shipit-ml 0.6.0

Package machine learning models for the web

ShipIt

Easily deploy your machine learning models to an API in the cloud.

Quickstart

1. Install and configure awscli
2. Install terraform
3. pip install shipit
4. shipit init
5. Edit the newly created shipit.yml See configuration
6. shipit deploy

You will get an output that tells you the version and what the endpoint is.

Project             : demo
Endpoint            : production-alb-demo-553835794.us-west-2.elb.amazonaws.com
Version             : 2

How it Works

Shipit wraps tools like docker, awscli, and terraform to make it easy to deploy production ready web APIs for your machine learning models. First a docker image is built based on the configurat

Running Locally

You can spin up your container locally like this:

shipit build -t yourtagname
docker run -p 5000:80 -it yourtagname

You now have your models being served from a web API. Visit localhost:5000/ to see the list of available models.

Commands

shipit deploy -t [yourtag] --verbosity 1 : Build and deploy the shipit project. All arguments are optional.

shipit destroy : Use terraform to destroy

shipit build -t [yourtag] --verbosity 1 : Build the docker image and tag it. All arguments are optional.

Usage

Getting predictions requires sending a POST to the relevant model's predict endpoint:

http://[your-endpoint]/predict/[model-name]

The payload should be a JSON serialized array or 2d array (for multiple predictions) to the provided model's endpoint. For example, a model that takes three features would look like this:

[33, 4, 10]

In the case of doing multiple predictions, pass that in as a 2d array.

[
    [33, 4, 10],
    [32, 1, 5]
]

Here's an example using cURL.

curl -d '[[5, 1, 6], [1, 2, 3]]' -H "Content-Type: application/json" -X POST http://[your-endpoint]:5000/predict/[modelname]

The response will always be a 2d array, so if you send one data point expect a list back with only one row.

Configuration

The config file shipit.yml for your project is broken down into two major sections.

meta

project_name : A unique project name, used to namespace the resources created for your project.

requirements : Path to a requirements.txt file to install dependencies for your models

provider : For now this is always assumed to be aws

aws_profile : Name of the profile from your awscli credentials.

aws_region : Which aws region to launch your service in.

models

This section can contain one or more models you want to include in this API service. See example/shipit.yml as a reference.

path : The relative path of the pickled model file e.g. models/my_model.pkl

variety : One of ["sklearn", "keras"]. Eventually we will add more model types.

preprocess : (optional) A python import dot path to a preprocess function. This function can perform manipulations of the API input before sending it to your model.

postprocess : (optional) A python import dot path to a postprocess function. This function can perform manipulations of the model's prediction output before returning it to the user.

Formatting model data

First, ensure your features is a (n_samples, n_features)-shaped numpy array ( in standard sklearn form ). Turn this into a list ( so that we can JSON-serialise it ).

Saving models

Models from scikit-learn should be saved with joblib. Models from keras should be saved with model.save(). See example/save_model_example.py.

To Do

• Deploy to Private VPN
• Route53 / private / public DNS
• Build an "export" feature for customization of Docker / terraform setup.
• Support XGBoost models
• Figure out why sklearn.linear_model.LinearRegression can't be pickled