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    NVIDIA GPU-Optimized AMI

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    Sold by: NVIDIA 
    The NVIDIA GPU-Optimized AMI is an environment for running the GPU-accelerated deep learning and HPC containers from the NVIDIA NGC catalog. The deep learning containers from NGC catalog require this AMI for GPU acceleration on AWS P4d, P3, G4dn, G5 GPU instances.
    Listing Thumbnail

    NVIDIA GPU-Optimized AMI

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    Sold by: NVIDIA 

    Overview

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    The NVIDIA GPU-Optimized AMI is a virtual machine image for accelerating your GPU accelerated Machine Learning, Deep Learning, Data Science and HPC workloads. Using this AMI, you can spin up a GPU-accelerated EC2 VM instance in minutes with a pre-installed Ubuntu OS, GPU driver, Docker and NVIDIA container toolkit.

    This AMI provides easy access to NVIDIA's NGC Catalog, a hub for GPU-optimized software, for pulling & and running performance-tuned, tested, and NVIDIA certified docker containers. The NGC catalog provides free access to containerized AI, Data Science, and HPC applications, pre-trained models, AI SDKs and other resources to enable data scientists, developers, and researchers to focus on building and deploying solutions.

    This GPU-optimized AMI is free with an option to purchase enterprise support offered through NVIDIA AI Enterprise. For how to get support for this AMI, scroll down to 'Support Information'

    NVIDIA GPU-Optimized AMI includes:

    • Ubuntu Server OS
    • NVIDIA Driver
    • Docker-ce
    • NVIDIA Container Toolkit
    • AWS CLI, NGC CLI
    • Miniconda, JupyterLab, Git

    Highlights

    • Provides data scientists and developers fast and easy access to NVIDIA A100, A10, V100 and T4 GPUs in the cloud and GPU-optimized AI/HPC software in an environment that is fully certified by NVIDIA.
    • Optimized for highest performance across a wide range of workloads on NVIDIA GPUs
    • NVIDIA accelerates innovation by eliminating the complex do-it-yourself task of building and optimizing a complete deep learning software stack tuned specifically for GPUs.

    Details

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    Delivery method

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

    Latest version

    Operating system
    Ubuntu 22.04

    Typical total price

    This estimate is based on use of the seller's recommended configuration (p3.2xlarge) in the US East (N. Virginia) Region. View pricing details

    $3.06/hour

    Pricing

    NVIDIA GPU-Optimized AMI

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    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.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    Usage costs (20)

     Info
    Instance type
    Product cost/hour
    EC2 cost/hour
    Total/hour
    p3.2xlarge
    Recommended
    $0.00
    $3.06
    $3.06
    p3.8xlarge
    $0.00
    $12.24
    $12.24
    p3.16xlarge
    $0.00
    $24.48
    $24.48
    p3dn.24xlarge
    $0.00
    $31.212
    $31.212
    p4d.24xlarge
    $0.00
    $32.773
    $32.773
    g4dn.xlarge
    $0.00
    $0.526
    $0.526
    g4dn.2xlarge
    $0.00
    $0.752
    $0.752
    g4dn.4xlarge
    $0.00
    $1.204
    $1.204
    g4dn.8xlarge
    $0.00
    $2.176
    $2.176
    g4dn.12xlarge
    $0.00
    $3.912
    $3.912

    Additional AWS infrastructure costs

    Type
    Cost
    EBS General Purpose SSD (gp3) volumes
    $0.08/per GB/month of provisioned storage

    Vendor refund policy

    This AMI is provided free of charge.

