YOLOv8 is a powerful and popular deep learning model for object detection. It has been used in a variety of applications, from self-driving cars to facial recognition. Now, thanks to Amazon SageMaker, it is possible to deploy YOLOv8 PyTorch models on Amazon SageMaker endpoints. This article will explain how to do this.
The first step is to create a SageMaker notebook instance. This can be done through the Amazon SageMaker console. Once the instance is created, you can upload your model to the instance. You can use the PyTorch model zoo or create your own model.
Once the model is uploaded, you need to create a SageMaker endpoint configuration. This will define the type of instance and the number of instances that will be used for inference. You can also specify the instance type and the number of GPUs that will be used for inference.
Next, you need to create an inference pipeline. This will define how your model will be used for inference. You can specify the input data type, the output data type, and the pre-processing and post-processing steps that will be used for inference.
Finally, you can deploy your model to the SageMaker endpoint. This will allow you to use your model for inference in real-time. You can also use the SageMaker API to monitor the performance of your model and make changes as needed.
Deploying YOLOv8 PyTorch models on Amazon SageMaker endpoints is a great way to take advantage of the power of deep learning for object detection. By following the steps outlined above, you can quickly and easily deploy your model and start using it for inference in real-time.
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