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‎docs/.nav.yml‎

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- Getting Started:
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- get_started/index.md
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- Using Deep Learning Containers: get_started/using_dlcs.md
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- Release Notes:
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- releasenotes/index.md
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- Base: releasenotes/base/index.md
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- SGLang: releasenotes/sglang/index.md
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- vLLM: releasenotes/vllm/index.md
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- PyTorch: releasenotes/pytorch/index.md
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- Tensorflow: releasenotes/tensorflow/index.md
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- Tutorials: tutorials
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- Reference:
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- Available Images: reference/available_images.md

‎docs/releasenotes/base/cuda-12.8-ec2.md‎

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# AWS Deep Learning Base Containers for EC2, ECS, EKS (CUDA 12.8)
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[AWS Deep Learning Containers (DLCs)](https://aws.amazon.com/machine-learning/containers/) now support Base images that serve as a foundational layer to build the machine learning environment on EC2, ECS and EKS, with Ubuntu 24.04.
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These Base DLCs package the essential deep learning components and dependencies without being tied to a specific framework implementation, providing users the flexibility to customize the DLCs with their preferred frameworks.
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## Release Notes
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- Development Tools: Includes curl, build-essential, cmake, and git
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- Python Environment: Python 3.12 with AWS CLI, boto3, and requests pre-installed
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- GPU Support: CUDA 12.8.1 with cuda-compat for backward compatibility
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- Neural Network Libraries: cuDNN 9.8.0.87 for deep neural network operations
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- Distributed Training: NCCL 2.26.2-1 for multi-GPU and multi-node communication
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- Network Performance: EFA 1.40.0 for low-latency network communications
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## Security Advisory
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AWS recommends that customers monitor critical security updates in the [AWS Security Bulletin](https://aws.amazon.com/security/security-bulletins/).
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## Python Support
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Python 3.12 is supported.
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## GPU Instance Type Support
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- CUDA 12.8
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- cuDNN 9.8.0.87
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- NCCL 2.26.2-1
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## Example URL
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```
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763104351884.dkr.ecr.us-east-1.amazonaws.com/base:12.8.1-gpu-py312-cu128-ubuntu24.04-ec2
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```
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## Build and Test
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- Built on: c5.18xlarge
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- Tested on: p4d.24xlarge, p4de.24xlarge, p5.48xlarge
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- Tested with: [openclip](https://github.com/mlfoundations/open_clip), [nccl-tests](https://github.com/NVIDIA/nccl-tests)
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## Known Issues
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No known issues so far.

‎docs/releasenotes/base/cuda-12.9-ec2.md‎

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# AWS Deep Learning Base Containers for EC2, ECS, EKS (CUDA 12.9)
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[AWS Deep Learning Containers (DLCs)](https://aws.amazon.com/machine-learning/containers/) now support Base images that serve as a foundational layer to build the machine learning environment on EC2, ECS and EKS, with Ubuntu 22.04.
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These Base DLCs package the essential deep learning components and dependencies without being tied to a specific framework implementation, providing users the flexibility to customize the DLCs with their preferred frameworks.
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## Release Notes
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- Development Tools: Includes curl, build-essential, cmake, and git
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- Python Environment: Python 3.12 with AWS CLI, boto3, and requests pre-installed
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- GPU Support: CUDA 12.9.1 with cuda-compat for backward compatibility
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- Neural Network Libraries: cuDNN 9.10.2.21 for deep neural network operations
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- Distributed Training: NCCL 2.27.3-1 for multi-GPU and multi-node communication
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- Network Performance: EFA 1.43.1 for low-latency network communications
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## Security Advisory
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AWS recommends that customers monitor critical security updates in the [AWS Security Bulletin](https://aws.amazon.com/security/security-bulletins/).
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## Python Support
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Python 3.12 is supported.
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## GPU Instance Type Support
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- CUDA 12.9
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- cuDNN 9.10.2.21
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- NCCL 2.27.3-1
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## Example URL
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```
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763104351884.dkr.ecr.us-east-1.amazonaws.com/base:12.9.1-gpu-py312-cu129-ubuntu22.04-ec2
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```
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## Build and Test
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- Built on: c5.18xlarge
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- Tested on: p4d.24xlarge, p4de.24xlarge, p5.48xlarge
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- Tested with: [openclip](https://github.com/mlfoundations/open_clip), [nccl-tests](https://github.com/NVIDIA/nccl-tests)
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## Known Issues
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No known issues so far.

