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Amazon SageMaker Multi-Model Endpoints provides a scalable and cost-effective way to deploy large numbers of custom machine learning models. SageMaker Multi-Model endpoints will let you deploy multiple ML models on a single endpoint and serve them using a single serving container. Your application simply needs to include an API call with the target model to this endpoint to achieve low latency, high throughput inference. Instead of paying for a separate endpoint for every single model, you can host many models for the price of a single endpoint. For detailed information about multi-model endpoints, see Save on inference costs by using Amazon SageMaker multi-model endpoints.

In this repository, we demonstrate how to host two computer vision models trained using the TensorFlow framework under one SageMaker multi-model endpoint. For a detailed full walkthrough of the example covered in this repo, see this accompanying AWS blog post. For the first model, we train a smaller version of AlexNet CNN to classify images from the CIFAR-10 dataset. For the second model, we use a pretrained VGG16 CNN model pretrained on the ImageNet dataset and fine-tuned on the Sign Language Digits Dataset to classify hand symbol images.

For model-1, we will use the CIFAR-10 dataset. CIFAR-10 is a benchmark dataset for image classification in the CV and ML literature. CIFAR images are colored (three channels) with dramatic variation in how the objects appear. It consists of 32×32 color images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images.

For model-2, we will use the sign language digits dataset. This dataset distinguishes the sign language digits from 0 to 9. The figure below shows a sample of the dataset. Following are the details of the dataset:

• Number of classes = 10 (digits 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9)
• Image size = 100 × 100
• Color space = RGB
• 1,712 images in the training set
• 300 images in the validation set
• 50 images in the test set

See CONTRIBUTING for more information.

This library is licensed under the MIT-0 License. See the LICENSE file.