Related Products
|
||||||
About
AWS Deep Learning AMIs (DLAMI) provides ML practitioners and researchers with a curated and secure set of frameworks, dependencies, and tools to accelerate deep learning in the cloud. Built for Amazon Linux and Ubuntu, Amazon Machine Images (AMIs) come preconfigured with TensorFlow, PyTorch, Apache MXNet, Chainer, Microsoft Cognitive Toolkit (CNTK), Gluon, Horovod, and Keras, allowing you to quickly deploy and run these frameworks and tools at scale. Develop advanced ML models at scale to develop autonomous vehicle (AV) technology safely by validating models with millions of supported virtual tests. Accelerate the installation and configuration of AWS instances, and speed up experimentation and evaluation with up-to-date frameworks and libraries, including Hugging Face Transformers. Use advanced analytics, ML, and deep learning capabilities to identify trends and make predictions from raw, disparate health data.
|
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.
|
About
Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community. At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner.
|
||||
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
||||
Audience
Deep Learning solution that helps developers quickly build scalable, secure deep learning applications
|
Audience
Researchers in need of an open source machine learning solution to accelerate research prototyping and production deployment
|
Audience
Developers looking for a scientific computing framework for their neural networks and energy-based models
|
||||
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
||||
API
Offers API
|
API
Offers API
|
API
Offers API
|
||||
Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
||||
Pricing
No information available.
Free Version
Free Trial
|
Pricing
No information available.
Free Version
Free Trial
|
Pricing
No information available.
Free Version
Free Trial
|
||||
Reviews/
|
Reviews/
|
Reviews/
|
||||
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
||||
Company InformationAmazon
Founded: 2006
United States
aws.amazon.com/machine-learning/amis/
|
Company InformationPyTorch
Founded: 2016
pytorch.org
|
Company InformationTorch
torch.ch/
|
||||
Alternatives |
Alternatives |
Alternatives |
||||
|
|
||||||
|
|
|
|
||||
|
|
||||||
|
|
||||||
Categories |
Categories |
Categories |
||||
Integrations
AWS Elastic Fabric Adapter (EFA)
Amazon EC2 Capacity Blocks for ML
Amazon SageMaker Unified Studio
CodeQwen
Cyfuture Cloud
Daft
EasyODM
FakeYou
GPUonCLOUD
Gemma
|
Integrations
AWS Elastic Fabric Adapter (EFA)
Amazon EC2 Capacity Blocks for ML
Amazon SageMaker Unified Studio
CodeQwen
Cyfuture Cloud
Daft
EasyODM
FakeYou
GPUonCLOUD
Gemma
|
Integrations
AWS Elastic Fabric Adapter (EFA)
Amazon EC2 Capacity Blocks for ML
Amazon SageMaker Unified Studio
CodeQwen
Cyfuture Cloud
Daft
EasyODM
FakeYou
GPUonCLOUD
Gemma
|
||||
|
|
|
|