TensorComprehensions
A domain specific language to express machine learning workloads. (by facebookresearch)
TensorOperations.jl
Julia package for tensor contractions and related operations (by Jutho)
TensorComprehensions | TensorOperations.jl | |
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1 | 3 | |
1,695 | 413 | |
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0.0 | 8.5 | |
about 4 years ago | 14 days ago | |
C++ | Julia | |
Apache License 2.0 | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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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.
TensorComprehensions
Posts with mentions or reviews of TensorComprehensions.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-02-15.
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Programming Languages where element-wise matrix notation is possible
Dex from DeepMind have it natively. TensorComprehensions is also cool.
TensorOperations.jl
Posts with mentions or reviews of TensorOperations.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-27.
- Einsum in 40 Lines of Python
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Absolutely suck at tech stuff,but Julia makes me want to learn coding. Wish me luck.
Sometimes broadcasting feels like magic to me. It just works more often than not even when I am confused with the dimensions. If you do a lot of Tensor stuff it's also worth checking out Einstein notation (https://github.com/Jutho/TensorOperations.jl)
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Programming Languages where element-wise matrix notation is possible
There are some libraries and macros for Einstein notation and related ideas, like TensorOperations.jl in Julia, einsum in numpy which someone already mentioned, and some small-scale/research languages like Diderot and Egison. In the mainstream, I guess languages generally use for loops or list comprehensions and try to recover vectorisation from that after the fact, but don’t guarantee it. Those that do make guarantees tend to use combinators that are matrixwise/function-level. I admit I pretty much categorically prefer the latter so I’m not as aware of the state of this as I’d like to be able to help.
What are some alternatives?
When comparing TensorComprehensions and TensorOperations.jl you can also consider the following projects:
Halide - a language for fast, portable data-parallel computation
seq - A high-performance, Pythonic language for bioinformatics
NDTensors.jl - A Julia package for n-dimensional sparse tensors.
TensorFlock - A small functional tensor language with Einstein summation notation convention and shape-checking at compile-time.
Tullio.jl - ⅀
stu - Build automation
Grassmann.jl - ⟨Grassmann-Clifford-Hodge⟩ multilinear differential geometric algebra
MPS - JetBrains Meta programming System
tensorflow - An Open Source Machine Learning Framework for Everyone
ThinkJuliaFR.jl - Introduction à la programmation en Julia (livre)
TensorComprehensions vs Halide
TensorOperations.jl vs Halide
TensorComprehensions vs seq
TensorOperations.jl vs NDTensors.jl
TensorComprehensions vs TensorFlock
TensorOperations.jl vs Tullio.jl
TensorComprehensions vs stu
TensorOperations.jl vs Grassmann.jl
TensorComprehensions vs MPS
TensorOperations.jl vs TensorFlock
TensorComprehensions vs tensorflow
TensorOperations.jl vs ThinkJuliaFR.jl