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Apache TVM vs Deeplearning4j

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.

Apache TVM

Free plan Open source AndroidAPIiPhone+5

TVM offers broad model, backend, and runtime support with Python-based optimization. Treat its RPC server as a high-trust interface because API users can write files and execute code remotely.

Visit Apache TVM

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.

Visit Deeplearning4j

The facts

Apache TVMDeeplearning4j
Free planYesYes
Open sourceYesNot published
Runs onAndroid, API, iPhone, Linux, Mac, Self-hosted, Web, WindowsAPI, Linux, Mac, Self-hosted, Windows
Lowest paid planFree or on requestFree or on request
Plans published11
MakerNot publishedNot published
Websitetvm.apache.orgdeeplearning4j.konduit.ai
On deep learning software#1 of 35#5 of 35

Apache TVM: what it does well

  • Imports PyTorch, ONNX, and TensorFlow Lite models
  • Customizes optimization in Python without recompiling TVM
  • Supports CPU, GPU, and multiple deployment backends
  • Lightweight runtime covers mobile devices and browsers
  • Free open-source software under Apache License 2.0

What to know first

  • RPC assumes trusted users and networks
  • RPC API users can write arbitrary files and execute code remotely

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.

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