Accountability APIs Benchmark Scripts 
AWS Machine Learning
MAY 22, 2024
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.
AWS Machine Learning
FEBRUARY 6, 2023
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
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
AWS Machine Learning
MARCH 20, 2023
Refer to the appendix for instance details and benchmark data. Use the supplied Python scripts for quantization. Run the provided Python test scripts to invoke the SageMaker endpoint for both INT8 and FP32 versions. Quantizing the model in PyTorch is possible with a few APIs from Intel PyTorch extensions.
AWS Machine Learning
MARCH 13, 2024
. * The `if __name__ == "__main__"` block checks if the script is being run directly or imported. To run the script, you can use the following command: ``` python hello.py ``` * The output will be printed in the console: ``` Hello, world! If it is run directly, the `hello()` function is called. * This looks pretty good!
AWS Machine Learning
APRIL 27, 2023
Similar to the process of PyTorch integration with C++ code, Neuron CustomOps requires a C++ implementation of an operator via a NeuronCore-ported subset of the Torch C++ API. Finally, the custom library is built by calling the load API. For more information, refer to Custom Operators API Reference Guide [Experimental].
AWS Machine Learning
JUNE 6, 2023
The following figure shows a performance benchmark of fine-tuning a RoBERTa model on Amazon EC2 p4d.24xlarge inference with AWS Graviton processors for details on AWS Graviton-based instance inference performance benchmarks for PyTorch 2.0. Run your DLC container with a model training script to fine-tune the RoBERTa model.
AWS Machine Learning
JANUARY 19, 2024
We use the Recognizing Textual Entailment dataset from the GLUE benchmarking suite. Choose Request increase at account-level. The requested quota approval may take some time to complete depending on the account permissions. The following diagram provides an overview of the workflow explained in this post. Choose Request.
Expert insights. Personalized for you.
103 
Let's personalize your content