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Accelerated PyTorch inference with torch.compile on AWS Graviton processors

AWS Machine Learning

You can see that for the 45 models we benchmarked, there is a 1.35x latency improvement (geomean for the 45 models). You can see that for the 33 models we benchmarked, there is around 2x performance improvement (geomean for the 33 models). We benchmarked 45 models using the scripts from the TorchBench repo.

Benchmark 119
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Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 2: Interactive User Experiences in SageMaker Studio

AWS Machine Learning

SageMaker makes it easy to deploy models into production directly through API calls to the service. It’s a low-level API available for Java, C++, Go, JavaScript, Node.js, PHP, Ruby, and Python. It’s a low-level API available for Java, C++, Go, JavaScript, Node.js, PHP, Ruby, and Python.

Benchmark 126
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Testing times: testingRTC is the smart, synchronized, real-world scenario WebRTC testing solution for the times we live in.

Spearline

And testingRTC offers multiple ways to export these metrics, from direct collection from webhooks, to downloading results in CSV format using the REST API. Flip the script With testingRTC, you only need to write scripts once, you can then run them multiple times and scale them up or down as you see fit. Happy days!

Scripts 98
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Image classification model selection using Amazon SageMaker JumpStart

AWS Machine Learning

The former question addresses model selection across model architectures, while the latter question concerns benchmarking trained models against a test dataset. This post provides details on how to implement large-scale Amazon SageMaker benchmarking and model selection tasks. swin-large-patch4-window7-224 195.4M efficientnet-b5 29.0M

APIs 98
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Train gigantic models with near-linear scaling using sharded data parallelism on Amazon SageMaker

AWS Machine Learning

To get started, follow Modify a PyTorch Training Script to adapt SMPs’ APIs in your training script. In this section, we only call out a few main steps with code snippets from the ready-to-use training script train_gpt_simple.py. The notebook uses the script data_prep_512.py Benchmarking performance.

Scripts 94
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Best practices for load testing Amazon SageMaker real-time inference endpoints

AWS Machine Learning

We first benchmark the performance of our model on a single instance to identify the TPS it can handle per our acceptable latency requirements. Note that the model container also includes any custom inference code or scripts that you have passed for inference. Any issues related to end-to-end latency can then be isolated separately.

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How Games24x7 transformed their retraining MLOps pipelines with Amazon SageMaker

AWS Machine Learning

The code to invoke the pipeline script is available in the Studio notebooks, and we can change the hyperparameters and input/output when invoking the pipeline. This is quite different from our earlier method where we had all the parameters hard coded within the scripts and all the processes were inextricably linked.

Scripts 107