Software · head to head
PyTorch vs scikit-learn
PyTorch
Software
Deep learning framework with dynamic computation graphs
- From
- Free
- Rated
- -
The short version
- Each has a real cost: PyTorch dynamic computation graph can be less efficient for production inference than static graphs; scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays
- They diverge on capability: PyTorch covers Dynamic computation graphs, scikit-learn covers Classification algorithms.
Where they differ
Only the attributes on which PyTorch and scikit-learn actually diverge.
| Attribute | PyTorch | scikit-learn |
|---|---|---|
| Platforms | Linux, Windows, macOS | Python, Linux, macOS, Windows |
| Founded | 2016 | 2007 |
Identical on both: starting price (Free), pricing model (Unknown), free tier (Yes), user rating (Not yet rated), category (Unknown).
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 scikit-learn
- Classification algorithms
- Regression models
- Clustering methods
- Dimensionality reduction
- Model selection
- NumPy
- SciPy
- Pandas
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 learning
- Data analysis
- Model training
- Predictive analytics
scikit-learn
- Machine learning
- Data analysis
- Model training
- Predictive analytics
Both are used for machine learning, data analysis, model training, predictive analytics, on those jobs the choice comes down to price and fit rather than capability.
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
scikit-learn
- No GPU acceleration by default; limited optional GPU support requires external arrays
- Single-machine only; no built-in distributed computing across clusters
- All datasets must fit entirely in RAM; no out-of-core learning
- No production-grade deep learning; neural network support limited to basic multilayer perceptron
- No reinforcement learning algorithms
Pricing, plan by plan
PyTorch
FreeNo published plan breakdown. See the PyTorch review.
scikit-learn
FreeNo published plan breakdown. See the scikit-learn review.
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.
Choose scikit-learn if
- You need classification algorithms.
- You want to start without paying.
- You work on Python, Linux, macOS, Windows.
- You also want regression models.
Questions people ask
- Is PyTorch or scikit-learn better?
- Neither clearly leads. PyTorch starts at Free and scikit-learn at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, PyTorch or scikit-learn?
- PyTorch starts at Free and scikit-learn at Free.
- Does PyTorch or scikit-learn run on more platforms?
- PyTorch runs on Linux, Windows, macOS. scikit-learn runs on Python, Linux, macOS, Windows.
- 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.
- What can PyTorch do that scikit-learn cannot?
- PyTorch covers Dynamic computation graphs, Automatic differentiation, GPU acceleration, Distributed training. scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction. 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.
Sourcescikit-learn: Does scikit-learn support GPU acceleration?
Scikit-learn has no native GPU support by design to keep installation simple and cross-platform. Since 2023, a limited number of estimators can run on GPUs if input data is provided as PyTorch or CuPy arrays, but this requires additional setup.
SourcePyTorch: 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.
Sourcescikit-learn: Can scikit-learn handle datasets larger than RAM?
No. Scikit-learn is built on NumPy which requires all data to fit in memory, and NumPy operates on single-machine CPUs only. For very large datasets, consider Spark MLlib or distributed alternatives.
SourcePyTorch: Can I use PyTorch for production deployments?
Yes. PyTorch provides graph-based execution, distributed training, mobile deployment, and quantization features to support production deployments.
Sourcescikit-learn: Is scikit-learn free to use commercially?
Yes. Scikit-learn is open source under the BSD license, which allows free commercial use, modification, and distribution.
Sourcescikit-learn: What neural network capabilities does scikit-learn have?
Scikit-learn includes only a basic multilayer perceptron (MLPClassifier and MLPRegressor) for simple feedforward networks. For serious deep learning, use PyTorch, TensorFlow, or Keras instead.
Sourcescikit-learn: Does scikit-learn include natural language processing?
Scikit-learn has minimal NLP support limited to basic text feature extraction and vectorization. For comprehensive text processing, use spaCy or NLTK instead.
Sourcescikit-learn: When was scikit-learn first released?
Scikit-learn's first public release was February 1, 2010, following its start as a Google Summer of Code project in 2007.
SourceRelated pages
More on scikit-learn
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