Deeplearning4j

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deeplearning4j is an AI Frameworks & Infra tool that provides a suite of tools for deploying and training deep learning models using the JVM. It supports model import from Keras, TensorFlow, ONNX, and PyTorch, and includes a modular C++ library for math operations.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is deeplearning4j?

Deeplearning4j is a comprehensive ecosystem designed for deploying and training deep learning models within the Java Virtual Machine (JVM) environment. It offers a high-level API for building MultiLayerNetworks and ComputationGraphs, supporting various layers including custom ones. A key feature is its ability to import models from popular frameworks like Keras, TensorFlow, ONNX, and PyTorch. The suite includes ND4J, a general-purpose linear algebra library with over 500 operations, and SameDiff, an automatic differentiation/deep learning framework similar to TensorFlow's graph mode. DataVec provides ETL capabilities for machine learning data, handling diverse formats and sources. The underlying C++ library, LibND4J, ensures high performance with CPU and GPU acceleration. Deeplearning4j supports Windows, Linux, and macOS, with broad hardware compatibility.

Best used for

Ideal for developers and data scientists who need to build, train, and deploy deep learning models within a JVM ecosystem, integrate models from other popular frameworks like Keras or TensorFlow, and perform high-performance numerical computations. Especially valuable for those working with Java, Scala, Kotlin, or Clojure and requiring robust, scalable deep learning capabilities.

Common actions

deploy deep learning

train deep learning

import AI models

perform numerical computations

process machine learning data

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Capabilities

Key features

JVM deep learning
Keras model import
TensorFlow model import
ONNX/PyTorch model import
Linear algebra library
Automatic differentiation framework
ETL for ML data

Target Audience

developerdata scientist

Integrations

apache-sparkkerastensorflowpytorchonnx

Pricing & Plans

Open Source

Free

FAQs

What programming languages can I use with Deeplearning4j?

Deeplearning4j runs on the JVM, allowing you to use it with a wide variety of JVM-based languages. This includes Java, Scala, Kotlin, and Clojure, providing flexibility for developers working in different environments.

Does Deeplearning4j support model import from other deep learning frameworks?

Yes, Deeplearning4j supports importing models from several popular frameworks. You can import Keras models (including tf.keras), TensorFlow frozen model format (.pb), and ONNX/PyTorch models, making it versatile for integrating existing work.

What hardware does Deeplearning4j support for acceleration?

Deeplearning4j offers broad hardware support, including CUDA GPUs (versions 10.0, 10.1, 10.2, except OSX), x86 CPUs (x86_64, AVX2, AVX512), ARM CPUs (arm, arm64, armhf), and PowerPC (ppc64le). This ensures high-performance computation across various systems.

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Coding & Development › Backend & APIs Coding & Development › Open Source & Models Research & Education › Scientific Computing

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