PyTorch vs scikit-learn vs TensorFlow

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PyTorch vs TensorFlow vs scikit-learn: What are the differences?


Below are the key differences between PyTorch, TensorFlow, and scikit-learn.

  1. Ease of Use: PyTorch and scikit-learn are known for their simplicity and ease of use. They provide intuitive APIs and are beginner-friendly. TensorFlow, on the other hand, has a steeper learning curve and can be more complex due to its computational graph concept.

  2. Dynamic vs Static Graphs: PyTorch and scikit-learn use dynamic computational graphs, where the graph is constructed on-the-fly during execution. This allows for easier debugging and flexibility. In contrast, TensorFlow uses a static computational graph, where the graph needs to be defined and optimized before execution. This makes TensorFlow more efficient for large-scale deployments and optimizations.

  3. Community and Ecosystem: TensorFlow has a larger community and a broader ecosystem compared to PyTorch and scikit-learn. It has been around for longer and is backed by Google, which has led to extensive support, numerous libraries, and a wealth of online resources. PyTorch and scikit-learn, although growing rapidly, have a smaller community and ecosystem in comparison.

  4. Deep Learning Focus: PyTorch and TensorFlow are primarily focused on deep learning, with extensive support for neural networks. They provide a wide range of pre-built neural network architectures and optimization techniques. On the other hand, scikit-learn is a general-purpose machine learning library that covers a broader range of traditional machine learning algorithms.

  5. Hardware and Deployment Support: TensorFlow has better support for deployment on a wide range of platforms, including mobile devices (via TensorFlow Lite) and distributed systems (via TensorFlow Distributed). It also has better integration with specialized hardware like GPUs and TPUs. PyTorch and scikit-learn, while not lacking in deployment options, do not have the same level of support as TensorFlow.

  6. Data Preprocessing Capabilities: scikit-learn stands out in terms of its comprehensive data preprocessing capabilities. It provides various preprocessing techniques such as scaling, encoding, and feature selection in a user-friendly manner. While PyTorch and TensorFlow have some data preprocessing functionality, scikit-learn offers more diversity and ease of use in this domain.

In summary, PyTorch and TensorFlow are widely used deep learning frameworks with different graph computation approaches and ecosystem sizes. TensorFlow is more popular, has extensive deployment support, and is focused on deep learning. On the other hand, PyTorch is known for its simplicity and dynamic graph, while scikit-learn covers a broader range of machine learning algorithms with excellent data preprocessing capabilities.

Decisions about PyTorch, scikit-learn, and TensorFlow

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 · 49.6K 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 · 91.4K 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
Pros of TensorFlow
  • 15
    Easy to use
  • 11
    Developer Friendly
  • 10
    Easy to debug
  • 7
    Sometimes faster than TensorFlow
  • 25
    Scientific computing
  • 19
  • 32
    High Performance
  • 19
    Connect Research and Production
  • 16
    Deep Flexibility
  • 12
  • 11
    True Portability
  • 6
    Easy to use
  • 5
    High level abstraction
  • 5

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Cons of PyTorch
Cons of scikit-learn
Cons of TensorFlow
  • 3
    Lots of code
  • 1
    It eats poop
  • 2
  • 9
  • 6
    Hard to debug
  • 2
    Documentation not very helpful

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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.

What is TensorFlow?

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.

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Dec 4 2019 at 8:01PM


What are some alternatives to PyTorch, scikit-learn, and TensorFlow?
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.
It is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. It supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.
See all alternatives