uncertainties
Transparent calculations with uncertainties on the quantities involved (aka "error propagation"); calculation of derivatives. (by lmfit)
tangent
Source-to-Source Debuggable Derivatives in Pure Python (by google)
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uncertainties | tangent | |
---|---|---|
1 | 2 | |
528 | 2,280 | |
2.5% | - | |
6.5 | 10.0 | |
8 days ago | over 1 year ago | |
Python | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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.
uncertainties
Posts with mentions or reviews of uncertainties.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-07.
tangent
Posts with mentions or reviews of tangent.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-12-25.
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[D] How AD is implemented in JAX/Tensorflow/Pytorch?
Thank you so much for the detail explaination! This remind me of tangent, an abandoned (?) SCT built by google couple of years ago. https://github.com/google/tangent
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Trade-Offs in Automatic Differentiation: TensorFlow, PyTorch, Jax, and Julia
No, autograd acts similarly to PyTorch in that it builds a tape that it reverses while PyTorch just comes with more optimized kernels (and kernels that act on GPUs). The AD that I was referencing was tangent (https://github.com/google/tangent). It was an interesting project but it's hard to see who the audience is. Generating Python source code makes things harder to analyze, and you cannot JIT compile the generated code unless you could JIT compile Python. So you might as well first trace to a JIT-compliable sublanguage and do the actions there, which is precisely what Jax does. In theory tangent is a bit more general, and maybe you could mix it with Numba, but then it's hard to justify. If it's more general then it's not for the standard ML community for the same reason as the Julia tools, but then it better do better than the Julia tools in the specific niche that they are targeting. Jax just makes much more sense for the people who were building it, it chose its niche very well.
What are some alternatives?
When comparing uncertainties and tangent you can also consider the following projects:
pint - Laravel Pint is an opinionated PHP code style fixer for minimalists.
autograd - Efficiently computes derivatives of numpy code.