Tensorflex
Tensorflow bindings for the Elixir programming language, enabling machine learning inference and tensor operations.
What is Tensorflex?
Tensorflex is a library that provides native TensorFlow bindings for Elixir, enabling developers to load pre-trained models, run inference, and perform tensor operations directly within the Elixir ecosystem. It bridges TensorFlow's machine learning capabilities with Elixir's functional programming strengths, making ML accessible in BEAM environments like Erlang/OTP. The library supports tasks such as image classification, sentiment analysis, and data manipulation through a seamless Elixir interface.
Elixir developers working on machine learning projects who need to integrate pre-trained TensorFlow models into BEAM-based applications, such as web services or distributed systems. It is also suitable for data scientists or ML engineers familiar with Elixir who want to perform inference or tensor operations without leaving the Elixir environment.
Developers choose Tensorflex because it offers a high-performance, idiomatic Elixir interface to TensorFlow's C API, with optimizations like fast CSV loading and direct integration. Its unique selling point is enabling ML workflows within Elixir's functional paradigm, avoiding the need to switch to Python or other languages for TensorFlow tasks, which simplifies deployment in BEAM environments.
Overview
Tensorflow bindings for the Elixir programming language :muscle:
Use Cases
Best For
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