Overview

PyTorch

Stacks1.6K

Followers1.5K

Votes43

GitHub Stars94.7K

Forks25.8K

TensorFlow.js

Stacks184

Followers378

Votes18

GitHub Stars19.0K

Forks2.0K

PyTorch vs TensorFlow.js: What are the differences?

Introduction

PyTorch and TensorFlow.js are both popular frameworks used for developing machine learning models. While PyTorch is primarily used for deep learning tasks, TensorFlow.js is designed for running machine learning models in the browser. Despite having similar functionality, there are several key differences between the two frameworks.

1. Tensor Computation: PyTorch and TensorFlow.js have different approaches to tensor computations. PyTorch uses dynamic computational graphs, allowing for more flexibility and ease of debugging. On the other hand, TensorFlow.js uses static computational graphs, which optimize performance and facilitate deployment on different platforms.

2. Backend Language: PyTorch is primarily implemented in Python and leverages its rich ecosystem of libraries and tools for scientific computing. In contrast, TensorFlow.js uses JavaScript, enabling the execution of machine learning models directly in web browsers.

3. Model Development: PyTorch offers a more intuitive and pythonic API, making it easier for researchers and developers to experiment with new models and architectures. TensorFlow.js, on the other hand, provides a higher-level API with pre-built layers and models, making it more accessible for web developers without extensive machine learning knowledge.

4. Training Flexibility: PyTorch offers more flexibility in defining and customizing training loops. Developers have fine-grained control over every step of the training process, making it suitable for research purposes. In contrast, TensorFlow.js focuses on providing a high-level API for training and evaluation, which simplifies the process but may limit customization options.

5. Deployment: PyTorch models are typically deployed using frameworks like Flask or Django, allowing for serving models as web APIs. TensorFlow.js, being designed for web deployment, offers direct integration with web tools and libraries, allowing models to be executed client-side, reducing network latency and server load.

6. Community and Ecosystem: While both frameworks have active communities, TensorFlow.js has a larger and more mature ecosystem due to its association with TensorFlow. TensorFlow.js provides various pre-trained models and tools for transfer learning, making it easier to leverage existing models for different tasks.

In summary, PyTorch and TensorFlow.js differ in their tensor computation approach, backend language, model development experience, training flexibility, deployment options, and community support.

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Advice on PyTorch, TensorFlow.js

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

cfvedova

Oct 10, 2020

Decided

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.

72.8k views72.8k

Comments

Detailed Comparison

PyTorch	TensorFlow.js
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.	Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
Tensor computation (like numpy) with strong GPU acceleration;Deep Neural Networks built on a tape-based autograd system	-
Statistics
GitHub Stars 94.7K	GitHub Stars 19.0K
GitHub Forks 25.8K	GitHub Forks 2.0K
Stacks 1.6K	Stacks 184
Followers 1.5K	Followers 378
Votes 43	Votes 18
Pros & Cons
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	Pros 6 Open Source 5 NodeJS Powered 2 Deploy python ML model directly into javascript 1 Can run TFJS on backend, frontend, react native, + IOT 1 Privacy - no data sent to server
Integrations
Python	JavaScript TensorFlow

What are some alternatives to PyTorch, TensorFlow.js?

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.

scikit-learn

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

Keras

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

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

Gluon

A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.