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This software product is presented in a repackaged form, with the option for additional technical support available upon request. For inquiries regarding support, please contact us at support@[provider email]. The product is based on the AWS Deep Learning AMI (Amazon Linux), a specialized platform optimized for developing, training, debugging, and deploying deep learning models on EC2 instances. It comes with popular deep learning frameworks such as TensorFlow, MXNet, PyTorch, and more. These frameworks are set up within Conda environments to ensure stability and isolation, providing users with a reliable development environment.

It’s worth noting that the AWS Deep Learning AMI is available to Amazon EC2 users at no additional cost, making it accessible to a wide range of users. It is with the latest versions of NVIDIA CUDA, cuDNN, and Intel acceleration libraries like MKL-DNN to ensure high performance across various AWS EC2 instance types.

Key components of the AWS Deep Learning AMI include:

Popular deep learning frameworks, including TensorFlow (1.x, 2.x), PyTorch (1.x), and MXNet (1.x), all finely tuned for AWS infrastructures. These frameworks are organized into Conda environments, ready for immediate use.

Built-in support for AWS Elastic Inference, enhancing your deep learning capabilities.

A comprehensive suite of AWS Deep Learning Tools, including AWS Elastic Fabric Adapter (EFA), to optimize deep learning workflows.

NVIDIA Deep Learning Software stack, encompassing the NVIDIA GPU Driver, CUDA Toolkit, cuDNN, NCCL, and Fabric Manager.

Containerization capabilities with Docker and NVIDIA-Docker, enabling the easy creation and deployment of GPU-accelerated Docker containers.

Leveraging the Intel Architecture performance library Intel MKL-DNN, the AMI delivers enhanced performance, especially on CPU and GPU AWS EC2 instance types.

A rich collection of popular tools such as awscli, boto3, numpy, scikit-learn, opencv, pandas, matplotlib, graphviz, jupyter, ipython, and more, providing comprehensive support for data science and deep learning tasks.