PyTorch聽vs聽scikit-learn聽vs聽TensorFlow

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PyTorch
PyTorch

422
452
+ 1
15
scikit-learn
scikit-learn

536
488
+ 1
25
TensorFlow
TensorFlow

1.6K
1.6K
+ 1
70

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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Why do developers choose PyTorch?
Why do developers choose scikit-learn?
Why do developers choose TensorFlow?

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What companies use PyTorch?
What companies use scikit-learn?
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What tools integrate with PyTorch?
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What are some alternatives to PyTorch, scikit-learn, and TensorFlow?
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
Caffe2
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.
MXNet
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.
Torch
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
Chainer
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.
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Decisions about PyTorch, scikit-learn, and TensorFlow
Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 6 upvotes 640.3K views
atUber TechnologiesUber Technologies
TensorFlow
TensorFlow
Keras
Keras
PyTorch
PyTorch

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

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Reviews of PyTorch, scikit-learn, and TensorFlow
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How developers use PyTorch, scikit-learn, and TensorFlow
Avatar of Yonas B.
Yonas B. uses PyTorchPyTorch

I used PyTorch when i was working on an AI application, image classification using deep learning.

Avatar of Eliana Abraham
Eliana Abraham uses TensorFlowTensorFlow

Machine Learning in EECS 445

Avatar of Eliana Abraham
Eliana Abraham uses scikit-learnscikit-learn

Machine Learning in EECS 445

Avatar of Taylor Host
Taylor Host uses TensorFlowTensorFlow

Pilot integration for retraining.

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