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PyTorch vs scikit-learn: What are the differences?

Introduction: PyTorch and scikit-learn are two popular libraries used for machine learning tasks in python. While both libraries offer functionality for building and training machine learning models, there are several key differences between PyTorch and scikit-learn.

  1. Backend and Optimization: PyTorch is a deep learning library that uses dynamic computation graphs, which makes it more suitable for neural network models. It provides automatic differentiation and supports GPU acceleration, making it efficient for large-scale deep learning tasks. On the other hand, scikit-learn is a general-purpose machine learning library that uses static computation graphs and focuses on traditional machine learning algorithms. It is optimized for these algorithms and provides a wide range of pre-implemented models and tools.

  2. Model Flexibility: PyTorch offers a high level of model flexibility, allowing users to build and customize complex models easily. It provides a dynamic execution model, making it straightforward to implement custom architectures, control flow, and incorporate external libraries. In contrast, scikit-learn focuses on simplicity and provides a fixed set of predefined models. While scikit-learn allows for limited customization, it is not as flexible as PyTorch when it comes to model design.

  3. Deep Learning Support: PyTorch is widely used for deep learning tasks, including image and speech recognition, natural language processing, and reinforcement learning. It offers a rich set of tools, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, specifically designed for these tasks. Scikit-learn, on the other hand, does not have native support for deep learning models and is primarily used for traditional machine learning algorithms like linear regression, random forests, and support vector machines.

  4. Ease of Use: Scikit-learn provides a user-friendly interface and is known for its simplicity and ease of use. It has a consistent API and follows a similar syntax across different algorithms, making it accessible for beginners. PyTorch, on the other hand, has a steeper learning curve and requires a deeper understanding of neural networks and deep learning concepts. It is more suited for users with a background in machine learning and deep learning.

  5. Community and Ecosystem: PyTorch has gained popularity in recent years and has a vibrant community of developers and researchers. It is backed by Facebook's research team and has a wide range of resources, tutorials, and pre-trained models available. Scikit-learn, on the other hand, has been around for a longer time and has an extensive community and ecosystem built around it. It has a rich collection of tutorials, examples, and documentation, making it easy to find support and learn.

  6. Deployment and Productionization: PyTorch provides tools and frameworks like TorchServe and ONNX to help with model deployment and productionization. It has native support for exporting models to production frameworks like TensorFlow and Caffe2. Scikit-learn, on the other hand, does not have built-in tools for deployment, but its models can be easily serialized and deployed using frameworks like Flask or Django.

In summary, PyTorch is a deep learning library with dynamic computation graphs and extensive support for neural networks, while scikit-learn is a general-purpose machine learning library with a focus on simplicity and traditional machine learning algorithms. PyTorch offers more model flexibility and is widely used for deep learning tasks, but it has a steeper learning curve compared to scikit-learn. Both libraries have active communities and resources available.

Decisions about PyTorch and scikit-learn

Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.

I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.

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Fabian Ulmer
Software Developer at Hestia · | 3 upvotes · 50.5K views

For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.

We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.

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Xi Huang
Developer at University of Toronto · | 8 upvotes · 92.7K views

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

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A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

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Pros of PyTorch
Pros of scikit-learn
  • 15
    Easy to use
  • 11
    Developer Friendly
  • 10
    Easy to debug
  • 7
    Sometimes faster than TensorFlow
  • 25
    Scientific computing
  • 19

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Cons of PyTorch
Cons of scikit-learn
  • 3
    Lots of code
  • 1
    It eats poop
  • 2

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What is PyTorch?

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

What is scikit-learn?

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

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Dec 4 2019 at 8:01PM


What are some alternatives to PyTorch and scikit-learn?
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano.
Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.
A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
See all alternatives