I get an error message saying: 'Could not load HyperDock preference pane'!
I'm not quite sure yet why this happens, but installing the current developer version seems to fix it.
HyperDock shows 'Trial Expired' error messages even though I bought HyperDock in the Mac App Store.
This is a bug in certain older release versions that occurs if Mac App Store and Homepage versions are installed in parallel. To fix this, upgrade to HyperDock 1.7 manually.
What's the difference between the Mac App Store and the homepage version?
Due to some restrictions, the App Store version has a slightly reduced feature set:
- The homepage version lives in your system preferences, the App Store version is an app.
- The homepage version gives you faster updates and access to developer versions.
- The shortcuts functionality isn't ported to the App Store version (yet).
I lost my license file!
Try the license recovery tool
HyperDock gives me errors on license activation / when uninstalling!
This is most often caused by incorrect permissions. Try to completely remove HyperDock with this script: hyperdock_uninstall.zip, and install HyperDock from scratch.
May I use the license on all my Macs?
You may use your license on all your personal accounts on any Mac.
HyperDock doesn't work for me since I upgraded to Mac OS X Lion!
Make sure you use the latest version. If that doesn't fix it, try to reset your configuration (HyperDock preferences -> About -> Restore Defaults).
Could you add feature X?
Maybe, I’m always open to new ideas. Simply drop me a message and I see what I can do.
You do not answer my mail!
You should usually receive an answer within 24 hours. If not, try to resend it with a NOSPAM prefix in the subject.
HyperDock is repeatedly asking me to enable 'Access for assistive devices'.
Follow this tutorial to authorize HyperDock to use the Accessibility-API.
Will you include Safari/Chrome/Firefox tab previews?
They are on my mid-term long-term todo list. 'Safari' preferences, Safari 5.1 is not yet supported) -->
What information is contained in the crash reports?
The crash reports contain the same information that apple uses for their crash reports, i.e. thread states, used libraries, register contents..
HyperDock uses PLCrashReporter for its crash reports. You can find information on their homepage on how to decrypt these. The reason for them not being human readable is that HyperDock strips 'debugging symbols' from the binaries to achieve a smaller package size.
What about Mac OS 10.4., 10.5?
Sorry, I internally use much code that is 10.6+ only.
Will you add a volume control slider to the iTunes preview?
Probably. But you can also adjust volume by scrolling on the iTunes preview OR the iTunes dock icon itself!
After upgrading to lion, a popup window appears asking me to enter my password when I launch Safari!
I'm currently inspecting this issue. A workaround is to restore the default configuration (HyperDock preferences -> About -> Restore Defaults).
HyperDock for Mac lets you take a quick look at the windows you have open in each program when you hover over icons in the Dock. See thumbnails of both open and minimized windows in the same place, and quickly access the programs you need with one click through this app's intuitive and convenient interface.
When you install HyperDock, it will run from the System Preferences menu. To customize your user experience, you can set Preferences for Enabling Window Previews and Activation Delay Time in milliseconds. It's also possible to select whether or not you want to include windows from all display spaces in the thumbnail previews that pop up, and to turn on or off the inclusion of minimized windows. Windows previews can appear in order of creation time or of most recent usage, and you can select or deselect certain Dock items if you don't want them to show previews. In addition to the main features of this app, there are also some additional touches, such as the ability to create and manage keyboard shortcuts for the app's controls, and the option to have windows snap automatically when you drag them to the edges of the screen.
HyperDock for Mac is a powerful and convenient program, with a lot of great features. It runs smoothly, and it's free to try for 15 days to see if it's something you'd like to use on a regular basis. If you do want to keep the program, you'll have to pay $9.95 for a full license, which seems a little high, even though the app does work quite well.
What do you need to know about free software?
