Package machine learning models for the web
Easily deploy your machine learning models to an API in the cloud.
- Install and configure awscli
- Install terraform
pip install shipit
- Edit the newly created
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
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.
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.
Getting predictions requires sending a
POST to the relevant model's predict endpoint:
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.
The config file
shipit.yml for your project is broken down into two major sections.
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
: For now this is always assumed to be
aws_profile : Name of the profile from your awscli credentials.
aws_region : Which aws region to launch your service in.
This section can contain one or more models you want to include in this API service. See
example/shipit.yml as a reference.
: The relative path of the pickled model file e.g.
: 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 ).
scikit-learn should be saved with
joblib. Models from
keras should be saved with
- 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
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Hashes for shipit_ml-0.6.0-py3-none-any.whl