‎docs/releasenotes/base/cuda-13.0-ec2.md‎

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# AWS Deep Learning Base Containers for EC2, ECS, EKS (CUDA 13.0)
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[AWS Deep Learning Containers (DLCs)](https://aws.amazon.com/machine-learning/containers/) now support Base images that serve as a foundational layer to build the machine learning environment on EC2, ECS and EKS, with Ubuntu 22.04.
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These Base DLCs package the essential deep learning components and dependencies without being tied to a specific framework implementation, providing users the flexibility to customize the DLCs with their preferred frameworks.
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## Release Notes
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- Development Tools: Includes curl, build-essential, cmake, and git
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- Python Environment: Python 3.12 with AWS CLI, boto3, and requests pre-installed
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- GPU Support: CUDA 13.0.0 with cuda-compat for backward compatibility
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- Neural Network Libraries: cuDNN 9.13.0.50 for deep neural network operations
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- Distributed Training: NCCL 2.27.7-1 for multi-GPU and multi-node communication
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- Network Performance: EFA 1.44.0 for low-latency network communications
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## Security Advisory
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AWS recommends that customers monitor critical security updates in the [AWS Security Bulletin](https://aws.amazon.com/security/security-bulletins/).
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## Python Support
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Python 3.12 is supported.
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## GPU Instance Type Support
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- CUDA 13.0
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- cuDNN 9.13.0.50
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- NCCL 2.27.7-1
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## Example URL
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```
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763104351884.dkr.ecr.us-east-1.amazonaws.com/base:13.0.0-gpu-py312-cu130-ubuntu22.04-ec2
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```
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## Build and Test
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- Built on: c5.18xlarge
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- Tested on: p4d.24xlarge, p4de.24xlarge, p5.48xlarge
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- Tested with: [openclip](https://github.com/mlfoundations/open_clip), [nccl-tests](https://github.com/NVIDIA/nccl-tests)
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## Known Issues
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No known issues so far.

‎docs/releasenotes/base/index.md‎

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# Base Container Release Notes
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Release notes for AWS Deep Learning Base Containers with CUDA support.
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## CUDA 13.0
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| Platform | Type | Link |
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| ------------- | ------- | --------------------------------------------------- |
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| EC2, ECS, EKS | General | [Base CUDA 13.0 on EC2, ECS, EKS](cuda-13.0-ec2.md) |
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## CUDA 12.9
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| Platform | Type | Link |
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| ------------- | ------- | --------------------------------------------------- |
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| EC2, ECS, EKS | General | [Base CUDA 12.9 on EC2, ECS, EKS](cuda-12.9-ec2.md) |
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## CUDA 12.8
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| Platform | Type | Link |
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| ------------- | ------- | --------------------------------------------------- |
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| EC2, ECS, EKS | General | [Base CUDA 12.8 on EC2, ECS, EKS](cuda-12.8-ec2.md) |

‎docs/releasenotes/index.md‎

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# Release Notes
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This section contains release notes for AWS Deep Learning Containers organized by framework.
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## Frameworks
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- [Base](base/index.md) - Release notes for Base CUDA containers
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- [SGLang](sglang/index.md) - Release notes for SGLang inference containers
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- [vLLM](vllm/index.md) - Release notes for vLLM inference containers
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- [PyTorch](pytorch/index.md) - Release notes for PyTorch containers
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- [TensorFlow](tensorflow/index.md) - Release notes for TensorFlow containers
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## Resources
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- [Available Images](../reference/available_images.md)
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- [Support Policy](../reference/support_policy.md)
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- [GitHub Repository](https://github.com/aws/deep-learning-containers)
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- [Discussion Forum](https://repost.aws/tags/TAtQOYCNQXQAypuIl0ZxRowA/aws-deep-learning-containers)

