super classes. It's great tbh.