This project is adapted from Udacity Machine Learning Engineer Nanodegree Program and aim to construct a complete ML project from end to end. Our goal will be to have a simple web page which a user can use to enter a movie review. The web page will then send the review off to our deployed model which will predict the sentiment of the entered review.
The project use sagemaker to build an end-to-end machine learning application, so you need to have an AWS account and create a notebook instance. After activate your notebook instance you can clone this repository and run the cells of the notebook sentiment Analysis - SageMaker.ipynb until deploy the sagemaker inference endpoint. To build the webapp you need to follow the step 7 of the notebook that is also available below which shows how to setting up Lambda function and API Gateway.
So far we have been accessing our model endpoint by constructing a predictor object which uses the endpoint and then just using the predictor object to perform inference. What if we wanted to create a web app which accessed our model? The way things are set up currently makes that not possible since in order to access a SageMaker endpoint the app would first have to authenticate with AWS using an IAM role which included access to SageMaker endpoints. However, there is an easier way! We just need to use some additional AWS services.
The diagram above gives an overview of how the various services will work together. On the far right is the model which we trained above and which is deployed using SageMaker. On the far left is our web app that collects a user's movie review, sends it off and expects a positive or negative sentiment in return.
In the middle is where some of the magic happens. We will construct a Lambda function, which you can think of as a straightforward Python function that can be executed whenever a specified event occurs. We will give this function permission to send and recieve data from a SageMaker endpoint.
Lastly, the method we will use to execute the Lambda function is a new endpoint that we will create using API Gateway. This endpoint will be a url that listens for data to be sent to it. Once it gets some data it will pass that data on to the Lambda function and then return whatever the Lambda function returns. Essentially it will act as an interface that lets our web app communicate with the Lambda function.
The first thing we are going to do is set up a Lambda function. This Lambda function will be executed whenever our public API has data sent to it. When it is executed it will receive the data, perform any sort of processing that is required, send the data (the review) to the SageMaker endpoint we've created and then return the result.
Since we want the Lambda function to call a SageMaker endpoint, we need to make sure that it has permission to do so. To do this, we will construct a role that we can later give the Lambda function.
Using the AWS Console, navigate to the IAM page and click on Roles. Then, click on Create role. Make sure that the AWS service is the type of trusted entity selected and choose Lambda as the service that will use this role, then click Next: Permissions.
In the search box type sagemaker and select the check box next to the AmazonSageMakerFullAccess policy. Then, click on Next: Review.
Lastly, give this role a name. Make sure you use a name that you will remember later on, for example LambdaSageMakerRole. Then, click on Create role.
Now it is time to actually create the Lambda function.
Using the AWS Console, navigate to the AWS Lambda page and click on Create a function. When you get to the next page, make sure that Author from scratch is selected. Now, name your Lambda function, using a name that you will remember later on, for example sentiment_analysis_func. Make sure that the Python 3.6 runtime is selected and then choose the role that you created in the previous part. Then, click on Create Function.
On the next page you will see some information about the Lambda function you've just created. If you scroll down you should see an editor in which you can write the code that will be executed when your Lambda function is triggered. In our example, we will use the code below.
# We need to use the low-level library to interact with SageMaker since the SageMaker API
# is not available natively through Lambda.
import boto3
def lambda_handler(event, context):
# The SageMaker runtime is what allows us to invoke the endpoint that we've created.
runtime = boto3.Session().client('sagemaker-runtime')
# Now we use the SageMaker runtime to invoke our endpoint, sending the review we were given
response = runtime.invoke_endpoint(EndpointName = '**ENDPOINT NAME HERE**', # The name of the endpoint
ContentType = 'text/plain', # The data format expected
Body = event['body']) # The actual review
# The response is an HTTP response whose body contains the result of our inference
result = response['Body'].read().decode('utf-8')
return {
'statusCode' : 200,
'headers' : { 'Content-Type' : 'text/plain', 'Access-Control-Allow-Origin' : '*' },
'body' : result
}Once you have copy and pasted the code above into the Lambda code editor, replace the **ENDPOINT NAME HERE** portion with the name of the endpoint that we deployed earlier. You can determine the name of the endpoint using the code cell below.
Once you have added the endpoint name to the Lambda function, click on Save. Your Lambda function is now up and running. Next we need to create a way for our web app to execute the Lambda function.
Now that our Lambda function is set up, it is time to create a new API using API Gateway that will trigger the Lambda function we have just created.
Using AWS Console, navigate to Amazon API Gateway and then click on Get started.
On the next page, make sure that New API is selected and give the new api a name, for example, sentiment_analysis_api. Then, click on Create API.
Now we have created an API, however it doesn't currently do anything. What we want it to do is to trigger the Lambda function that we created earlier.
Select the Actions dropdown menu and click Create Method. A new blank method will be created, select its dropdown menu and select POST, then click on the check mark beside it.
For the integration point, make sure that Lambda Function is selected and click on the Use Lambda Proxy integration. This option makes sure that the data that is sent to the API is then sent directly to the Lambda function with no processing. It also means that the return value must be a proper response object as it will also not be processed by API Gateway.
Type the name of the Lambda function you created earlier into the Lambda Function text entry box and then click on Save. Click on OK in the pop-up box that then appears, giving permission to API Gateway to invoke the Lambda function you created.
The last step in creating the API Gateway is to select the Actions dropdown and click on Deploy API. You will need to create a new Deployment stage and name it anything you like, for example prod.
You have now successfully set up a public API to access your SageMaker model. Make sure to copy or write down the URL provided to invoke your newly created public API as this will be needed in the next step. This URL can be found at the top of the page, highlighted in blue next to the text Invoke URL.
Now that we have a publicly available API, we can start using it in a web app. For our purposes, we have provided a simple static html file which can make use of the public api you created earlier.
In the website folder has a file called index.html. Download the file to your computer and open that file up in a text editor of your choice. There should be a line which contains **REPLACE WITH PUBLIC API URL**. Replace this string with the url that you wrote down in the last step and then save the file.
Now, if you open index.html on your local computer, your browser will behave as a local web server and you can use the provided site to interact with your SageMaker model.
If you'd like to go further, you can host this html file anywhere you'd like, for example using github or hosting a static site on Amazon's S3. Once you have done this you can share the link with anyone you'd like and have them play with it too!
Important Note In order for the web app to communicate with the SageMaker endpoint, the endpoint has to actually be deployed and running. This means that you are paying for it. Make sure that the endpoint is running when you want to use the web app but that you shut it down when you don't need it, otherwise you will end up with a surprisingly large AWS bill.
Now that your web app is working, trying playing around with it and see how well it works.
Give an example of a review that you entered into your web app. What was the predicted sentiment of your example review?
