Amazon Deep Learning ARM64 AMI GPU PyTorch 2.4 (Ubuntu 22.04)
For help getting started, see Getting started with DLAMI.
AMI name format
Deep Learning ARM64 AMI OSS Nvidia Driver GPU PyTorch 2.4.${PATCH-VERSION} (Ubuntu 22.04) ${YYYY-MM-DD}
Supported EC2 instances
G5g
The AMI includes the following:
Supported Amazon Service: Amazon EC2
Operating System: Ubuntu 22.04
Compute Architecture: ARM64
Python: /opt/conda/envs/pytorch/bin/python
Python version: 3.11
NVIDIA Driver:
OSS Nvidia driver: 550.144.03
NVIDIA CUDA12.1 stack:
CUDA, NCCL and cuDDN installation path: /usr/local/cuda-12.4/
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Default CUDA: 12.4
PATH /usr/local/cuda points to /usr/local/cuda-12.4/
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Updated below env vars:
LD_LIBRARY_PATH to have /usr/local/cuda/lib:/usr/local/cuda/lib64:/usr/local/cuda:/usr/local/cuda/targets/sbsa-linux/lib:/usr/local/cuda/nvvm/lib64:/usr/local/cuda/extras/CUPTI/lib64
PATH to have /usr/local/cuda/bin/:/usr/local/cuda/include/
Compiled system NCCL Version present at /usr/local/cuda/: 2.21.5
PyTorch Compiled NCCL Version from PyTorch conda environment: 2.20.5
Amazon CLI v2 as aws2 and Amazon CLI v1 as aws
EBS volume type: gp3
Query AMI-ID with SSM Parameter (example Region is us-east-1):
aws ssm get-parameter --regionus-east-1\ --name /aws/service/deeplearning/ami/arm64/oss-nvidia-driver-gpu-pytorch-2.4-ubuntu-22.04/latest/ami-id \ --query "Parameter.Value" \ --output textQuery AMI-ID with AWSCLI (example Region is us-east-1):
aws ec2 describe-images --regionus-east-1\ --owners amazon \ --filters 'Name=name,Values=Deep Learning ARM64 AMI OSS Nvidia Driver GPU PyTorch 2.4.? (Ubuntu 22.04) ????????' 'Name=state,Values=available' \ --query 'reverse(sort_by(Images, &CreationDate))[:1].ImageId' \ --output text
Release Date: 2025-02-17
AMI name: Deep Learning ARM64 AMI OSS Nvidia Driver GPU PyTorch 2.4.0 (Ubuntu 22.04) 20250215
Updated
Updated NVIDIA Container Toolkit from version 1.17.3 to version 1.17.4
Please see the release notes page here for more information: https://github.com/NVIDIA/nvidia-container-toolkit/releases/tag/v1.17.4
In Container Toolkit version 1.17.4, the mounting of CUDA compat libraries is now disabled. In order to ensure compatibility with multiple CUDA versions on container workflows, please ensure you update your LD_LIBRARY_PATH to include your CUDA compatibility libraries as shown in the If you use a CUDA compatibility layer tutorial.
Removed
Removed user space libraries cuobj and nvdisasm provided by NVIDIA CUDA toolkit
to address CVEs present in the NVIDIA CUDA Toolkit Security Bulletin for February 18, 2025
Release Date: 2025-01-21
AMI name: Deep Learning ARM64 AMI OSS Nvidia Driver GPU PyTorch 2.4.0 (Ubuntu 22.04) 20250117
Updated
Upgraded Nvidia driver from version 550.127.05 to 550.144.03 to address CVEs present in the NVIDIA GPU Display Driver Security Bulletin for January 2025
.
Release Date: 2024-09-30
AMI name: Deep Learning ARM64 AMI OSS Nvidia Driver GPU PyTorch 2.4.0 (Ubuntu 22.04) 20240927
Updated
Upgraded Nvidia Container Toolkit from version 1.16.1 to 1.16.2, addressing the security vulnerability CVE-2024-0133
.
Release Date: 2024-09-26
AMI name: Deep Learning ARM64 AMI OSS Nvidia Driver GPU PyTorch 2.4.0 (Ubuntu 22.04) 20240926
Added
Initial release of Deep Learning ARM64 AMI OSS Nvidia Driver GPU PyTorch 2.4.0 (Ubuntu 22.04) series. Including a conda environment pytorch complimented with NVIDIA Driver R550, CUDA=12.4, cuDNN=8.9.7, PyTorch NCCL=2.20.5.