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Note: Clipper is not actively maintained currently. It is available as a research artifact.

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 supports Python 2.7, 3.5, 3.6 and 3.7.

Clipper Example Code

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

Create a ClipperConnection object and start Clipper. Running this command for the first time will download several Docker containers, so it may take some time.

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


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.

Possible Error If start_clipper() is stuck at this logs, try pip install -U cloudpickle==0.5.3

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

It is because of cloudpickle dependency version issue. You may see this error logs from model container docker log.

$ docker logs 439ba722d79a # model container logs. For this example, it will be simple-example model container
Starting Python Closure container
Connecting to Clipper with default port: 7000
Traceback (most recent call last):
  File "/container/", line 56, in <module>
  File "/container/", line 28, in __init__
    self.predict_func = load_predict_func(predict_path)
  File "/container/", line 17, in load_predict_func
    return cloudpickle.load(serialized_func_file)
  File "/usr/local/lib/python3.6/site-packages/cloudpickle/", line 1060, in _make_skel_func
    base_globals['__builtins__'] = __builtins__
TypeError: 'str' object does not support item assignment

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]}'

With Python:

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.

Create a new connection. If you have still have the Python REPL from earlier, you can skip this step.

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


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

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.