234 points by syntheticmind 1 year ago flag hide 12 comments
curiouslearner 1 year ago next
This is really impressive! I've been trying to make something similar but had a hard time understanding how to deploy TensorFlow models on Lambda. Any tips to share?
codewizard 1 year ago next
Make sure to optimize and convert your TensorFlow models with TensorFlow Serving or using tools like tflite. It will help you reduce the size and improve the inference time on Lambda. Also, AWS Serverless Application Model (SAM) can ease the deployment process.
devopsguru 1 year ago prev next
@curiousLearner You can look into using AWS Lambda Layers for TensorFlow dependencies as well, making your deployment packages more manageable.
datavizmaster 1 year ago prev next
What did you use to preprocess and resize the images in your serverless system?
imageprocessor 1 year ago next
I've used AWS Lambda with the Pillow library to preprocess the images and resize them based on the model input size requirements before sending them to AWS Lambda for inference.
securityengineer 1 year ago prev next
Did you consider using any VPC functions to protect the image recognition system or did you rely completely on IAM roles?
curiouslearner 1 year ago next
I consider using a VPC, but I wanted to keep the system simple without managing additional networking components. IAM Roles provided me enough control and protection for the time being.
machinelearningguy 1 year ago prev next
Optimizing the AWS Lambda memory settings to fit your TensorFlow model can give you a substantial speed boost. Make sure to test around with this parameter.
perfmaster 1 year ago next
100% agreed. Benchmarking various memory configurations to accommodate your specific TensorFlow requirements on Lambda will make a huge difference in performance.
cloudexplorer 1 year ago prev next
Really well done. Can you touch on monitoring and scalability? How do you manage these aspects when working with serverless image recognition?
automateallthethings 1 year ago next
I simply use AWS CloudWatch alarms monitoring the invocation count, error rates, and concurrency along with AWS Lambda provisioned concurrency to handle the scaling aspects. It works seamlessly as the incoming traffic increases.
tensorflowfan 1 year ago prev next
This tutorial demonstrates a powerful functionality using TensorFlow and serverless computing. Thanks for sharing your experience, I'll be diving into this today!