Deploying Tensorflow on AWS Lambda serverless service

A few weeks ago I was trying to write a webapp for machine learning. It turns out that the tensorflow library is way too big for the deployment on AWS Lambda. After many hours of googling, I found that we have to reduce the size of the library. There are certainly several options to accomplish this task. Here I focus on the following technologies:

1. AWS Lambda
2. python libraries: numpy and tensorflow
3. python library for the webapp: flask
4. zappa: a tool to deploy flask webapp onto AWS Lambda.

I use this combination simply because I am more comfortable to do everything in python than relying on node.js etc for the web.

Deployment

Everything AWS here.

For large files, we can use Amazon S3 for the hosting and let the Lambda function to request it.

We first create a s3 bucket:

aws s3 mb s3://ml-bucket-3

Here ml-bucket-3 is the bucket name. We should also include this name in our config.json below such that our script can get back our files on s3.

Virtual environment

As in any flask webapp, we need to create a virtual environment. I used virtualenv to create it. After activating the new python environment (activate), we need to install the required packages:

pip install flask uuid zappa boto3 numpy tensorflow

The webapp folder is now 676 MB, which is way about the limit. Some of the unimportant stuff need to be removed:

find . -name "tests" | xargs rm -rf
find . -name "*.pyc" -delete
find . -name "*.so" | xargs strip

We use zappa to deploy the package on the Amazon Lambda. We first run zappa init to initialize the project. Here are our sample configuration file:

{
    "dev": {
        "app_function": "webapp.app",
        "aws_region": "us-east-1",
        "profile_name": "default",
        "project_name": "webapp",
        "runtime": "python3.6",
        "s3_bucket": "YOUR-ZAPPA-BUCKET-NAME",
	    "slim_handler": true,
	    "use_precompiled_packages": false,
        "memory_size": 1024
    }
}

Remember to fill in the bucket name you created in the previous step. In addition to the default options, we also added the following: slim_handler, use_precompiled_packages, and memory_size.

And…this is how I deployed my webapp for text analytics (GitHub Repo )

• 2019 2