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Deeplearning4j vs JAX

Both stand on deep learning software. Which one you can start free with, which one lets you read the code, and what each costs once you pay.

Deeplearning4j

Free plan APILinuxMac+2

Choose Deeplearning4j for open-source deep learning in JVM applications, especially when Java or Scala, Spark training, or CUDA acceleration fits the project. It also offers model import and several deployment settings. Its quickstart is aimed at professional Java developers, and documentation covers version 1.0.0-M2.1.

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JAX

Free plan LinuxMacSelf-hosted+1

Choose JAX if you want a free Python library for numerical computing with composable transformations and multiple accelerator backends. Check the experimental status of the relevant platform support, and account for its documented connection security defaults and possible numerical differences under JIT compilation.

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The facts

Deeplearning4jJAX
Free planYesYes
Open sourceNot publishedNot published
Runs onAPI, Linux, Mac, Self-hosted, WindowsLinux, Mac, Self-hosted, Windows
Lowest paid planFree or on requestFree or on request
Plans published11
MakerNot publishedNot published
Websitedeeplearning4j.konduit.aidocs.jax.dev
On deep learning software#5 of 35#9 of 35

Deeplearning4j: what it does well

  • Builds, trains and deploys neural networks in Java and Scala.
  • Supports CUDA GPU acceleration and CPU computation.
  • Enables distributed training across CPU or GPU clusters with Apache Spark.
  • Imports models from Keras, TensorFlow and ONNX.
  • Libraries use the Apache License 2.0.

What to know first

  • The quickstart targets professional Java developers familiar with production deployments.
  • The current documentation covers version 1.0.0-M2.1.

JAX: what it does well

  • Free, open-source Python library.
  • NumPy-style API for array computation.
  • Composes compilation, batching, differentiation, and parallelization transformations.
  • Code can target CPU, GPU, and TPU backends.
  • Supports local training, GPU acceleration, and distributed training.

What to know first

  • Windows x86_64 CPU support is experimental.
  • Apple GPU support on Apple ARM is experimental.
  • Distributed coordination connections are unauthenticated and unencrypted by default.
  • JIT compilation can produce numerically different results from eager computation.

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