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    Deep Learning Base GPU AMI (Ubuntu 20.04)

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    Deployed on AWS
    AWS Free Tier
    This is a repackaged open source software product wherein additional charges apply for support provided by Galaxys, includes AWS Deep Learning Base AMI Ubuntu 18.04 32.0 and includes support.

    Overview

    This is a repackaged open source software product wherein additional charges apply for support provided by Galaxys, includes AWS Deep Learning Base AMI Ubuntu 18.04 32.0 and includes support.

    AMI provides a foundational platform for deep learning on AWS EC2 with NVIDIA CUDA, cuDNN, NCCL, GPU Drivers, Intel MKL-DNN, Docker, NVIDIA-Docker, EFA, and AWS Neuron support. This AMI is suitable for deploying your own custom deep learning environment at scale. For example, for machine learning developers contributing to open source deep learning framework enhancements, the AWS Deep Learning Base AMI provides a foundation for installing your custom configurations and forked repositories to test out new framework features.

    Below are the core components of AWS Deep Learning Base AMI:

    AWS Deep Learning Tools including AWS Elastic Fabric Adapter(EFA) and AWS Neuron. NVIDIA Deep Learning Softwares Including NVIDIA GPU Driver, CUDA Toolkit, cuDNN, NCCL, and Fabric Manager. Containerization platforms including Docker, and NVIDIA-Docker for build and run GPU accelerated Docker containers. Intel Architecture performance library Intel MKL-DNN. A collection of popular tools such as awscli, boto3, numpy, scikit-learn, opencv, pandas, matplotlib, graphviz,

    Highlights

    • AWS Deep Learning Base AMI ships multiple CUDA Toolkits and can be easily switched. https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-base.html

    Details

    Delivery method

    Delivery option
    64-bit (x86) Amazon Machine Image (AMI)

    Latest version

    Operating system
    Ubuntu Linux/Unix, 20.04

    Deployed on AWS

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    Pricing

    Deep Learning Base GPU AMI (Ubuntu 20.04)

     Info
    Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    Usage costs (546)

     Info
    • ...
    Dimension
    Cost/hour
    c4.xlarge
    Recommended
    $0.20
    t3.micro
    AWS Free Tier
    $0.05
    m6idn.metal
    $2.40
    c6i.8xlarge
    $1.60
    m5d.2xlarge
    $0.40
    r7iz.8xlarge
    $1.60
    r6idn.12xlarge
    $2.40
    c6i.large
    $0.10
    trn1n.32xlarge
    $3.20
    r7iz.large
    $0.10

    Vendor refund policy

    For this offering, Galaxys Cloud does not offer refund, you may cancel at anytime.

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    Legal

    Vendor terms and conditions

    Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .

    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

    64-bit (x86) Amazon Machine Image (AMI)

    Amazon Machine Image (AMI)

    An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.

    Version release notes

    v2023

    Additional details

    Usage instructions

    1.Start the instance 2.Use "ubuntu" as the username to SSH into the instance. 4.Remember to save the generated keypair when raising the instance for the first time 5.- to connect via ssh you can do it with the following command using putty or command terminal: ssh -i /path/your-key-pair-name.pem ubuntu@instance-public-dns-name

    Our software does not store confidential customer information. Our software does not encrypt the data present in it. Our software does not use rotating programmatic system credentials or cryptographic keys.

    For more information: Getting-started guide for AWS Deep Learning AMI: http://docs.aws.amazon.com/dlami/latest/devguide/gs.html 

    Support

    Vendor support

    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.

    Product comparison

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    Updated weekly

    Accolades

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    Top
    10
    In ML Solutions, High Performance Computing
    Top
    50
    In High Performance Computing

    Overview

     Info
    AI generated from product descriptions
    Machine Learning Framework Support
    Provides foundational platform for deep learning with support for multiple open-source deep learning frameworks
    GPU Acceleration Infrastructure
    Includes NVIDIA CUDA, cuDNN, NCCL, and GPU drivers for high-performance GPU computing
    Containerization Support
    Integrates Docker and NVIDIA-Docker for building and running GPU-accelerated container environments
    Performance Libraries
    Incorporates Intel MKL-DNN for optimized machine learning and deep learning computations
    AWS Specialized Tools
    Includes AWS Elastic Fabric Adapter (EFA) and AWS Neuron for enhanced machine learning infrastructure
    Machine Learning Framework Support
    Provides foundational platform for deep learning with support for multiple machine learning frameworks and tools
    GPU Acceleration Infrastructure
    Includes NVIDIA CUDA, cuDNN, NCCL, and GPU drivers for high-performance GPU computing
    Containerization Support
    Integrates Docker and NVIDIA-Docker for building and running GPU-accelerated containers
    Performance Libraries
    Includes Intel MKL-DNN for optimized machine learning and deep learning performance
    Advanced Networking Capabilities
    Supports AWS Elastic Fabric Adapter (EFA) and AWS Neuron for enhanced computational networking
    GPU Acceleration
    Includes NVIDIA drivers, CUDA, and cuDNN for high-performance GPU-based deep learning computations
    Deep Learning Framework
    Pre-installed PyTorch 2.0.1 with support for advanced machine learning model development
    Operating System
    Built on Amazon Linux 2, providing a lightweight and secure cloud-optimized environment
    Machine Learning Libraries
    Pre-configured with essential libraries including TorchVision, NumPy, and PyTorch-related packages
    Cloud Instance Compatibility
    Supports multiple GPU-based EC2 instances for scalable AI training and inference tasks

    Contract

     Info
    Standard contract

    Customer reviews

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