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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 Fabric for Deep Learning (FfDL)?

Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have greatly improved the ease with which deep learning models can be designed, trained, and utilized. Fabric for Deep Learning (FfDL, pronounced "fiddle") provides a unified approach for deploying these deep-learning frameworks as a service on Kubernetes, facilitating seamless functionality. The FfDL architecture is constructed using microservices, which reduces the reliance between components, enhances simplicity, and ensures that each component operates in a stateless manner. This architectural choice is advantageous as it allows failures to be contained and promotes independent development, testing, deployment, scaling, and updating of each service. By leveraging Kubernetes' capabilities, FfDL creates an environment that is highly scalable, resilient, and capable of withstanding faults during deep learning operations. Furthermore, the platform includes a robust distribution and orchestration layer that enables efficient processing of extensive datasets across several compute nodes within a reasonable time frame. Consequently, this thorough strategy guarantees that deep learning initiatives can be carried out with both effectiveness and dependability, paving the way for innovative advancements in the field.

Media

Media

Integrations Supported

Akira AI
Amazon EC2 G5 Instances
Amazon EC2 Inf1 Instances
Amazon Elastic Inference
Amazon SageMaker Model Training
Amazon Web Services (AWS)
Cameralyze
DagsHub
Deep Lake
EdgeCortix
Exafunction
Gemma
Google Cloud Platform
Gradient
IREN Cloud
MLReef
NVIDIA TensorRT
Skyportal
voyage-3-large

Integrations Supported

Akira AI
Amazon EC2 G5 Instances
Amazon EC2 Inf1 Instances
Amazon Elastic Inference
Amazon SageMaker Model Training
Amazon Web Services (AWS)
Cameralyze
DagsHub
Deep Lake
EdgeCortix
Exafunction
Gemma
Google Cloud Platform
Gradient
IREN Cloud
MLReef
NVIDIA TensorRT
Skyportal
voyage-3-large

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

IBM

Date Founded

1911

Company Location

United States

Company Website

developer.ibm.com/open/projects/fabric-for-deep-learning-ffdl/

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

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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