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CONTRIBUTING.md

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# How to contribute
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The project currently does not accept pull requests, but we'd be more than happy
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to get your feedback via issues!
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## Contributor License Agreement
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Contributions to this project must be accompanied by a Contributor License
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Agreement (CLA). You (or your employer) retain the copyright to your
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contribution; this simply gives us permission to use and redistribute your
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contributions as part of the project. Head over to
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<https://cla.developers.google.com/> to see your current agreements on file or
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to sign a new one.
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You generally only need to submit a CLA once, so if you've already submitted one
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(even if it was for a different project), you probably don't need to do it
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again.
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## Community Guidelines
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This project follows
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[Google's Open Source Community Guidelines](https://opensource.google/conduct/).

LICENSE

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Apache License
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README.md

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# Open Source Python version of the Vizier API service.
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OSS Vizier is an infrastructural API for blackbox optimization and hyperparameter tuning, based on Google's original internal [Vizier](https://dl.acm.org/doi/10.1145/3097983.3098043) service.
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It allows a user to setup a main Vizier Server, which can host blackbox optimization algorithms to multiple clients simultaneously in a fault-tolerant manner.
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# Table of Contents
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* [Installation](#installation)
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* [Running Vizier](#running-vizier)
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* [Running the Server](#running-the-server)
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* [Running a Client](#running-a-client)
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* [Writing Pythia Policies](#writing-pythia-policies)
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* [Tensorflow-Probability and Keras Modules](#tensorflow-probability-and-keras-modules)
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* [Citing Vizier](#citing-vizier)
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# Installation
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The simplest way is to run the provided `install.sh`. It installs the necessary dependencies, and builds the relevant protobuf libraries needed for the service.
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# Running Vizier
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An example of the entire server + client loop running locally can be found in `vizier/service/vizier_client_test.py`.
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We also present the core components of the example below:
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## Running the Server
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To start the Vizier service, the standard way via GRPC is to do the following on the host machine:
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```
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import grpc
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import portpicker
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# Setup Vizier Service and its data.
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servicer = vizier_server.VizierService()
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# Setup local networking.
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port = portpicker.pick_unused_port()
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address = f'localhost:{port}'
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# Setup server.
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server = grpc.server(futures.ThreadPoolExecutor(max_workers=100), ports=(port,))
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vizier_service_pb2_grpc.add_VizierServiceServicer_to_server(self.servicer, self.server)
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server.add_secure_port(address, grpc.local_server_credentials())
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server.start()
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```
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## Running a client
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Using the `address` created above, we may now create the client (e.g. on a different machine):
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```
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client = vizier_client.VizierClient(
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service_endpoint=address,
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owner_id='my_name',
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study_id='my_study_id')
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```
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The client may now send requests to the server and receive responses, for example:
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```
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client.list_trials() # List out trials for `my_study_id`.
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client.get_trial(trial_id='1') # Get the first trial.
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```
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# Writing Pythia Policies
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Writing blackbox optimization algorithms requires implementing the `Policy` interface, with pseudocode shown below:
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```
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class MyPolicy(Policy):
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def __init__(self, ...):
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self.policy_supporter = PolicySupporter(...) # Used to obtain old trials.
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def suggest(self, request: SuggestRequest) -> List[SuggestDecision]:
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"""Suggests trials to be evaluated."""
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suggestions = []
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for _ in range(request.count):
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old_trials = self.policy_supporter.GetTrials(...)
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trial = make_new_trial(old_trials, request.study_config)
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suggestions.append(base.SuggestDecision(trial))
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return suggestions
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def early_stop(self, request: EarlyStopRequest) -> List[EarlyStopDecision]:
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"""Selects trials to stop from the request."""
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old_trials = self.policy_supporter.GetTrials(...)
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trials_to_stop = determine_trials_to_stop(old_trials, request.trial_ids)
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return [base.EarlyStopDecision(id) for id in trials_to_stop]
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```
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An example is given in `random_policy.py`.
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## Tensorflow-Probability and Keras Modules
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For writing policies involving Bayesian Optimization, we have also provided Tensorflow-Probability (TFP) and Keras utility functions in `vizier/tfp/...` and `vizier/keras/...` respectively.
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# Citing Vizier
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If you found this code useful, please consider citing the [technical report (TBA)]() as well as the [original Vizier paper](https://dl.acm.org/doi/10.1145/3097983.3098043). Thanks!

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