Developing and deploying a deep learning model involves many steps: gathering and cleansing data, designing the model, fine-tuning model parameters, evaluating the results, and going through it again until a desirable result is achieved. Then comes the final step: deploying the model.
AWS Lambda is one of the most cost effective service that lets you run code without provisioning or managing servers. It offers many advantages when working with serverless infrastructure. When you break down the logic of your deep learning service into a single Lambda function for a single request, things become much simpler and easy to scale. You can forget all about the resource handling needed for the parallel requests coming into your model. If your usage is sparse and tolerable to a higher latency, Lambda is a great choice among various solutions.
Now, let’s say you’ve decided to use Lambda to deploy your model. You go through the process, but it becomes confusing or complex with the various setup steps to run your models. Namely, you face issues with the Lambda size limits and managing the model dependencies within.
Deep Java Library (DJL) is a deep learning framework designed to make your life easier. DJL uses various deep learning backends (such as Apache MXNet, PyTorch, and TensorFlow) for your use case and is easy to set up and integrate within your Java application! Thanks to its excellent dependency management design, DJL makes it extremely simple to create a project that you can deploy on Lambda. DJL helps alleviate some of the problems we mentioned by downloading the prepackaged framework dependencies so you don’t have to package them yourself, and loads your models from a specified location such as Amazon Simple Storage Service (Amazon S3) so you don’t need to figure out how to push your models to Lambda.
This post covers how to get your models running on Lambda with DJL in 5 minutes.
Deep Java Library (DJL) is a Deep Learning Framework written in Java, supporting both training and inference. DJL is built on top of modern deep learning engines (such as TenserFlow, PyTorch, and MXNet). You can easily use DJL to train your model or deploy your favorite models from a variety of engines without any additional conversion. It contains a powerful model zoo design that allows you to manage trained models and load them in a single line. The built-in model zoo currently supports more than 70 pre-trained and ready-to-use models from GluonCV, HuggingFace, TorchHub, and Keras.
You need the following items to proceed:
- An AWS account with access to Lambda
- The AWS Command Line Interface (AWS CLI) installed on your system and configured with your credentials and Region
- A Java environment set up on your system
In this post, we fol
Source - Continue Reading: https://aws.amazon.com/blogs/machine-learning/model-serving-made-easier-with-deep-java-library-and-aws-lambda/