xla
A machine learning compiler for GPUs, CPUs, and ML accelerators (by openxla)
gomlx
GoMLX -- Accelerated ML Libraries for Go (by gomlx)
| xla | gomlx | |
|---|---|---|
| 3 | 4 | |
| 2,215 | 83 | |
| 5.5% | - | |
| 10.0 | 9.6 | |
| 3 days ago | 5 days ago | |
| C++ | Go | |
| Apache License 2.0 | 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.
xla
Posts with mentions or reviews of xla.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-09.
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VSL; Vlang's Scientific Library
Would it make sense to have a backend support for OpenXLA, Apache TVM, Jittor or other similar to get free GPU, TPU and other accelerators for free ?
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GoMLX -- Accelerated ML for Go
GoMLX (github.com/gomlx/gomlx) is a fast and (relatively) easy-to-use set of ML libraries built on top of OpenXLA, a just-in-time compiler of numeric computations for CPU, TPU and GPUs.
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Will go ever get C/Java style exceptions?
I'm doing something that uses XLA to quickly execute computation graphs (in CPU, GPU or TPUs). One builds the graph, and later executes it (quickly) on actual values (sometimes large tensors).
gomlx
Posts with mentions or reviews of gomlx.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-20.
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Open source contributions?
Well, since you offered, and you have Python experience, if you are looking for a giant itch to scratch -- actually what started me in this project -- check github.com/gomlx/gomlx.
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Just how niche is Go?
On that topic, let me advertise an experimental alternative in Go for the ML market: GoMLX -- it uses XLA, same engine that powers TensorFlow and Jax (so presumably same speed/accelerator support). It has a tutorial, examples and already has a working diffusion model -- LLMs coming soon.
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If you had a wish for a Go project – library, framework, tool, or something else –, what would it be?
A bit experimental still, but moving fast and many examples (even a [Diffusion example](https://github.com/gomlx/gomlx/blob/main/examples/oxfordflowers102/OxfordFlowers102_Diffusion.ipynb)).
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GoMLX -- Accelerated ML for Go
GoMLX (github.com/gomlx/gomlx) is a fast and (relatively) easy-to-use set of ML libraries built on top of OpenXLA, a just-in-time compiler of numeric computations for CPU, TPU and GPUs.
What are some alternatives?
When comparing xla and gomlx you can also consider the following projects:
vsl - V library to develop Artificial Intelligence and High-Performance Scientific Computations
gonb - GoNB, a Go Notebook Kernel for Jupyter
jittor - Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.
eris - Error handling library with readable stack traces and flexible formatting support 🎆
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators