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What is PyTorch?

Seamlessly transition between eager and graph modes with TorchScript, while expediting your production journey using TorchServe. The torch-distributed backend supports scalable distributed training, boosting performance optimization in both research and production contexts. A diverse array of tools and libraries enhances the PyTorch ecosystem, facilitating development across various domains, including computer vision and natural language processing. Furthermore, PyTorch's compatibility with major cloud platforms streamlines the development workflow and allows for effortless scaling. Users can easily select their preferences and run the installation command with minimal hassle. The stable version represents the latest thoroughly tested and approved iteration of PyTorch, generally suitable for a wide audience. For those desiring the latest features, a preview is available, showcasing the newest nightly builds of version 1.10, though these may lack full testing and support. It's important to ensure that all prerequisites are met, including having numpy installed, depending on your chosen package manager. Anaconda is strongly suggested as the preferred package manager, as it proficiently installs all required dependencies, guaranteeing a seamless installation experience for users. This all-encompassing strategy not only boosts productivity but also lays a solid groundwork for development, ultimately leading to more successful projects. Additionally, leveraging community support and documentation can further enhance your experience with PyTorch.

What is ONNX?

ONNX offers a standardized set of operators that form the essential components for both machine learning and deep learning models, complemented by a cohesive file format that enables AI developers to deploy models across multiple frameworks, tools, runtimes, and compilers. This allows you to build your models in any framework you prefer, without worrying about the future implications for inference. With ONNX, you can effortlessly connect your selected inference engine with your favorite framework, providing a seamless integration experience. Furthermore, ONNX makes it easier to utilize hardware optimizations for improved performance, ensuring that you can maximize efficiency through ONNX-compatible runtimes and libraries across different hardware systems. The active community surrounding ONNX thrives under an open governance structure that encourages transparency and inclusiveness, welcoming contributions from all members. Being part of this community not only fosters personal growth but also enriches the shared knowledge and resources that benefit every participant. By collaborating within this network, you can help drive innovation and collectively advance the field of AI.

Media

Media

Integrations Supported

Cirrascale
Flyte
Groq
Intel Open Edge Platform
OpenVINO
Qualcomm Cloud AI SDK
SiMa
AI Squared
Akira AI
Amazon EC2 Capacity Blocks for ML
GPUEater
NVIDIA FLARE
NVIDIA Triton Inference Server
Qualcomm AI Hub
Skyportal
SuperDuperDB
Unify AI
Vertex AI Notebooks
neptune.ai

Integrations Supported

Cirrascale
Flyte
Groq
Intel Open Edge Platform
OpenVINO
Qualcomm Cloud AI SDK
SiMa
AI Squared
Akira AI
Amazon EC2 Capacity Blocks for ML
GPUEater
NVIDIA FLARE
NVIDIA Triton Inference Server
Qualcomm AI Hub
Skyportal
SuperDuperDB
Unify AI
Vertex AI Notebooks
neptune.ai

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

PyTorch

Date Founded

2016

Company Website

pytorch.org

Company Facts

Organization Name

ONNX

Company Website

onnx.ai/

Categories and Features

Machine Learning

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

Categories and Features

Machine Learning

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

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