playtorch
PlayTorch is a framework for rapidly creating mobile AI experiences. (by facebookresearch)
functorch
functorch is JAX-like composable function transforms for PyTorch. (by pytorch)
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playtorch | functorch | |
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10 | 11 | |
818 | 1,372 | |
- | 0.4% | |
0.0 | 0.0 | |
6 months ago | 8 days ago | |
MDX | Jupyter Notebook | |
MIT License | BSD 3-clause "New" or "Revised" License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
playtorch
Posts with mentions or reviews of playtorch.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-26.
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[D] Pytorch or TensorFlow for development and deployment?
First, PlayTorch is a thing. That could replace tflite for mobile.
- PlayTorch – Build your AI powered mobile prototypes in minutes
- ¿Data Science o Diseño Web?
- [D] Anyone tried using Apple’s Swift for Machine Learning
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using a teachable machine model in react native
maybe this package can help you here
- Easy to use set of tools to create on-device ML demos on Android and iOS. Unlock the vast potential of AI innovations.
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[D] Are you using PyTorch or TensorFlow going into 2022?
I'd say stick with PT for now! If you're interested in deployment, might wanna look into TF, but I'd use PyTorch Live to build a mobile application before you do that!
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Introducing PyTorch Live!!
Learn more here - https://pytorch.org/live/
- PyTorch Live: an easy to use command line interface, a React Native package, and a React Native template to build, deploy, and share machine learning models
functorch
Posts with mentions or reviews of functorch.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-12-08.
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What is the most efficient approach to ensemble a pytorch actor-critic model?
I would suggest checking https://pytorch.org/functorch/ and https://github.com/metaopt/torchopt for efficient inference and training with ensembles (e.g., t be independent actors in a multi-agent setting or multiple critics).
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[P] Multidimensional array batch indexing for pytorch and numpy
There were some bugs still with advanced indexing in an older release of functorch, I believe they should be fixed now though: https://github.com/pytorch/functorch/pull/862
- Functorch: Jax-like composable function transforms for PyTorch
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Jax vs. Julia (Vs PyTorch)
Tangentially related but there is an effort to get some of the features of JAX into PyTorch: https://pytorch.org/functorch/
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[D] Current State of JAX vs Pytorch?
Fwiw, composable vmap and stuff like that have also been implemented in PyTorch now - see functorch :) https://github.com/pytorch/functorch
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[D] Ideal deep learning library
Fwiw, it’s not like Pytorch’s design prevents function transformations from being implemented. See functorch for an example of grad/vmap function transforms: https://github.com/pytorch/functorch
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[P] Made Some Pytorch Modules For Agent Systems
You may find vmap from functorch to be quite useful: https://github.com/pytorch/functorch
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[D] Are you using PyTorch or TensorFlow going into 2022?
If you're interested in function transformations in PyTorch, try out functorch :) https://github.com/pytorch/functorch
- PyTorch: Where we are headed and why it looks a lot like Julia (but not exactly)
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Show HN: How does Jax allocate memory on a TPU? An interactive C++ walkthrough
The pytorch programming model is just really hard to adapt to an XLA-like compiler. Imperative python code doesn't translate to an ML graph compiler particularly well; Jax's API is functional, so it's easier to translate to the XLA API. By contrast, torch/xla uses "lazy tensors" that record the computation graph and compile when needed. The trouble is, if the compute graph changes from run to run, you end up recompiling a lot.
I guess in Jax you'd just only apply `jax.jit` to the parts where the compute graph is static? I'd be curious to see examples of how this works in practice. Fwiw, there's an offshoot of pytorch that is aiming to provide this sort of API (see https://github.com/pytorch/functorch and look at eager_compilation.py).
(Disclaimer: I worked on this until quite recently.)
What are some alternatives?
When comparing playtorch and functorch you can also consider the following projects:
equinox - Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more