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Deep Learning AMI Amazon Linux

It appears you’ve provided information about a repackaged open-source software image, specifically the Deep Learning AMI (DLAMI) for Amazon Linux. This image is designed for creating deep learning models on Amazon Web Services (AWS) instances that have GPU capabilities. Here’s a breakdown of the key features and components mentioned:

Repackaged Open Source Software: The DLAMI is a image that includes various open-source deep learning frameworks and tools. Users can access and use these software components without the need for manual installation and setup.

Additional Charges for Extended Support: Extended support with a 24-hour response time is available for users who require more comprehensive assistance, but it comes at an additional cost.

Compatible GPU Instances: The DLAMI is optimized for use with specific GPU instance types on AWS, including various P2, P3, G2, G3, G4dn, and Inf1 instances. These instances are well-suited for deep learning workloads due to their GPU capabilities.

Supported Frameworks: The image includes several deep learning frameworks, each with multiple versions. These frameworks include:

Python 2 and Python 3
Apache MXNet (Incubating) 1.6.0 with Gluon
TensorFlow 1.15 with Horovod
TensorFlow 2.0 with Horovod
PyTorch 1.3.1
Chainer 6.1.0
Keras 2.2.4.2
Model Debugging and Hosting Capabilities: The DLAMI also provides tools for model debugging and hosting. These include:

Apache MXNet (Incubating) Model Server 1.0
TensorFlow Serving 1.15
TensorBoard 1.15
GPU Drivers: The image includes NVIDIA GPU drivers (version 418.87.03), which are necessary for utilizing GPU resources efficiently when running deep learning tasks.

Cost-Effective Inference: The image is designed to enable users to switch from GPU instances to Inf1 instances for inference tasks. Inf1 instances are optimized for cost-effective inference, potentially offering up to a 40% lower cost per inference compared to GPU instances.

Overall, this DLAMI simplifies the process of setting up a deep learning environment on AWS by providing software, GPU support, and options for extended support. Users can choose from a range of deep learning frameworks and easily switch between different AWS instance types to optimize cost and performance for their specific use cases.”

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