‎docs/releasenotes/pytorch/2.4-graviton-ec2-ecs-eks.md‎

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# AWS Deep Learning Containers for PyTorch 2.4 Graviton on EC2, ECS, and EKS
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[AWS Deep Learning Containers](https://aws.amazon.com/machine-learning/containers/) (DLC) for Amazon Elastic Kubernetes Service (EKS), Amazon Elastic Compute Cloud (EC2), and Amazon Elastic Container Service (ECS) are now available for the [Graviton](https://aws.amazon.com/ec2/graviton/) instance type with support for PyTorch 2.4.
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This release includes container images for inference on CPU and GPU, optimized for performance and scale on AWS. The CPU image has been tested with each of the EC2, ECS, and EKS services, while the GPU image only supports EC2 (see the table below). The GPU image provides stable versions of NVIDIA CUDA, cuDNN, NCCL, and other components. All software components in these images are scanned for security vulnerabilities and updated or patched in accordance with AWS Security best practices.
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| | EC2 | ECS | EKS |
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| ------------------------ | --------- | ------------- | ------------- |
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| Graviton CPU | Supported | Supported | Supported |
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| Graviton with NVIDIA GPU | Supported | Not Supported | Not Supported |
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## Release Notes
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- Introduced containers for PyTorch 2.4 for inference supporting EC2, ECS, and EKS on Graviton instances. For details about this release, check out our GitHub release tags: [for CPU](https://github.com/aws/deep-learning-containers/releases/tag/v1.0-pt-graviton-ec2-2.4.0-inf-cpu-py311) and [for GPU](https://github.com/aws/deep-learning-containers/releases/tag/v1.0-pt-graviton-ec2-2.4.0-inf-gpu-py311).
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- TorchServe version: 0.11.1
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- 11/01/24: Updated TorchServe to 0.12.0 (release tags: [for CPU](https://github.com/aws/deep-learning-containers/releases/tag/v1.5-pt-graviton-ec2-2.4.0-inf-cpu-py311) and [for GPU](https://github.com/aws/deep-learning-containers/releases/tag/v1.4-pt-graviton-ec2-2.4.0-inf-gpu-py311))
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- The GPU image is the first ever DLC supporting Graviton (ARM64) + GPU platforms. It should be used with the [G5g instance type](https://aws.amazon.com/ec2/instance-types/g5g/), which is powered by Graviton CPUs and NVIDIA T4G Tensor Core GPUs.
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- Please refer to the official PyTorch 2.4 release notes [here](https://github.com/pytorch/pytorch/releases/tag/v2.4.0) for framework updates.
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## Performance Improvements
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These DLCs continue to deliver the best performance on Graviton CPU for BERT and RoBERTa sentiment analysis and fill mask models, making Graviton3 the most cost effective CPU platform on the AWS cloud for these models. For more information, please refer to the [Graviton PyTorch User Guide](https://github.com/aws/aws-graviton-getting-started/blob/main/machinelearning/pytorch.md).
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## Security Advisory
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AWS recommends that customers monitor critical security updates in the [AWS Security Bulletin](https://aws.amazon.com/security/security-bulletins/).
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## Python 3.11 Support
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Python 3.11 is supported in the PyTorch Graviton Inference containers.
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## CPU Instance Type Support
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The containers support Graviton CPU instance types supported under each of the above mentioned services.
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## GPU Instance Type Support
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The containers support the Graviton GPU instance type G5g and contain the following software components for GPU support:
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- CUDA 12.4.0
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- cuDNN 9.1.0.70+cuda12.4
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- NCCL 2.20.5+cuda12.4
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## Build and Test
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- Built on: c6g.2xlarge
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- Tested on: c7g.4xlarge, c6g.4xlarge, t4g.2xlarge, r6g.2xlarge, m6g.4xlarge, g5g.4xlarge
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- Tested with [MNIST](http://yann.lecun.com/exdb/mnist/) and Resnet50/DenseNet datasets on EC2, ECS AMI (Amazon Linux AMI 2.0.20220822 arm64) and EKS AMI (1.25.6-20230304 arm64)
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## Known Issues
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- There is no official [Triton](https://github.com/triton-lang/triton) distribution for ARM64/aarch64 yet, so some torch.compile workloads will fail with:
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```
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torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised:
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RuntimeError: Cannot find a working triton installation. More information on installing Triton can be found at https://github.com/openai/triton
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```
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For latest updates, please refer to the [aws/deep-learning-containers GitHub repo](https://github.com/aws/deep-learning-containers/tags).

