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    AWS Deep Learning Containers for PyTorch

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    Sold by: Amazon Web Services 
    AWS Deep Learning Containers for PyTorch include Docker images for training and inference on CPU/GPU, optimized for performance and scale across Amazon SageMaker, ECS, EKS, and Kubernetes. Includes stable versions of Nvidia CUDA, cuDNN, Intel MKL and Horovod.
    5
    1 AWS reviews
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    22 external reviews
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    AWS Deep Learning Containers for PyTorch

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    Sold by: Amazon Web Services 
    View purchase options

    Overview

    The AWS Deep Learning Containers for PyTorch include containers for training and inference for CPU and GPU, optimized for performance and scale on AWS. These Docker images have been tested with SageMaker, EC2, ECS, and EKS and provide stable versions of NVIDIA CUDA, cuDNN, Intel MKL, Horovod and other required software components to provide a seamless user experience for deep learning workloads. All software components in these images are scanned for security vulnerabilities and updated or patched in accordance with AWS Security best practices.

    Highlights

    • Optimized and stable Docker images for training and inference with PyTorch
    • Built for use with Amazon SageMaker, Amazon EKS, Amazon ECS and Amazon EC2
    • Get started with AWS Deep Learning Containers https://docs.aws.amazon.com/dlami/latest/devguide/deep-learning-containers.html

    Details

    Sold by
    Amazon Web Services 
    Categories
    Application Development 
    Machine Learning 
    Education & Research 
    Delivery method
    Delivery option
    PyTorch Training
    Latest version
    Operating system
    Linux

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    AWS Deep Learning Containers for PyTorch

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    This product is free. Subscriptions have no end date and can be canceled anytime.

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    Content disclaimer

    Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.

    Usage information

     Info

    Delivery details

    PyTorch Training

    Supported services: Learn more 
    • Amazon EKS
    • Amazon ECS
    Container image

    Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.

    Version release notes

    Container

    Additional details

    Usage instructions

    https://docs.aws.amazon.com/dlami/latest/devguide/deep-learning-containers.html 

    Support

    Vendor support

    AWS Deep Learning Containers for PyTorch

    Support is available through AWS Premium Support, AWS forums, technical FAQs, and the Service Help Dashboard. Post your questions to the AWS Deep Learning Containers Discussion Forum

    https://forums.aws.amazon.com/forum.jspa?forumID=341 

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    AWS infrastructure support

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

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    Ratings and reviews

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    5
    1 ratings
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    1 AWS reviews
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    22 external reviews
    External reviews are sourced from G2  and are not included in the star rating for this product.
    shixuesong

    很好

    Reviewed on Jun 26, 2023
    Purchase verified by AWS

    The AWS Deep Learning Containers for PyTorch include containers for training and inference for CPU and GPU, optimized for performance and scale on AWS. These Docker images have been tested with SageMaker, EC2, ECS, and EKS and provide stable versions of NVIDIA CUDA, cuDNN, Intel MKL
    okkokkokkokkokkokkokk

    Leave a comment0 comments
    Hisham K.

    The usage is quite logical and output is reasonable

    Reviewed on Oct 11, 2022
    Review provided by G2
    What do you like best about the product?
    In deep learning most time constraints are there so saving time with investing much time is good approach
    What do you dislike about the product?
    As long as hanging in loop need to relook
    What problems is the product solving and how is that benefiting you?
    Mostly NLP
    Leave a comment0 comments
    Mohammad Yahya A.

    Working with AWS DLC significantly accelerates the ML deployment.

    Reviewed on Oct 10, 2022
    Review provided by G2
    What do you like best about the product?
    Frequently updating trained images for different frameworks reduced ML time to production.
    What do you dislike about the product?
    Customizing an AWS DLC still takes time to rebuild. The UI need to be improved.
    What problems is the product solving and how is that benefiting you?
    The ML Container idea solved many issues in ML deployment:
    1) ML model portability
    2) ML deployment speed
    3) reduced ML production time
    Leave a comment0 comments
    Shubhangi M.

    Best for easily deploying coustom ml environments

    Reviewed on Oct 06, 2022
    Review provided by G2
    What do you like best about the product?
    It is very easy to just skip through the difficult process of building and deploying the environment scratch and we can let easily deploy ML environments. It is better from others as we can just use it to have pre installed docker images.
    What do you dislike about the product?
    Nothing it is which is not likeable about the AWS deep learning container.
    What problems is the product solving and how is that benefiting you?
    It lets us skip the complicated part of developing and deploying the environment from scratch each time the requirements come up, we can use the pre installed docker images, create templates and containers , in just few click we are ready to launch a new ML environment when need comes up.
    Leave a comment0 comments
    Yogita K.

    AWS Deep Learning Containers Review

    Reviewed on Sep 15, 2022
    Review provided by G2
    What do you like best about the product?
    This AWS Deep learning containers structure is very easy to learn. Smooth proficiency with deep knowledge. Not much of the configuration is required to go live on the go. Images can be easily used. The machine learning operation made easy with AWS deep learning containers.
    What do you dislike about the product?
    There is the only problem that i think needs to be corrected is the troubleshooting problem of error. Otherwise, all is good and smooth with these AWS deep learning containers.
    What problems is the product solving and how is that benefiting you?
    The manual Image creation was a problem that is being solved because of this AWS deep learning we can do it in more smooth and fast manner.
    Leave a comment0 comments
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