Getting Started

Clipper is a low-latency prediction serving system for machine learning. Clipper makes it simple to integrate machine learning into user-facing serving systems.

The simplest way to start using Clipper is to use the Clipper Admin Python tool to start a local Clipper cluster using Docker. Read the container orchestration guide to learn about other ways to run Clipper, including on Kubernetes.


Check out the links below to find detailed examples:

Install Clipper

Before starting Clipper, you must have a recent version of Docker and Python installed. Clipper currently supports Python 2, 3.5, 3.6, and 3.7. (Future features may not support Python 2.)

We recommend installing Clipper in an Anaconda environment.

Please note that Clipper only provides support for devices running Linux or MacOS.

Version Information

pip install clipper_admin


First start a Python interpreter session.

# Bare Python interpreter
$ python
# iPython shell
$ conda install ipython
$ ipython

From the Python shell, you can start a new Clipper cluster and deploy a simple Python function as your first model.

from clipper_admin import ClipperConnection, DockerContainerManager
clipper_conn = ClipperConnection(DockerContainerManager())

Start Clipper. Running this command for the first time will download several Docker containers, so it may take some time.

18-05-21:12:18:46 INFO     [] Starting managed Redis instance in Docker
18-05-21:12:18:50 INFO     [] Clipper is running

Register an application called “hello-world”. This will create a prediction REST endpoint at http://localhost:1337/hello-world/predict

clipper_conn.register_application(name="hello-world", input_type="doubles", default_output="-1.0", slo_micros=100000)
18-05-21:12:19:02 INFO     [] Application hello-world was successfully registered

Inspect Clipper to see the registered apps


Define a simple model that just returns the sum of each feature vector. Note that the prediction function takes a list of feature vectors as input and returns a list of strings.

def feature_sum(xs):
    return [str(sum(x)) for x in xs]

Import the python deployer package

from clipper_admin.deployers import python as python_deployer

Deploy the “feature_sum” function as a model. Notice that the application and model must have the same input type.

python_deployer.deploy_python_closure(clipper_conn, name="sum-model", version=1, input_type="doubles", func=feature_sum)
18-05-21:12:19:59 INFO     [] Saving function to /tmp/clipper/tmpx6d_zqeq
18-05-21:12:19:59 INFO     [] Serialized and supplied predict function
18-05-21:12:19:59 INFO     [] Python closure saved
18-05-21:12:19:59 INFO     [] Using Python 3.6 base image
18-05-21:12:19:59 INFO     [] Building model Docker image with model data from /tmp/clipper/tmpx6d_zqeq
18-05-21:12:20:00 INFO     [] {'stream': 'Step 1/2 : FROM clipper/python36-closure-container:develop'}
18-05-21:12:20:00 INFO     [] {'stream': '\n'}
18-05-21:12:20:00 INFO     [] {'stream': ' ---> 1aaddfa3945e\n'}
18-05-21:12:20:00 INFO     [] {'stream': 'Step 2/2 : COPY /tmp/clipper/tmpx6d_zqeq /model/'}
18-05-21:12:20:00 INFO     [] {'stream': '\n'}
18-05-21:12:20:00 INFO     [] {'stream': ' ---> b7c29f531d2e\n'}
18-05-21:12:20:00 INFO     [] {'aux': {'ID': 'sha256:b7c29f531d2eaf59dd39579dbe512538be398dcb5fdd182db14e4d58770d2055'}}
18-05-21:12:20:00 INFO     [] {'stream': 'Successfully built b7c29f531d2e\n'}
18-05-21:12:20:00 INFO     [] {'stream': 'Successfully tagged sum-model:1\n'}
18-05-21:12:20:00 INFO     [] Pushing model Docker image to sum-model:1
18-05-21:12:20:02 INFO     [] Found 0 replicas for sum-model:1. Adding 1
18-05-21:12:20:09 INFO     [] Successfully registered model sum-model:1
18-05-21:12:20:09 INFO     [] Done deploying model sum-model:1.

Tell Clipper to route requests for the “hello-world” application to the “sum-model”

clipper_conn.link_model_to_app(app_name="hello-world", model_name="sum-model")
18-05-21:12:20:19 INFO     [] Model sum-model is now linked to application hello-world

Your application is now ready to serve predictions

Query Clipper for predictions

Now that you’ve deployed your first model, you can start requesting predictions with your favorite REST client at the endpoint that Clipper created for your application: http://localhost:1337/hello-world/predict

Directly from the command line with curl:

curl -X POST --header "Content-Type:application/json" -d '{"input": [1.1, 2.2, 3.3]}'

From a Python interpreter:

import requests, json, numpy as np
headers = {"Content-type": "application/json"}"http://localhost:1337/hello-world/predict", headers=headers, data=json.dumps({"input": list(np.random.random(10))})).json()

Clean up

If you closed the Python interpreter session that you used to start Clipper, you will need to start a new Python interpreter session and create another connection to the Clipper cluster. If you still have the interpreter session active from earlier, you can re-use your existing ClipperConnection object.

If you have still have the Python REPL from earlier, skip directly to clipper_conn.stop_all()

from clipper_admin import ClipperConnection, DockerContainerManager
clipper_conn = ClipperConnection(DockerContainerManager())

Stop all Clipper docker containers

17-08-30:16:15:38 INFO     [] Stopped all Clipper cluster and all model containers

Next steps