    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

    Ubuntu Server: 22.04 (x86) NVIDIA TRD Driver: 550.54.15 Docker-ce: 26.1.2 NVIDIA Container Toolkit: 1.15.0 Latest AWS CLI Miniconda: JupyterLab latest and other Jupyter core packages NGC-CLI: 3.41.4 Git, Python3-PIP

    Additional details

    Usage instructions

    For usage instructions and quick start guide, please refer: https://docs.nvidia.com/ngc/ngc-deploy-public-cloud/ngc-aws/index.html 

    Support

    Vendor support

    This AMI comes with an Enterprise Support option https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/ 

    To use miniconda, follow the steps below ubuntu@ip-172-31-38-41:$ conda --version conda 24.3.0 ubuntu@ip-172-31-38-41:$ conda activate base (base) ubuntu@ip-172-31-38-41:~$ jupyter --version Selected Jupyter core packages... IPython : 8.22.2 ipykernel : 6.29.3 ipywidgets : not installed jupyter_client : 8.6.1 jupyter_core : 5.7.2 jupyter_server : 2.13.0 jupyterlab : 4.1.5 nbclient : 0.10.0 nbconvert : 7.16.3 nbformat : 5.10.3 notebook : not installed qtconsole : not installed traitlets : 5.14.2

    For more information please check https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/ 

    Free support for AWS images is available through forums, technical documentation & FAQs https://devtalk.nvidia.com/default/board/200/nvidia-gpu-cloud-ngc-users/  NVIDIA Developer Forum

    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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    Customer reviews

    Ratings and reviews

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    2.8
    9 ratings
    5 star
    4 star
    3 star
    2 star
    1 star
    33%
    0%
    22%
    0%
    44%
    9 AWS reviews
    CS

    Install Drivers

    Reviewed on Jun 05, 2024
    Purchase verified by AWS

    You need to first change the username from root to ubuntu in order to have the drivers be installed! I feel this should have been more specified in the directions!

    yikesawjeez

    AMI is not configured as advertised.

    Reviewed on Mar 15, 2024
    Purchase verified by AWS

    None of the advertised utilities are installed in the AMI, neither is CUDA. This is current as of 3/14/24. It appears to be a raw installation of 22.04, by my estimation.

    root@ip-172-31-38-109:/cuda-samples/Samples/5_Domain_Specific/nbody# jupyterlab --version
    jupyterlab: command not found
    root@ip-172-31-38-109:
    /cuda-samples/Samples/5_Domain_Specific/nbody# miniconda --version
    miniconda: command not found

    There's been a lot of troubleshooting so far with regard to attempting to get cuda installed, so I won't copy-paste my terminal.

    Dan

    Drives auto installed on login not boot

    Reviewed on Feb 15, 2024
    Purchase verified by AWS

    I wanted to use this AMI in my automation to run ML jobs in our platform. What I needed was a Ubuntu 22.04, because podman is in the repo, and Nvidia drivers installed. The downside of this AMI is, Nvidia drivers are installed via /home/ubuntu/.bashrc and not cloud-init. I looked at /var/tmp/nvidia/driver.sh and there was no variable to set to force driver install at cloud-init. Since my automation runs at the end of cloud-init this doesn't work.

    Ema

    Very good

    Reviewed on Jan 20, 2024
    Purchase verified by AWS

    Older reviews are not valid anymore, now at the date of my review the image is very good, it has all the drivers required to run optimized code on various types of NVIDIA GPUs, it has CUDA 12.1 preinstalled and also miniconda and Jupyterlab.
    The machine is ready to run code on GPU very easily with everything you need already in place.

    AI researcher unhappy with NVIDIA software

    Missing drivers

    Reviewed on Dec 18, 2023
    Purchase verified by AWS

    This should be preconfigured to run NVIDIA GPU Cloud (NGC) containers such as the PyTorch one, however it fails on launch on AWS (on a p3.2xlarge instance).

    After sshing in, I see this error message:
    <br/>Installing drivers ...<br/>modprobe: FATAL: Module nvidia not found in directory /lib/modules/6.2.0-1011-aws<br/>
    And sure enough, running containers such as PyTorch (https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch ) does not work:

    <br/>~$ docker run --gpus all -it --rm nvcr.io/nvidia/pytorch:23.11-py3<br/>docker: Error response from daemon: failed to create task for container: failed to create shim task: OCI runtime create failed: runc create failed: unable to start container process: error during container init: error running hook #0: error running hook: exit status 1, stdout: , stderr: Auto-detected mode as 'legacy'<br/>nvidia-container-cli: initialization error: nvml error: driver not loaded: unknown.<br/>

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