stensorflow and tensorflow_scala

The tools are complements: the first tool, `ekrich/stensorflow`, provides the low-level Scala Native bindings to the TensorFlow C API, which can then be utilized by higher-level Scala wrappers like the second tool, `eaplatanios/tensorflow_scala`, to offer a more idiomatic Scala API for TensorFlow.

stensorflow
45
Emerging
tensorflow_scala
45
Emerging
Maintenance 13/25
Adoption 8/25
Maturity 16/25
Community 8/25
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 19/25
Stars: 42
Forks: 3
Downloads: —
Commits (30d): 0
Language: Scala
License: —
Stars: 941
Forks: 94
Downloads: —
Commits (30d): 0
Language: Scala
License: Apache-2.0
No Package No Dependents
Stale 6m No Package No Dependents

About stensorflow

ekrich/stensorflow

Scala Native support for the TensorFlow C API on Linux and macOS

This library enables Scala Native developers to integrate TensorFlow's powerful machine learning capabilities directly into their high-performance native applications. It provides bindings to the TensorFlow C API, allowing you to use existing TensorFlow models and operations within Scala Native projects. This is specifically for Scala Native developers looking to leverage TensorFlow without the JVM.

Scala Native development machine learning integration high-performance computing native application development deep learning frameworks

About tensorflow_scala

eaplatanios/tensorflow_scala

TensorFlow API for the Scala Programming Language

This is a tool for machine learning engineers and data scientists who primarily use Scala for their development. It allows you to build, train, and deploy machine learning models, especially neural networks, using the TensorFlow framework directly within Scala. You can input raw data, define your model architecture, train it, and then analyze the results, similar to how Python users work with TensorFlow.

machine-learning-engineering deep-learning-development data-science neural-network-training model-deployment

Related comparisons

stensorflow and TensorFlowOnSpark

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