PyTorch Description

TorchScript allows you to seamlessly switch between graph and eager modes. TorchServe accelerates the path to production. The torch-distributed backend allows for distributed training and performance optimization in production and research. PyTorch is supported by a rich ecosystem of libraries and tools that supports NLP, computer vision, and other areas. PyTorch is well-supported on major cloud platforms, allowing for frictionless development and easy scaling. Select your preferences, then run the install command. Stable is the most current supported and tested version of PyTorch. This version should be compatible with many users. Preview is available for those who want the latest, but not fully tested, and supported 1.10 builds that are generated every night. Please ensure you have met the prerequisites, such as numpy, depending on which package manager you use. Anaconda is our preferred package manager, as it installs all dependencies.

PyTorch Alternatives
NLTK Reviews
NLTK
The Natural Language Toolkit is a comprehensive open-source Python library for processing human language data. It provides user-friendly interfaces for over 50 corpora, lexical resources and text processing libraries, including WordNet. NLTK provides wrappers for industrial strength NLP libraries, and maintains a lively discussion forum. NLTK comes with a comprehensive API documentation and a hands-on tutorial that teaches programming fundamentals along with computational linguistics. It is suitable for linguists and engineers, as well as students, educators, researchers and industry professionals. It is compatible with Windows Mac OS X and Linux platforms. NLTK is a community-driven, free project.
Learn more
spaCy Reviews
spaCy
spaCy is designed for real work, real products and real insights. The library respects your time, and tries not to waste it. It is easy to install and the API is simple and efficient. spaCy excels in large-scale information extraction tasks. It is written in Cython, which is carefully managed for memory. SpaCy is the library to use if your application requires to process large web dumps. spaCy was released in 2015 and has been a industry standard with a large ecosystem. You can choose from a wide range of plugins and integrate them with your machine-learning stack to create custom components and workflows. You can use these components to recognize named entities, part-of speech tagging, dependency parsing and sentence segmentation. Easy extensible with custom components or attributes Model packaging, deployment, workflow management made easy.
Learn more
ONNX Reviews
ONNX
ONNX defines a set of common operators - the building block of machine learning and deeper learning models – and a standard file format that allows AI developers to use their models with a wide range of frameworks, runtimes and compilers. You can use your preferred framework to develop without worrying about downstream implications. ONNX allows you to use the framework of your choice with your inference engine. ONNX simplifies the access to hardware optimizations. Use runtimes and libraries compatible with ONNX to optimize performance across hardware. Our community thrives in our open governance structure that provides transparency and inclusion. We encourage you to participate and contribute.
Learn more
TensorFlow Reviews
TensorFlow
(2 Ratings)
Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.
Learn more

Integrations

View Integrations

Reviews - 1 Verified Review

Total
ease
features
design
support
See More Reviews Write a Review

Company Details

Company:
PyTorch
Year Founded:
2016
Website:
pytorch.org

Media

PyTorch Screenshot 1
PyTorch Screenshot 1 PyTorch Screenshot 2 PyTorch Screenshot 3
Recommended Products
Passwordless Authentication and Passwordless Security Icon
Passwordless Authentication and Passwordless Security

Identity is everything. Protect it with Duo.

It’s no secret — passwords can be a real headache, both for the people who use them and the people who manage them. Over time, we’ve created hundreds of passwords, it’s easy to lose track of them and they’re easily compromised. Fortunately, passwordless authentication is becoming a feasible reality for many businesses. Duo can help you get there.
Get a Free Trial

Product Details

Platforms
Windows
Mac
Linux
iPhone App
iPad App
Android App
Types of Training
Training Docs
Webinars
Training Videos
Customer Support
Online Support

PyTorch Features and Options

Machine Learning Software

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

PyTorch User Reviews

Write a Review
  • Name: Anonymous (Verified)
    Job Title: Principal Software Engineer
    Length of product use: Less than 6 months
    Used How Often?: Yearly
    Role: User
    Organization Size: 100 - 499
    Features
    Design
    Ease
    Pricing
    Support
    Likelihood to Recommend to Others
    1 2 3 4 5 6 7 8 9 10

    Great open source machine learning framework

    Date: Aug 03 2022

    Summary: PyTorch is a great machine learning framework that is both flexible and fast. It's highly customizable and free, but very complicated to learn.

    Positive: - creates dynamic neural networks in Python
    - GPU acceleration compatible
    - easy transition between eager and graph modes
    - scalable across distributed computing networks
    - excellent documentation and community
    - very flexible and fast machine learning
    - free and open source

    Negative: - very high learning curve
    - requires significant power to run any sort of computation

    Read More...
  • Previous
  • You're on page 1
  • Next