‎docs/releasenotes/pytorch/2.4-graviton-sagemaker.md‎

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# AWS Deep Learning Containers for PyTorch 2.4 Graviton on SageMaker
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[AWS Deep Learning Containers](https://aws.amazon.com/machine-learning/containers/) (DLCs) for Amazon SageMaker are now available for the [Graviton](https://aws.amazon.com/ec2/graviton/) instance type with support for PyTorch 2.4.
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This release includes a container image for inference on CPU, optimized for performance and scale on AWS. This Docker image was tested on SageMaker. All software components in this image are scanned for security vulnerabilities and updated or patched in accordance with AWS Security best practices.
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Please refer to the [SageMaker Graviton blog](https://aws.amazon.com/blogs/machine-learning/run-machine-learning-inference-workloads-on-aws-graviton-based-instances-with-amazon-sagemaker/) and DLC [developer guide](https://docs.aws.amazon.com/dlami/latest/devguide/deep-learning-containers.html) to migrate the Deep Learning workloads to Graviton instances.
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## Release Notes
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- Introduced container for PyTorch 2.4 for inference supporting SageMaker services on Graviton instances. For details about this release, check out our GitHub [release tag](https://github.com/aws/deep-learning-containers/releases/tag/v1.0-pt-graviton-sagemaker-2.4.0-inf-cpu-py311).
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- TorchServe version: 0.11.1
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- 10/25/24: Updated TorchServe to 0.12.0 ([release tag](https://github.com/aws/deep-learning-containers/releases/tag/v1.3-pt-graviton-sagemaker-2.4.0-inf-cpu-py311))
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- Please refer to the official PyTorch 2.4 release notes [here](https://github.com/pytorch/pytorch/releases/tag/v2.4.0) for framework updates.
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## Performance Improvements
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These DLCs continue to deliver the best performance on Graviton for BERT and RoBERTa sentiment analysis and fill mask models, making Graviton3 the most cost effective CPU platform on the AWS cloud for these models. For more information, please refer to the [Graviton PyTorch User Guide](https://github.com/aws/aws-graviton-getting-started/blob/main/machinelearning/pytorch.md).
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## Security Advisory
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AWS recommends that customers monitor critical security updates in the [AWS Security Bulletin](https://aws.amazon.com/security/security-bulletins/).
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## Python 3.11 Support
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Python 3.11 is supported in the PyTorch Graviton Inference containers.
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## CPU Instance Type Support
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The containers support Graviton CPU instance types supported under SageMaker.
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## Build and Test
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- Built on: c6g.2xlarge
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- Tested on: c7g.4xlarge, c6g.4xlarge, t4g.2xlarge, r6g.2xlarge, m6g.4xlarge
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- Tested with [MNIST](http://yann.lecun.com/exdb/mnist/) and Resnet50/DenseNet datasets on EC2, ECS AMI (Amazon Linux AMI 2.0.20220822 arm64) and EKS AMI (1.25.6-20230304 arm64)
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## Known Issues
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- None
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For latest updates, please refer to the [aws/deep-learning-containers GitHub repo](https://github.com/aws/deep-learning-containers/tags).

‎docs/releasenotes/pytorch/2.4-inference-ec2-ecs-eks.md‎

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# AWS Deep Learning Containers for PyTorch 2.4 Inference on EC2, ECS and EKS
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[AWS Deep Learning Containers](https://aws.amazon.com/machine-learning/containers/) (DLCs) for Amazon Elastic Compute Cloud (EC2), Amazon Elastic Container Service (ECS), and Amazon Elastic Kubernetes Service (EKS) are now available with PyTorch 2.4 and support for CUDA 12.4 on Ubuntu 22.04.
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This release includes container images for inference on CPU and GPU, optimized for performance and scale on AWS. These Docker images have been tested with EC2, ECS and EKS services, and provide stable versions of NVIDIA CUDA, cuDNN, Intel MKL, and other components. All software components in these images are scanned for security vulnerabilities and updated or patched in accordance with AWS Security best practices. If you are looking for a DLC to use with SageMaker, please refer to [this documentation](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#general-framework-containers-ec2-ecs-eks--sm-support).
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## Release Notes
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- Introduced containers for PyTorch 2.4.0 for inference supporting EC2, ECS, and EKS. For details about this release, check out our GitHub [release tag](https://github.com/aws/deep-learning-containers/releases/tag/v1.0-pt-ec2-2.4.0-inf-py311).
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- PyTorch 2.4 offers support for python custom operator API allowing users to integrate custom kernels such as Triton kernels into torch.compile.
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- Please refer to the official PyTorch 2.4 release notes [here](https://github.com/pytorch/pytorch/releases/tag/v2.4.0) for the full description of updates.
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- Added Python 3.11 support
13+
- Added CUDA 12.4 support
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- Added Ubuntu 22.04 support
15+
- The Dockerfile for CPU can be found [here](https://github.com/aws/deep-learning-containers/blob/master/pytorch/inference/docker/2.4/py3/Dockerfile.cpu), and the Dockerfile for GPU can be found [here](https://github.com/aws/deep-learning-containers/blob/master/pytorch/inference/docker/2.4/py3/cu124/Dockerfile.gpu).
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## Security Advisory
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AWS recommends that customers monitor critical security updates in the [AWS Security Bulletin](https://aws.amazon.com/security/security-bulletins/).
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## Python 3.11 Support
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Python 3.11 is supported in the PyTorch Inference containers.
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## CPU Instance Type Support
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The containers support x86_64 CPU instance types.
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## GPU Instance Type Support
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The containers support GPU instance types and contain the following software components for GPU support:
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- CUDA 12.4.1
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- cuDNN 9.1.0.70+cuda12.4
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- NCCL 2.22.3+cuda12.4
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## Build and Test
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- Built on: c5.18xlarge
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- Tested on: c5.18xlarge, g3.16xlarge, m5.16xlarge, t3.2xlarge, p3.16xlarge, p3dn.24xlarge, p4d.24xlarge, g4dn.xlarge
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- Tested with [MNIST](http://yann.lecun.com/exdb/mnist/) and Resnet50/ImageNet datasets on EC2, ECS AMI (Amazon Linux AMI 2.0.20221102), and EKS AMI (amazon-eks-gpu-node-1.25.16-20240307)

