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https://github.com/ucbrise/clipper

A low-latency prediction-serving system
https://github.com/ucbrise/clipper

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A low-latency prediction-serving system

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README 

        
# Clipper



[![Build Status](https://amplab.cs.berkeley.edu/jenkins/buildStatus/icon?job=Clipper)](https://amplab.cs.berkeley.edu/jenkins/job/Clipper/) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)






## 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.*



## Quickstart



**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](http://clipper.ai)**



> This quickstart requires [Docker](https://www.docker.com/) and supports Python 2.7, 3.5, 3.6 and 3.7.



#### Clipper Example Code

* Basic query: https://github.com/ucbrise/clipper/tree/develop/examples/basic_query

* Image query: https://github.com/ucbrise/clipper/tree/develop/examples/image_query



* Examples including metrics: https://github.com/ucbrise/clipper/tree/develop/examples

#### Start a Clipper Instance and Deploy a Model



__Install Clipper__



You can either install Clipper directly from GitHub:

```sh

pip install git+https://github.com/ucbrise/clipper.git@develop#subdirectory=clipper_admin

```

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

```sh

pip install -e /clipper_admin

```



__Start a local Clipper cluster__



First start a Python interpreter session.



```sh

$ 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.



```py

from clipper_admin import ClipperConnection, DockerContainerManager

clipper_conn = ClipperConnection(DockerContainerManager())

clipper_conn.start_clipper()

```



```pycon

17-08-30:15:48:41 INFO     [docker_container_manager.py:95] Starting managed Redis instance in Docker

17-08-30:15:48:43 INFO     [clipper_admin.py:105] Clipper still initializing.

17-08-30:15:48:44 INFO     [clipper_admin.py:107] Clipper is running

```



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



```py

clipper_conn.register_application(name="hello-world", input_type="doubles", default_output="-1.0", slo_micros=100000)

```



```pycon

17-08-30:15:51:42 INFO     [clipper_admin.py:182] Application hello-world was successfully registered

```



Inspect Clipper to see the registered apps



```py

clipper_conn.get_all_apps()

```



```pycon

[u'hello-world']

```



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.



```py

def feature_sum(xs):

    return [str(sum(x)) for x in xs]

```



Import the python deployer package



```py

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.



```py

python_deployer.deploy_python_closure(clipper_conn, name="sum-model", version=1, input_type="doubles", func=feature_sum)

```



```pycon

17-08-30:15:59:56 INFO     [deployer_utils.py:50] Anaconda environment found. Verifying packages.

17-08-30:16:00:04 INFO     [deployer_utils.py:150] Fetching package metadata .........

Solving package specifications: .



17-08-30:16:00:04 INFO     [deployer_utils.py:151]

17-08-30:16:00:04 INFO     [deployer_utils.py:59] Supplied environment details

17-08-30:16:00:04 INFO     [deployer_utils.py:71] Supplied local modules

17-08-30:16:00:04 INFO     [deployer_utils.py:77] Serialized and supplied predict function

17-08-30:16:00:04 INFO     [python.py:127] Python closure saved

17-08-30:16:00:04 INFO     [clipper_admin.py:375] Building model Docker image with model data from /tmp/python_func_serializations/sum-model

17-08-30:16:00:05 INFO     [clipper_admin.py:378] Pushing model Docker image to sum-model:1

17-08-30:16:00:07 INFO     [docker_container_manager.py:204] Found 0 replicas for sum-model:1. Adding 1

17-08-30:16:00:07 INFO     [clipper_admin.py:519] Successfully registered model sum-model:1

17-08-30:16:00:07 INFO     [clipper_admin.py:447] 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     [deployer_utils.py:44] Saving function to /tmp/clipper/tmpx6d_zqeq

18-05-21:12:19:59 INFO     [deployer_utils.py:54] Serialized and supplied predict function

18-05-21:12:19:59 INFO     [python.py:192] Python closure saved

18-05-21:12:19:59 INFO     [python.py:206] Using Python 3.6 base image

18-05-21:12:19:59 INFO     [clipper_admin.py:451] Building model Docker image with model data from /tmp/clipper/tmpx6d_zqeq

18-05-21:12:20:00 INFO     [clipper_admin.py:455] {'stream': 'Step 1/2 : FROM clipper/python36-closure-container:develop'}

18-05-21:12:20:00 INFO     [clipper_admin.py:455] {'stream': '\n'}

18-05-21:12:20:00 INFO     [clipper_admin.py:455] {'stream': ' ---> 1aaddfa3945e\n'}

18-05-21:12:20:00 INFO     [clipper_admin.py:455] {'stream': 'Step 2/2 : COPY /tmp/clipper/tmpx6d_zqeq /model/'}

18-05-21:12:20:00 INFO     [clipper_admin.py:455] {'stream': '\n'}

18-05-21:12:20:00 INFO     [clipper_admin.py:455] {'stream': ' ---> b7c29f531d2e\n'}

18-05-21:12:20:00 INFO     [clipper_admin.py:455] {'aux': {'ID': 'sha256:b7c29f531d2eaf59dd39579dbe512538be398dcb5fdd182db14e4d58770d2055'}}

18-05-21:12:20:00 INFO     [clipper_admin.py:455] {'stream': 'Successfully built b7c29f531d2e\n'}

18-05-21:12:20:00 INFO     [clipper_admin.py:455] {'stream': 'Successfully tagged sum-model:1\n'}

18-05-21:12:20:00 INFO     [clipper_admin.py:457] Pushing model Docker image to sum-model:1

18-05-21:12:20:02 INFO     [docker_container_manager.py:247] 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/python_closure_container.py", line 56, in 

    rpc_service.get_input_type())

  File "/container/python_closure_container.py", line 28, in __init__

    self.predict_func = load_predict_func(predict_path)

  File "/container/python_closure_container.py", line 17, in load_predict_func

    return cloudpickle.load(serialized_func_file)

  File "/usr/local/lib/python3.6/site-packages/cloudpickle/cloudpickle.py", 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"



```py

clipper_conn.link_model_to_app(app_name="hello-world", model_name="sum-model")

```



```pycon

17-08-30:16:08:50 INFO     [clipper_admin.py:224] 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:



```sh

$ curl -X POST --header "Content-Type:application/json" -d '{"input": [1.1, 2.2, 3.3]}' 127.0.0.1:1337/hello-world/predict

```



With Python:



```py

import requests, json, numpy as np

headers = {"Content-type": "application/json"}

requests.post("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.



```py

from clipper_admin import ClipperConnection, DockerContainerManager

clipper_conn = ClipperConnection(DockerContainerManager())

clipper_conn.connect()

```

Stop all Clipper docker containers



```py

clipper_conn.stop_all()

```



```pycon

17-08-30:16:15:38 INFO     [clipper_admin.py:1141] Stopped all Clipper cluster and all model containers

```



## Contributing



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



+ [Dan Crankshaw](https://github.com/dcrankshaw)

+ [Corey Zumar](https://github.com/Corey-Zumar)

+ [Joey Gonzalez](https://github.com/jegonzal)

+ [Alexey Tumanov](https://github.com/atumanov)

+ [Eyal Sela](https://github.com/EyalSel)

+ [Simon Mo](https://github.com/simon-mo)

+ [Rehan Durrani](https://github.com/RehanSD)

+ [Eric Sheng](https://github/com/es1024)



You can contact us at 



## Acknowledgements



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.