tensor_annotations
FL
tensor_annotations | FL | |
---|---|---|
2 | 1 | |
158 | 1 | |
-0.6% | - | |
5.8 | 10.0 | |
10 months ago | over 1 year ago | |
Python | ||
Apache License 2.0 | - |
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.
tensor_annotations
-
[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
-
Matrix Multiplication Inches Closer to Mythic Goal
I've explored this space quite a bit. In my view, static checking should be the goal.
https://github.com/deepmind/tensor_annotations and tsastanley seem to be the most far along. I've developed a mypy plugin that does similarly off of the "Named Tensor" dynamic feature (which isn't well supported yet), but haven't released it yet.
FL
-
[D] Have their been any attempts to create a programming language specifically for machine learning?
I absolutely agree with the OP. Out of the same frustration I actually ended up designing my own language and wrote a compiler for it, and now I use it for all my ML modelling. It probably only solves my particular problems and I don't expect it to be very useful for anyone else, but here goes, in case anyone is curious: https://github.com/waveworks-ai/fl
What are some alternatives?
dex-lang - Research language for array processing in the Haskell/ML family
miniF2F - Formal to Formal Mathematics Benchmark
torchtyping - Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.
TablaM - The practical relational programing language for data-oriented applications
tiny-cuda-nn - Lightning fast C++/CUDA neural network framework
MindsDB - The platform for customizing AI from enterprise data
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
jaxtyping - Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays. https://docs.kidger.site/jaxtyping/
hasktorch - Tensors and neural networks in Haskell