‎docs/releasenotes/pytorch/2.4-inference-sagemaker.md‎

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# AWS Deep Learning Containers for PyTorch 2.4 Inference on SageMaker
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[AWS Deep Learning Containers](https://aws.amazon.com/machine-learning/containers/) (DLC) for Amazon SageMaker are now available with support for PyTorch 2.4 and support for CUDA 12.4 on Ubuntu 22.04.
4+
5+
This release includes container images for inference on CPU and GPU, optimized for performance and scale on AWS. These Docker images have been tested with SageMaker services, and provide stable versions of NVIDIA CUDA, cuDNN, and other components. All software components in these images are scanned for security vulnerabilities and updated or patched in accordance with AWS Security best practices.
6+
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## Release Notes
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9+
- Introduced containers for PyTorch 2.4 for inference supporting SageMaker services. For details about this release, check out our GitHub [release tag](https://github.com/aws/deep-learning-containers/releases/tag/v1.0-pt-sagemaker-2.4.0-inf-py311).
10+
- PyTorch 2.4 offers support for python custom operator API allowing users to integrate custom kernels such as Triton kernels into torch.compile.
11+
- Please refer to the official PyTorch 2.4 release notes [here](https://github.com/pytorch/pytorch/releases/tag/v2.4.0) for the full description of updates.
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- Added Python 3.11 support
13+
- Added CUDA 12.4 support
14+
- Added Ubuntu 22.04 support
15+
- Added TorchServe 0.11.1 support
16+
- 10/25/24: Updated TorchServe to 0.12.0 ([release tag](https://github.com/aws/deep-learning-containers/releases/tag/v1.1-pt-sagemaker-2.4.0-inf-py311))
17+
- The Dockerfile for CPU can be found [here](https://github.com/aws/deep-learning-containers/blob/master/pytorch/inference/docker/2.4/py3/Dockerfile.cpu), and the Dockerfile for GPU can be found [here](https://github.com/aws/deep-learning-containers/blob/master/pytorch/inference/docker/2.4/py3/cu124/Dockerfile.gpu).
18+
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## Security Advisory
20+
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AWS recommends that customers monitor critical security updates in the [AWS Security Bulletin](https://aws.amazon.com/security/security-bulletins/).
22+
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## Python 3.11 Support
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Python 3.11 is supported in the PyTorch Inference containers.
26+
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## CPU Instance Type Support
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The containers support x86_64 CPU instance types.
30+
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## GPU Instance Type Support
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The containers support GPU instance types and contain the following software components for GPU support:
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- CUDA 12.4.1
36+
- cuDNN 9.1.0.70+cuda12.4
37+
- NCCL 2.22.3+cuda12.4
38+
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## Build and Test
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- Built on: c5.18xlarge
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- Tested on: c5.18xlarge, g3.16xlarge, m5.16xlarge, t3.2xlarge, p3.16xlarge, p3dn.24xlarge, p4d.24xlarge, g4dn.xlarge
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- Tested with [MNIST](http://yann.lecun.com/exdb/mnist/) and Resnet50/ImageNet datasets on EC2, ECS AMI (Amazon Linux AMI 2.0.20221102), and EKS AMI (amazon-eks-gpu-node-1.25.16-20240307)

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