Eclipse Deeplearning4j
A distributed, deep learning library for Java virtual machine (JVM)
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| + | Java Support | Offers a programming library for Java virtual machine (JVM) environments. |
|---|---|---|
| + | Distributed Computing | Facilitates distributed deep learning across multiple CPUs and GPUs. |
| + | Model Import | Allows importing of Keras models, TensorFlow models, and ONNX (Open Neural Network Exchange) models, reducing the need to rebuild models from scratch. |
| + | Spark Integration | Integrates with Apache Spark for large-scale data processing and model training. |
| + | Data Management | Includes tools for data cleaning, ETL (Extract, Transform, Load) operations, preprocessing, and conversion into neural network-compatible vectors. |
| + | Deployment Options | Supports deployment via REST, Spark, or embedded environments like Android or Raspberry Pi. |
| + | Enterprise Security | Provides secure connections to enterprise environments using authentication protocols like Kerberos. |
| + | Python Interoperability | Provides interaction with the Python ecosystem through CPython bindings. |
| - | Model Compatibility | Due to native components, it may not be compatible with all systems. |
| - | Performance Overhead | Java virtual machine can introduce some performance overhead compared to natively compiled languages like Python. |
| - | Data Preprocessing | Data preprocessing tools might require more manual configuration compared to higher-level abstractions in other frameworks. |
| - | Development Pace | Updates and new features may be implemented less frequently compared to actively maintained frameworks. |
| - | Learning Curve | Requires familiarity with Java and potentially Scala/Clojure. Setting up the project involves configuring Maven dependencies which may be complex for beginners. |
System Requirements
Version ↓
| # | Minimum | Recommended |
|---|---|---|
| 1 | 4 GB | 8 GB |
| 2 |
|
Developer
Adam Gibson, Chris Nicholson, Josh Patterson, Other contributors
Written in
Java, C++, Python, JavaScript, Scala, Cuda
Initial Release
22 February 2014
License
Categories
Deep Learning, Machine Learning, Artificial Intelligence, Framework
Alternatives
Deep Learning
TensorFlow Apache MXNet Apache SystemDS Caffe OpenNN PyTorch The Microsoft Cognitive Toolkit Torch Weka
Machine Learning
Massive Online Analysis TensorFlow Apache Mahout Apache Spark Apache MXNet Apache SystemDS MALLET mlpack OpenCV Orange PyTorch scikit-learn The Microsoft Cognitive Toolkit Torch Weka Yooreeka
Artificial Intelligence
Accord.NET AForge.NET TensorFlow OpenCog The Microsoft Cognitive Toolkit
TensorFlow Apache MXNet Apache SystemDS Caffe OpenNN PyTorch The Microsoft Cognitive Toolkit Torch Weka
Machine Learning
Massive Online Analysis TensorFlow Apache Mahout Apache Spark Apache MXNet Apache SystemDS MALLET mlpack OpenCV Orange PyTorch scikit-learn The Microsoft Cognitive Toolkit Torch Weka Yooreeka
Artificial Intelligence
Accord.NET AForge.NET TensorFlow OpenCog The Microsoft Cognitive Toolkit
Notes
- Please read the docs here completely for proper understanding of system requirements and configuration.