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Benchmarking TorchVision ResNet18 on Amazon SageMaker CPU, GPU, and Inferentia instances (with a Neo twist)

francescopochetti.com · published 12 November 2022

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https://github.com/FraPochetti/KagglePlaygrounds/blob/master/aws_latency_benchmarks/pytorch/pytorch.ipynb
github.com
https://github.com/aws/amazon-sagemaker-examples/tree/main/frameworks/pytorch
github.com
https://github.com/aws/amazon-sagemaker-examples/blob/main/frameworks/pytorch/get_started_mnist_deploy.ipynb
github.com
https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/sagemaker.pytorch.html
sagemaker.readthedocs.io
https://aws.amazon.com/sagemaker/neo/
aws.amazon.com
https://aws.amazon.com/machine-learning/inferentia/
aws.amazon.com
https://medium.com/towards-data-science/a-complete-guide-to-ai-accelerators-for-deep-learning-inference-gpus-aws-inferentia-and-amazon-7a5d6804ef1c
medium.com
https://docs.aws.amazon.com/sagemaker/latest/dg/neo-supported-cloud.html
docs.aws.amazon.com
https://www.linkedin.com/in/mikegchambers/
linkedin.com
https://github.com/FraPochetti/KagglePlaygrounds/blob/master/aws_latency_benchmarks/mxnet/mxnet.ipynb
github.com
https://www.linkedin.com/in/matthewmcclean/
linkedin.com
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