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Last pushed: 6 days ago
Short Description
Python model container for testing and demonstration purposes. It can be used with any model type.
Full Description


<img src="images/clipper-logo.png" width="200">

What is Clipper?

Clipper is a prediction serving system that sits between user-facing applications and a wide range of commonly used machine learning models and frameworks. Learn more about Clipper and view documentation at our website

What does Clipper do?

  • Clipper simplifies integration of machine learning techniques into user facing applications by providing a simple standard REST interface for prediction and feedback across a wide range of commonly used machine learning frameworks. Clipper makes product teams happy.
  • Clipper simplifies model deployment and helps reduce common bugs by using the same tools and libraries used in model development to render live predictions. Clipper makes data scientists happy.
  • Clipper improves throughput and ensures reliable millisecond latencies by introducing adaptive batching, caching, and straggler mitigation techniques. Clipper makes the infra-team less unhappy.

  • Clipper improves prediction accuracy by introducing state-of-the-art bandit and ensemble methods to intelligently select and combine predictions and achieve real-time personalization across machine learning frameworks. Clipper makes users happy.


Note: This quickstart works for the latest version of code. For a quickstart that works with the released version of Clipper available on PyPi, go to our website

This quickstart requires Docker and only supports Python2.

Start a Clipper Instance and Deploy a Model

Install Clipper

You can either install Clipper directly from GitHub:

pip install git+

or by cloning Clipper and installing directly from the file system:

pip install -e </path/to/clipper_repo>/clipper_admin

Start a local Clipper cluster

First start a Python interpreter session.

$ python

# Or start one with iPython
$ conda install ipython
$ ipython
>>> 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.
>>> clipper_conn.start_clipper()
17-08-30:15:48:41 INFO     [] Starting managed Redis instance in Docker
17-08-30:15:48:43 INFO     [] Clipper still initializing.
17-08-30:15:48:44 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)
17-08-30:15:51:42 INFO     [] Application hello-world was successfully registered

# Inspect Clipper to see the registered apps
>>> clipper_conn.get_all_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)
17-08-30:15:59:56 INFO     [] Anaconda environment found. Verifying packages.
17-08-30:16:00:04 INFO     [] Fetching package metadata .........
Solving package specifications: .

17-08-30:16:00:04 INFO     []
17-08-30:16:00:04 INFO     [] Supplied environment details
17-08-30:16:00:04 INFO     [] Supplied local modules
17-08-30:16:00:04 INFO     [] Serialized and supplied predict function
17-08-30:16:00:04 INFO     [] Python closure saved
17-08-30:16:00:04 INFO     [] Building model Docker image with model data from /tmp/python_func_serializations/sum-model
17-08-30:16:00:05 INFO     [] Pushing model Docker image to sum-model:1
17-08-30:16:00:07 INFO     [] Found 0 replicas for sum-model:1. Adding 1
17-08-30:16:00:07 INFO     [] Successfully registered model sum-model:1
17-08-30:16:00:07 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")
17-08-30:16:08:50 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 at the REST endpoint that clipper created for your application: http://localhost:1337/hello-world/predict

With cURL:

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

From a Python REPL:

>>> 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 REPL you were using to start Clipper, you will need to start a new Python REPL and create another connection to the Clipper cluster. If you still have the Python REPL 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())
>>> clipper_conn.connect()

# Stop all Clipper docker containers
>>> clipper_conn.stop_all()
17-08-30:16:15:38 INFO     [] Stopped all Clipper cluster and all model containers


To file a bug or request a feature, please file a GitHub issue. Pull requests are welcome. Additional help and instructions for contributors can be found on our website at

The Team

GitHub usernames are in parentheses.

  • Dan Crankshaw (dcrankshaw)
  • Corey Zumar (Corey-Zumar)
  • Joey Gonzalez (jegonzal)
  • Nishad Singh (nishadsingh1)
  • Alexey Tumanov (atumanov)
  • Feynman Liang (feynmanliang)

You can contact us at


This research is supported in part by DHS Award HSHQDC-16-3-00083, DOE Award SN10040 DE-SC0012463, NSF CISE Expeditions Award CCF-1139158, and gifts from Ant Financial, Amazon Web Services, CapitalOne, Ericsson, GE, Google, Huawei, Intel, IBM, Microsoft and VMware.

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