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Machine Learning & Data Science · head to head

PyTorch vs RapidMiner

PyTorch logo

PyTorch

Machine Learning & Data Science

Deep learning framework with dynamic computation graphs

From
Free
Rated
-
Full reviewWebsite
R

RapidMiner

Machine Learning & Data Science

Data science platform for business teams

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: PyTorch dynamic computation graph can be less efficient for production inference than static graphs; RapidMiner rapidMiner is now a Siemens product: rapidminer.com redirects to a Siemens product page and the former Altair page redirects there too
  • They diverge on capability: PyTorch covers Dynamic computation graphs, RapidMiner covers Visual workflows.

Where they differ

Only the attributes on which PyTorch and RapidMiner actually diverge.

Attributes where PyTorch and RapidMiner differ
AttributePyTorchRapidMiner
Pricing modelUnknownfreemium
PlatformsLinux, Windows, macOSLinux, Mac, Windows, Web
Founded20162007

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in PyTorch

  • Dynamic computation graphs
  • Automatic differentiation
  • GPU acceleration
  • Distributed training
  • TorchScript
  • TorchVision
  • TorchText
  • TorchAudio

Only in RapidMiner

  • Visual workflows
  • AutoML
  • Data preparation
  • Model deployment
  • Text mining
  • Python
  • R
  • Spark

Both cover

  • Linux support
  • Mac support
  • Windows support

What people use each for

The jobs each tool is most often brought in to do.

PyTorch

  • Machine learningnot RapidMiner
  • Data analysisnot RapidMiner
  • Model trainingnot RapidMiner
  • Predictive analyticsnot RapidMiner

RapidMiner

  • Visual drag and drop machine learning model buildingnot PyTorch
  • Data preparation and cleansing before modellingnot PyTorch
  • Deploying and scoring predictive models in an enterprise settingnot PyTorch

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

PyTorch

  • Dynamic computation graph can be less efficient for production inference than static graphs
  • Requires more manual code for distributed training compared to some alternatives
  • Documentation focused heavily on research use cases rather than production deployment

RapidMiner

  • RapidMiner is now a Siemens product: rapidminer.com redirects to a Siemens product page and the former Altair page redirects there too
  • Pricing is by quote only: the product page publishes no rate, no licensing unit and no minimum, offering only a Contact us button
  • The product is now one component of a six product portfolio alongside Graph Studio, SLC, Monarch, Panopticon and Knowledge Studio

Pricing, plan by plan

PyTorch

Free

No published plan breakdown. See the PyTorch review.

RapidMiner

Free
  • FreeFree
    • 10,000 data rows
    • 1 logical processor
  • ProfessionalFree
    • Unlimited data
    • Full features
    • Support

Which should you pick?

Choose PyTorch if

  • You need dynamic computation graphs.
  • You want to start without paying.
  • You work on Linux, Windows, macOS.
  • You also want automatic differentiation.
PyTorch review

Choose RapidMiner if

  • You need visual workflows.
  • You want to start without paying.
  • You work on Linux, Mac, Windows, Web.
  • You also want automl.
RapidMiner review

Questions people ask

Is PyTorch or RapidMiner better?
Neither clearly leads. PyTorch starts at Free and RapidMiner at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, PyTorch or RapidMiner?
PyTorch starts at Free and RapidMiner at Free.
Does PyTorch or RapidMiner run on more platforms?
PyTorch runs on Linux, Windows, macOS. RapidMiner runs on Linux, Mac, Windows, Web.
Can I use PyTorch for free?
Both have a free tier, so you can try either at no cost before committing.
What is PyTorch best used for?
PyTorch is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what RapidMiner is typically brought in for.
What can PyTorch do that RapidMiner cannot?
PyTorch covers Dynamic computation graphs, Automatic differentiation, GPU acceleration, Distributed training. RapidMiner covers Visual workflows, AutoML, Data preparation, Model deployment. Both handle Linux support, Mac support, Windows support.

Answered from the vendors’ own pages

PyTorch: Is PyTorch free and open source?

Yes. PyTorch is an open source machine learning framework that is completely free to use. It was originally created and open-sourced by Facebook (now Meta) in 2016.

Source
PyTorch: What platforms does PyTorch support?

PyTorch supports Linux, Windows, and macOS. It provides strong GPU acceleration through CUDA and other backends for high-performance computing.

Source
PyTorch: Can I use PyTorch for production deployments?

Yes. PyTorch provides graph-based execution, distributed training, mobile deployment, and quantization features to support production deployments.

Source
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