Need advice about which tool to choose?Ask the StackShare community!


+ 1

+ 1
Add tool

PyTorch vs TensorFlow.js: What are the differences?


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.

Decisions about PyTorch and TensorFlow.js

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.

See more
Fabian Ulmer
Software Developer at Hestia · | 3 upvotes · 50.6K 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.

See more
Xi Huang
Developer at University of Toronto · | 8 upvotes · 92.9K 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.

See more

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.

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of PyTorch
Pros of TensorFlow.js
  • 15
    Easy to use
  • 11
    Developer Friendly
  • 10
    Easy to debug
  • 7
    Sometimes faster than TensorFlow
  • 6
    Open Source
  • 5
    NodeJS Powered
  • 2
    Deploy python ML model directly into javascript
  • 1
    Cost - no server needed for inference
  • 1
    Privacy - no data sent to server
  • 1
    Runs Client Side on device
  • 1
    Can run TFJS on backend, frontend, react native, + IOT
  • 1
    Easy to share and use - get more eyes on your research

Sign up to add or upvote prosMake informed product decisions

Cons of PyTorch
Cons of TensorFlow.js
  • 3
    Lots of code
  • 1
    It eats poop
    Be the first to leave a con

    Sign up to add or upvote consMake informed product decisions

    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 TensorFlow.js?

    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

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use PyTorch?
    What companies use TensorFlow.js?
    See which teams inside your own company are using PyTorch or TensorFlow.js.
    Sign up for StackShare EnterpriseLearn More

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with PyTorch?
    What tools integrate with TensorFlow.js?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    Blog Posts

    Dec 4 2019 at 8:01PM


    What are some alternatives to PyTorch and TensorFlow.js?
    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