Why can't Go be popular for machine learning?

This page summarizes the projects mentioned and recommended in the original post on /r/golang

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  1. Gorgonia

    Gorgonia is a library that helps facilitate machine learning in Go.

    CGO? Too much overhead in calling C functions (in which you can wrap libtorch or TF C++ code). And too much struggling woth CUDA (actually all GPU stuff). But, there are interesting attempts: https://github.com/gorgonia/gorgonia (I love it most), https://github.com/sugarme/gotch (bindings to libtorch), https://github.com/nlpodyssey/spago.

  2. InfluxDB

    InfluxDB high-performance time series database. Collect, organize, and act on massive volumes of high-resolution data to power real-time intelligent systems.

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  3. gotch

    Go binding for Pytorch C++ API (libtorch)

    CGO? Too much overhead in calling C functions (in which you can wrap libtorch or TF C++ code). And too much struggling woth CUDA (actually all GPU stuff). But, there are interesting attempts: https://github.com/gorgonia/gorgonia (I love it most), https://github.com/sugarme/gotch (bindings to libtorch), https://github.com/nlpodyssey/spago.

  4. spaGO

    Discontinued Self-contained Machine Learning and Natural Language Processing library in Go

    CGO? Too much overhead in calling C functions (in which you can wrap libtorch or TF C++ code). And too much struggling woth CUDA (actually all GPU stuff). But, there are interesting attempts: https://github.com/gorgonia/gorgonia (I love it most), https://github.com/sugarme/gotch (bindings to libtorch), https://github.com/nlpodyssey/spago.

  5. tensorflow

    An Open Source Machine Learning Framework for Everyone

    Go bindings for Tensorflow were supported by Google for a long time. The documentation for Go, however has since disappeared.

  6. tfgo

    Tensorflow + Go, the gopher way

    Paolo Galeone has improved bindings (tfgo) that can be used for training and deployment.

  7. CodeRabbit

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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