Deploy Custom Deep Learning Algorithm in a Docker Container on Amazon Sagemaker using Personalized Developed Methods
In a recent development, a custom deep learning container algorithm has been successfully deployed on Amazon Sagemaker. The container, which implements the Titanic predictions for a web app, is packaged using Docker and tested locally with a comprehensive framework.
The Local Testing Framework
The local testing framework includes three shell scripts: , , and . To locally test the container, follow these steps:
- Build the Docker image using the command .
- Train the model by running and . The Titanic-train data should be placed in the directory.
- Start the server by running . This command starts a Python application that listens on port 8080, and starts nginx and gunicorn.
- To get the model inference, run . The Titanic test file needs to be placed in the directory and renamed to for local testing. takes a payload file as input and sends a request to the server for model inference.
The Dockerfile
The Dockerfile for the Titanic use case installs libraries such as Tensorflow, Scikit-learn, Keras, etc. Changes need to be made in the Dockerfile to specify the required libraries for the entire process of training and inference. The working directory set in the Dockerfile will be .
The Code and Deployment
The files required to deploy and host the container can be found in the author's GitHub repository. A script named is used to build the container images and push them to ECR. The code for building and pushing the docker image to ECR can be found in the file or the Bring_Your_Own-Creating_Algorithm_and_Model_Package notebook.
After building the docker image, it can be pushed to Amazon ECR using a Sagemaker Notebook instance. The AWS account used to deploy the Docker container image of a custom deep learning algorithm on Amazon SageMaker is identified by the Amazon Elastic Container Registry (ECR) account ID part of the image URI, typically in the format as shown in examples like .
Deploying on Sagemaker
Sagemaker offers two options: using built-in algorithms or a custom docker container from ECR. After pushing the container image to ECR, the model can be trained, tested, and deployed on Amazon Sagemaker.
The Titanic Predictor
The Titanic predictions are implemented in the file, which also implements the Flask web server. Containers are isolated from each other and from the host environment, making them ideal for deploying machine learning models. Running a container is like running a program on a machine, but it creates a fully self-contained environment for the program to run.
Changes need to be made in the file, particularly in the function, for preprocessing on the Titanic dataset. Similarly, changes need to be made in the file to perform the same preprocessing steps before taking the inference from the trained model.
The file is a small wrapper used to invoke the Flask app, while the file is the configuration file for the nginx front-end. The script launches the gunicorn server when the container is started.
In conclusion, this deployment of a custom deep learning container algorithm on Amazon Sagemaker demonstrates the feasibility and efficiency of using Docker for packaging and deploying machine learning models. The complete code and instructions can be found in the author's GitHub repository.
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