# AWS Deep Learning ARM64 AMI GPU PyTorch 2.4 (Ubuntu 22.04)
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For help getting started, see [Getting started with DLAMI](getting-started.md).

#### AMI Name Format
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+ Deep Learning ARM64 AMI OSS Nvidia Driver GPU PyTorch 2.4.\$1\$1PATCH\$1VERSION\$1 (Ubuntu 22.04) \$1\$1YYYY-MM-DD\$1

#### Supported EC2 instances
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+ G5g

#### The AMI includes the following:
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+ **Supported AWS 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/
  + **Default CUDA:** 12.4
    + PATH /usr/local/cuda points to /usr/local/cuda-12.4/
    + Updated below env vars:
      + LD\$1LIBRARY\$1PATH 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
+ **AWS CLI v2** as aws2 and **AWS CLI v1** as aws
+ **EBS volume type**: gp3
+ **Query AMI-ID with SSM Parameter (example Region is us-east-1):**

  ```
  aws ssm get-parameter --region us-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 text
  ```
+ **Query AMI-ID with AWSCLI (example Region is us-east-1):**

  ```
  aws ec2 describe-images --region us-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
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**AMI name**: Deep Learning ARM64 AMI OSS Nvidia Driver GPU PyTorch 2.4.0 (Ubuntu 22.04) 20250215

##### Updated
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+ 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](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\$1LIBRARY\$1PATH to include your CUDA compatibility libraries as shown in the [If you use a CUDA compatibility layer](https://docs.aws.amazon.com/sagemaker/latest/dg/inference-gpu-drivers.html#collapsible-cuda-compat) tutorial.

##### Removed
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+ Removed user space libraries cuobj and nvdisasm provided by [NVIDIA CUDA toolkit](https://docs.nvidia.com/cuda/) to address CVEs present in the [ NVIDIA CUDA Toolkit Security Bulletin for February 18, 2025](https://nvidia.custhelp.com/app/answers/detail/a_id/5594)

#### Release Date: 2025-01-21
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**AMI name**: Deep Learning ARM64 AMI OSS Nvidia Driver GPU PyTorch 2.4.0 (Ubuntu 22.04) 20250117

##### Updated
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+ 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](https://nvidia.custhelp.com/app/answers/detail/a_id/5614).

#### Release Date: 2024-09-30
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**AMI name**: Deep Learning ARM64 AMI OSS Nvidia Driver GPU PyTorch 2.4.0 (Ubuntu 22.04) 20240927

##### Updated
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+ Upgraded Nvidia Container Toolkit from version 1.16.1 to 1.16.2, addressing the security vulnerability [CVE-2024-0133](https://nvd.nist.gov/vuln/detail/CVE-2024-0133).

#### Release Date: 2024-09-26
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**AMI name**: Deep Learning ARM64 AMI OSS Nvidia Driver GPU PyTorch 2.4.0 (Ubuntu 22.04) 20240926

##### Added
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+ 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.