Overview

scikit-learn

Stacks1.3K

Followers1.1K

Votes45

GitHub Stars63.9K

Forks26.4K

TensorFlow

Stacks3.8K

Followers3.5K

Votes106

GitHub Stars192.3K

Forks74.9K

PyTorch

Stacks1.6K

Followers1.5K

Votes43

GitHub Stars94.7K

Forks25.8K

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

Introduction

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

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.
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.
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.
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.
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.
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.

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Advice on scikit-learn, TensorFlow, PyTorch

Developer at DCSIL

Oct 11, 2020

Decided

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.

99.3k views99.3k

Comments

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

107k views107k

Comments

philippe

Research & Technology & Innovation | Software & Data & Cloud | Professor in Computer Science

Sep 13, 2020

Review

Hello Amina, You need first to clearly identify the input data type (e.g. temporal data or not? seasonality or not?) and the analysis type (e.g., time series?, categories?, etc.). If you can answer these questions, that would be easier to help you identify the right tools (or Python libraries). If time series and Python, you have choice between Pendas/Statsmodels/Serima(x) (if seasonality) or deep learning techniques with Keras.

Good work, Philippe

4.63k views4.63k

Comments

Detailed Comparison

scikit-learn	TensorFlow	PyTorch
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.	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.	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.
-	-	Tensor computation (like numpy) with strong GPU acceleration;Deep Neural Networks built on a tape-based autograd system
Statistics
GitHub Stars 63.9K	GitHub Stars 192.3K	GitHub Stars 94.7K
GitHub Forks 26.4K	GitHub Forks 74.9K	GitHub Forks 25.8K
Stacks 1.3K	Stacks 3.8K	Stacks 1.6K
Followers 1.1K	Followers 3.5K	Followers 1.5K
Votes 45	Votes 106	Votes 43
Pros & Cons
Pros 26 Scientific computing 19 Easy Cons 2 Limited	Pros 32 High Performance 19 Connect Research and Production 16 Deep Flexibility 12 Auto-Differentiation 11 True Portability Cons 9 Hard 6 Hard to debug 2 Documentation not very helpful	Pros 15 Easy to use 11 Developer Friendly 10 Easy to debug 7 Sometimes faster than TensorFlow Cons 3 Lots of code 1 It eats poop
Integrations
No integrations available	JavaScript	Python

What are some alternatives to scikit-learn, TensorFlow, PyTorch?

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

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TensorFlow.js

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Polyaxon

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Streamlit

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MLflow

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H2O

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PredictionIO

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Gluon

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Comet.ml

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