tensorflow
Haskell bindings for TensorFlow (by tensorflow)
accelerate
Embedded language for high-performance array computations (by AccelerateHS)
tensorflow | accelerate | |
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2 | 9 | |
1,569 | 886 | |
0.6% | 0.3% | |
2.4 | 5.3 | |
9 months ago | 8 days ago | |
Haskell | Haskell | |
LicenseRef-Apache | 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.
tensorflow
Posts with mentions or reviews of tensorflow.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-27.
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Is Haskell okay for prototyping machine learning models for research (discovery and exploration)
If you require JAX, you'll be out of luck using Haskell. But there is Hasktorch, http://hasktorch.org/, and Tensorflow bindings in Haskell, https://github.com/tensorflow/haskell. Both seem to be actively maintained.
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Haskell deep learning tutorials [Blog]
First I still have to admit that Haskell ML libraries have a large space for improvement. On the other hand, there exists bindings for Tensorflow. As can be seen from Github, they have recently added support for libtensorflow v2.3.0.
accelerate
Posts with mentions or reviews of accelerate.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-23.
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Should I use newer ghc?
Someone has opened a PR for accelerate here https://github.com/AccelerateHS/accelerate/pull/525 (sadly seems not actively maintained at the moment, but that can always change if people care enough). I agree for an executable you should freeze your dependencies and compiler version, and using 8.10 is fine. Although there are tons of improvements in 9.2+
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Haskell deep learning tutorials [Blog]
Backprop is a neat library. However, I guess its use case is if you actually don't want to go for anything standard like Torch or TF (perhaps for research?) For instance, if I were to use something like Accelerate for GPU acceleration, or some other computation-oriented library, then I would mix it with Backprop. Previously, I have benefited from Backprop in a ConvNet tutorial and I liked it.
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I made a petition to get the accelerate project for Haskell some funding.
Wait, really? Here's a conversation I had with him: https://github.com/AccelerateHS/accelerate/discussions/528
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Who is researching array languages these days?
I know Accelerate is being developed at Utrecht University in the Netherlands. You can look at publications by Trevor McDonell to get a taste of what they are doing.
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Next Decade in Languages: User Code on the GPU
I’m personally a big fan of http://www.acceleratehs.org / https://github.com/AccelerateHS/accelerate-llvm
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Introduction to Doctests in Haskell
Looking for a few projects that make use of it, I found accelerate, hawk, polysemy and pretty-simple, so I'll be interested to poke around in their code and see how they have things set up.
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Monthly Hask Anything (March 2022)
There's accelerate for GPU computing and hmatrix for bindings to BLAS and LAPACK.
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Idris2+WebGL, part #12: Linear algebra with linear types... not great
I'm toying with the idea of replacing vector values with vector generators, where e.g. v1 + v2 is not evaluated to a new vector, but to a vector program. This is similar to the approaches of Accelerate and TensorFlow. On the flip side, I don't think I could get rid of the overhead, and I expect much smaller computation loads than aforementioned libraries, so overheads could be very significant. The added benefit of using vector generators is that the generator could not only be evaluated, but also be turned into a Latex formula.
What are some alternatives?
When comparing tensorflow and accelerate you can also consider the following projects:
HLearn-algebra - Homomorphic machine learning
dhall - Maintainable configuration files
haskell-ml - Various examples of machine learning, in Haskell.
accelerate-bignum - Fixed-length large integer arithmetic for Accelerate
rc - Reservoir Computing, an RNN flavor
accelerate-cuda - DEPRECATED: Accelerate backend for NVIDIA GPUs
grenade - Deep Learning in Haskell
hyper-haskell-server - The strongly hyped Haskell interpreter.
genetics - A Genetic Algorithm library in Haskell
accelerate-fft - FFT library for Haskell based on the embedded array language Accelerate
neural - Neural Nets in native Haskell
feldspar-compiler - This is the compiler for the Feldspar Language.
tensorflow vs HLearn-algebra
accelerate vs dhall
tensorflow vs haskell-ml
accelerate vs accelerate-bignum
tensorflow vs rc
accelerate vs accelerate-cuda
tensorflow vs grenade
accelerate vs hyper-haskell-server
tensorflow vs genetics
accelerate vs accelerate-fft
tensorflow vs neural
accelerate vs feldspar-compiler