aiomultiprocess
torchtyping
aiomultiprocess | torchtyping | |
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2 | 7 | |
1,674 | 1,337 | |
1.1% | - | |
6.6 | 3.2 | |
6 days ago | 11 months ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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aiomultiprocess
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What's New in Python 3.11?
> Why not just use multi processing?
Multiprocessing provides parallelism up to what the machine supports, but no additional degree of concurrency, asyncio provides a fairly high degree of concurrency, but no parallelism.
OF course, you can use them together to get both.
https://github.com/omnilib/aiomultiprocess
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Standalone electrical circuit simulation framework
Take a look at aiomultiprocess. It combines multiprocessing and asynchio to bypass the GIL for greatly increased performance.
torchtyping
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[D] Have their been any attempts to create a programming language specifically for machine learning?
Not really an answer to your question, but there are Python packages that try to solve the problem of tensor shapes that you mentioned, e.g. https://github.com/patrick-kidger/torchtyping or https://github.com/deepmind/tensor_annotations
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What's New in Python 3.11?
I disagree. I've had a serious attempt at array typing using variadic generics and I'm not impressed. Python's type system has numerous issues... and now they just apply to any "ArrayWithNDimensions" type as well as any "ArrayWith2Dimenensions" type.
Variadic protocols don't exist; many operations like stacking are inexpressible; the synatx is awful and verbose; etc. etc.
I've written more about this here as part of my TorchTyping project: [0]
[0] https://github.com/patrick-kidger/torchtyping/issues/37#issu...
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Can anyone point out the mistakes in my input layer or dimension?
also https://github.com/patrick-kidger/torchtyping
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[D] Anyone using named tensors or a tensor annotation lib productively?
FWIW I'm the author of torchtyping so happy to answer any questions about that. :) I think people are using it!
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[D] Ideal deep learning library
The one thing I really *really* wish got more attention was named tensors and the tensor type system. Tensor misalignment errors are a constant source of silently-failing bugs. While 3rd party libraries have attempted to fill this gap, it really needs better native support. In particular it seems like bad form to me for programmers to have to remember the specific alignment and broadcasting rules, and then have to apply them to an often poorly documented order of tensor indices. I'd really like to see something like tsalib's warp operator made part of the main library and generalized to arbitrary function application, like a named-tensor version of fold. But preferably using notation closer to that of torchtyping.
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[P] torchtyping -- documentation + runtime type checking of tensor shapes (and dtypes, ...)
Yes it does work with numerical literals! It support using integers to specify an absolute size, strings to specify names for dimensions that should all be consistently sized (and optionally also checks named tensors), "..." to indicate batch dimensions, and so on. See the full list here.
What are some alternatives?
think-async - 🌿 Exploring cooperative concurrency primitives in Python
jaxtyping - Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays. https://docs.kidger.site/jaxtyping/
fastapi-crudrouter - A dynamic FastAPI router that automatically creates CRUD routes for your models
equinox - Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
aiopath - 📁 Asynchronous pathlib for Python
tsalib - Tensor Shape Annotation Library (numpy, tensorflow, pytorch, ...)
example-hftish - Example Order Book Imbalance Algorithm
mypy - Optional static typing for Python
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
functorch - functorch is JAX-like composable function transforms for PyTorch.
bunny-storm - RabbitMQ asynchronous connector library for Python with built in RPC support
tensor_annotations - Annotating tensor shapes using Python types