Deep LearningDeep Learning

This extension provides Deep Learning capabilities for execution on CPU and GPU.

This extension provides operators to create Deep Learning models using different types of layers. Networks can be executed both on CPU and on GPU.

Version 0.9.0:

  • Added Load Keras Model Operator (applying sequential Keras models without python)

  • Added Recurrent Network (like LSTM) handling

  • Added LSTM Layer

  • Added Time-Series to Tensor Operator

  • Fix of Anaconda blocking extension loading

  • Removed Text to Numbers via Word2Vec

  • Changed tensor handling (incompatible with previous tensors)

  • Lowered CUDA version requirement from 9.1 to 9.0.

Version 0.8.1:

  • fixed bug causing incompatibility with RapidMiner Studio 9.1

Version 0.8.0:

  • Deep Learning on ExampleSets with native model handling
  • Text Handling using Word2Vec
  • Layers:
    • fully-connected
    • dropout
    • batch normalization
    • convolutional
    • pooling
    • global pooling
  • GPU usage
  • History Port (epoch logging)
  • Custom Icons
  • No external requirements (except for GPU)
  • QuickFixes for switching between regression & classification (loss functions)
  • Model Updatability
  • Samples Processes (samples/Deep Learning)


Execution on GPU is currently only available on NVIDIA GPUs in combination with CUDA version 9.0.

This extension uses the java library DeepLearning4J (version 1.0-beta).

No support for 32-bit.

Product Details

Version 0.9.1
File size 795 MB
Downloads 14991 (11 Today)14991 downloads
Vendor RapidMiner Labs
Category Machine Learning
Released 6/19/19
Last Update 6/19/19 1:09 PM
License AGPL
Product web site
Rating 0.0 stars(0)


Extension version 0.9: (RM9.1) only supports CUDA 9.0 and CUDNN 7.0 EXACTLY. Ensure that CUDA bin directory is in your path before starting RM. Extension version 0.8.0: (RM9.0) only supports CUDA 9.1 and CUDNN 7.1 EXACTLY

wongcr, 12/21/18 8:12 AM

The data parsing problem occurs when a 32-bit Version of RapidMiner Studio or Java is used. Please check the 64-bit version.

Rapid-Labs, 8/8/18 2:24 PM

I only get a "data parsing problem" error even though with all numerical example sets. Is there a fix for this? Thanks...

Gottfried, 8/8/18 12:00 PM
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