Related Products
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About
GPUs bring data in and out quickly, but have little locality of reference because of their small caches. They are geared towards applying a lot of compute to little data, not little compute to a lot of data. The networks designed to run on them therefore execute full layer after full layer in order to saturate their computational pipeline (see Figure 1 below). In order to deal with large models, given their small memory size (tens of gigabytes), GPUs are grouped together and models are distributed across them, creating a complex and painful software stack, complicated by the need to deal with many levels of communication and synchronization among separate machines. CPUs, on the other hand, have large, much faster caches than GPUs, and have an abundance of memory (terabytes). A typical CPU server can have memory equivalent to tens or even hundreds of GPUs. CPUs are perfect for a brain-like ML world in which parts of an extremely large network are executed piecemeal, as needed.
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About
Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Not Supported
On-Premises
Not Supported
iPhone
Supported
iPad
Supported
Android
Supported
Chromebook
Not Supported
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Audience
Companies doing AI and ML development
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Audience
Researchers in need of an open source machine learning solution to accelerate research prototyping and production deployment
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Not Supported
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API
Offers API
Not Supported
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
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Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Training
Documentation
Supported
Webinars
Supported
Live Online
Not Supported
In Person
Not Supported
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Company InformationNeural Magic
Founded: 2018
United States
neuralmagic.com
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Company InformationPyTorch
Founded: 2016
pytorch.org
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Alternatives |
Alternatives |
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Categories |
Categories |
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Integrations
Amazon EC2 G5 Instances
Not Supported
Amazon SageMaker Unified Studio
Not Supported
Amazon Web Services (AWS)
Not Supported
DeepSpeed
Not Supported
Domino Enterprise AI Platform
Not Supported
Exafunction
Not Supported
Flyte
Not Supported
GLM-5.2
Not Supported
Gemma 3n
Not Supported
HunyuanWorld
Not Supported
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Integrations
Amazon EC2 G5 Instances
Supported
Amazon SageMaker Unified Studio
Supported
Amazon Web Services (AWS)
Supported
DeepSpeed
Supported
Domino Enterprise AI Platform
Supported
Exafunction
Supported
Flyte
Supported
GLM-5.2
Supported
Gemma 3n
Supported
HunyuanWorld
Supported
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