A simple framework for distributed hyperparameter optimization in Docker.
What is Hyperdock?
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Hyperdock is a framework for hyperparameter search that can be used to optimized any target as long as that target can be run in a Docker container. That means that the target can be written in any language, use any framework or run on any operating system as long as it can be made into a Docker image.
The figure below shows the Hyperdock system and its main components.
The Hyperdock Web UI is the main interface for the end-users from where they can specify trials (a target Docker image and the hyperparameter space to search over). All trials, their status and results are stored in a Mongo database.
The Hyperdock Supervisor is a background process that monitors all trials. It determines what jobs (a specific hyperparameter combination) need to scheduled, which jobs have failed and should be restarted, and notifies users of results.
The Hyperdock Workers dequeues jobs from the work queue and then evaluates the target image with these parameters. They continually send status updates to the database to notify the user of progress.
The entire Hyperdock system can be distributed, i.e. Supervisor, Workers and WebUI need not run on the same host. They only need to be able to access the same Mongo database and the workers need to have access to any data required by the target image.
Each program that should be optimized needs to have its own Docker image, the target image, that is setup to load the parameters and write progress reports. Parameters are available in the json file /hyperdock/params.json. Once the target image has evaluated the parameters it simply writes the loss to the file /hyperdock/loss.json with the option of storing important files to /hyperdock/out. Logs from the target image are periodically tailed from the workers to the WebUI. More about how to write a target image can found below.
How does Hyperdock work?
Hyperdock supports grid search of parameters from lists and distributions. See the wiki for details on how to define the parameters space.
Setting up Hyperdock
You can either use the pre-built Docker images for Hyperdock or run the sub-systems directly on the host(s). Finally you can also use the Docker compose file to setup a single host Hyperdock environment useful for testing - this method is very quick way to get started.
Supervisor
To start the Hyperdock Supervisor using the Docker image run the following command:
Doyourdata appuninser professional 5 2 players. Or run it on your host with Python >= 3.6 and install with pip:
Options
--mongo mongodb://localhost:27017/hyperdockURL to the Mongo database
For full arguments to the supervisor run: hyperdock-supervisor --help.
Worker
To start the Hyperdock Worker using the Docker image run the following command:
Or run it on your host with Python >= 3.6 and install with pip:
Options
-v $(pwd):$(pwd)mirrors the path structure from the host in to the Docker container. This is needed since the paths must be the the same when the worker starts the Target Image and mounts the data and results folders.-v /var/run/docker.sock:/var/run/docker.sockgives the Docker image access to control the outer Docker daemon. This is crucial for worker to start new containers
Or run it on your host with Python 3.6 and install with pip:
For full arguments to the worker run: hyperdock-worker --help.
Note: That since the Hyperdock Worker needs to control Docker and access files on the host computer.
WebUI
To start the Hyperdock WebUI using the Docker image run the following command:
Options
-e MONGO_URL=mongodb://hyperdock-mongo:27017/hyperdocksets the Mongo database-p 3000:3000publish the Web UI's http port
Or run it on your host with Meteor:
Target Image
Cv template. Each optimization target needs a target image. War ops game. This image can be dynamic (i.e. checkout the latest source from Github) but preferably should be reproducible, for example by always checking out a specific commit.
When running the container the target should:
Options
--mongo mongodb://localhost:27017/hyperdockURL to the Mongo database
For full arguments to the supervisor run: hyperdock-supervisor --help.
Worker
To start the Hyperdock Worker using the Docker image run the following command:
Or run it on your host with Python >= 3.6 and install with pip:
Options
-v $(pwd):$(pwd)mirrors the path structure from the host in to the Docker container. This is needed since the paths must be the the same when the worker starts the Target Image and mounts the data and results folders.-v /var/run/docker.sock:/var/run/docker.sockgives the Docker image access to control the outer Docker daemon. This is crucial for worker to start new containers
Or run it on your host with Python 3.6 and install with pip:
For full arguments to the worker run: hyperdock-worker --help.
Note: That since the Hyperdock Worker needs to control Docker and access files on the host computer.
WebUI
To start the Hyperdock WebUI using the Docker image run the following command:
Options
-e MONGO_URL=mongodb://hyperdock-mongo:27017/hyperdocksets the Mongo database-p 3000:3000publish the Web UI's http port
Or run it on your host with Meteor:
Target Image
Cv template. Each optimization target needs a target image. War ops game. This image can be dynamic (i.e. checkout the latest source from Github) but preferably should be reproducible, for example by always checking out a specific commit.
When running the container the target should:
- Read the parameters
- Evaluate the target program
- Write the loss / results and then exit (with error code 0).
Communication between Hyperdock and the target program is handle through a few special files and folders that are mounted and populated by Hyperdock.
/hyperdock/loss.jsonwrite the loss here; format described hereparams.jsonparameters for the rungraphs.jsonoptional file, contains graphs for Web UI plots; format described hereout/persistent folder, use this to write any other files to the result folder
/dataa read only folder that contains any external data needed
Hyperdock 1.8
See the Dockfile template for an example. It is available as a Docker image namederikgartner/hyperdock-demo:latest. By default it outputs 0 as its loss but by setting the environmentvariable FUNCTION to a python expression (for example a + b) you can compute an arbitrary loss based on the Hyperdock parameters.
Mongo Database
To start a Mongo database you can use this simple Docker command or use any normal Mongo instance.
Docker Compose
To setup Hyperdock on a single host the Docker compose file is a very easy way to get started. Just set the marked line in docker-compose.yml to a host directory that should contain data and results. Then simply run:
Developing
Hyperdock welcomes new contributors and pull-requests, but please start by reading the contribution guidelines. If you don't know where to start, sending a message to contributors is a good start!
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Hyperdock uses Pipenv to manage the Python version and the package dependencies. The WebUI is built using Meteor which needs to be installed prior to development.
Hyperdock uses Travis for test monitoring, continuous integration and continuous deployment.
Testing
Hyperdock uses nose as the test runner for the Python package. Note that the test machine needs a working Docker installation that doesn't require sudo. Always run the tests locally before pushing.
For the WebUI run:
License
Extensis portfolio 2 0 1 download free. Copyright 2018-2019 Erik Gärtner
Licensed under the Apache License, Version 2.0 (the 'License');you may not use this file except in compliance with the License.You may obtain a copy of the License at
Citing
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