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.

Integrations

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Reviews - 1 Verified Review

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Company Details

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

Media

PyTorch Screenshot 1
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Product Details

Platforms
Windows
Mac
Linux
iPhone
iPad
Android
Type of Training
Documentation
Webinars
Videos
Customer Support
Online

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

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  • 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...