Newer version available (0.18.3)Last released:
A simple program for distributed hyperparameter optimization in Docker
Project description
![Hyperdock logo](extra/banner.png)
[![PyPI](https://img.shields.io/pypi/v/hyperdock.svg?style=for-the-badge)](https://pypi.org/project/hyperdock/)
[![Docker Pulls](https://img.shields.io/docker/pulls/erikgartner/hyperdock-supervisor.svg?style=for-the-badge)](https://hub.docker.com/r/erikgartner/hyperdock-supervisor/)
[![Travis](https://img.shields.io/travis/ErikGartner/hyperdock.svg?style=for-the-badge)](https://travis-ci.org/ErikGartner/hyperdock)
[![Codecov](https://img.shields.io/codecov/c/github/ErikGartner/hyperdock.svg?style=for-the-badge)](https://codecov.io/gh/ErikGartner/hyperdock)
*A simple framework for distributed hyperparameter optimization in Docker.*
## Philosophy
The main idea of Hyperdock is universal hyperparameter optimization that can optimize a target written in any language or using any framework as long as it can be deployed in a Docker image.
![Hyperdock diagram](extra/diagram.png)
From the **Hyperdock WebUI** the user can add **trials** that specify the **target image** and the hyperparameter space.
The **Hyperdock Supervisor** then reads the user-defined trial from the Mongo database and creates sets of parameter combination to be evaluated.
These **jobs** are added to the **work queue**.
The **Hyperdock Workers** dequeues jobs from the work queue and then evaluates the **target image** with these parameters. During this the Hyperdock Supervisor monitors the progress of the jobs and the status of the workers.
The Hyperdock 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.
The **target image** receives the parameters from the json file `/hyperdock/params.json`. Once the target image has evaluated the parameters it simply writes the loss to 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.
## Running
Setting up the Hyperdock system can seem a bit complicated but once it is up it quite easy to use.
You can either use the built Docker images for Hyperdock or run the parts directly on the host.
#### Supervisor
To start the Hyperdock Supervisor using the Docker image run the following command:
```bash
docker run -it --rm --name hyperdock-supervisor --link hyperdock-mongo
erikgartner/hyperdock-supervisor:latest
--mongodb mongodb://hyperdock-mongo:27017/hyperdock
```
Or run it on your host with Python 3.6 and install with pip:
```bash
pip install hyperdock
hyperdock-supervisor --mongodb mongodb://localhost:27017/hyperdock
```
##### Options:
- `--mongo mongodb://localhost:27017/hyperdock` URL 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:
```bash
docker run -it --rm -v /var/run/docker.sock:/var/run/docker.sock
--link hyperdock-mongo
-v $(pwd):$(pwd)
erikgartner/hyperdock-worker:latest
--mongodb mongodb://hyperdock-mongo:27017/hyperdock
```
##### 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.sock` gives 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:
```bash
pip install hyperdock
hyperdock-worker --mongodb mongodb://localhost:27017/hyperdock
```
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 it might be easier to run it directly on the host.
#### WebUI
To start the Hyperdock WebUI using the Docker image run the following command:
```bash
docker run -it --rm --name hyperdock-webui --link hyperdock-mongo
-e ROOT_URL=http://localhost:3000/
-e MONGO_URL=mongodb://hyperdock-mongo:27017/hyperdock
-p 3000:3000
erikgartner/hyperdock-webui:latest
```
##### Options:
- `-e MONGO_URL=mongodb://hyperdock-mongo:27017/hyperdock` sets the Mongo database
- `-p 3000:3000` publish the port to the host
Or run it on your host with Meteor:
```bash
# Setup Meteor
curl https://install.meteor.com/ | sh # Install Meteor
cd web/
meteor npm install
# Start WebUI
export MONGO_URL=mongodb://localhost:27017/hyperdock
meteor run
```
#### Target Image
For the **Target Image** the following volumes are mounted:
- `/hyperdock/`
- `loss.json` write the final loss here
- `params.json` contains the parameters for this run
- `out/` use this to write any other files to the result folder
- `/data` a read only folder that contains any external data needed
See the [Dockfile template](docker/Dockerfile.template) for an example.
#### Mongo database
To start a Mongo database you can use this simple Docker command or use any normal Mongo instance.
```bash
# Starts mongo db, add --bind_ip_all to listen on all interfaces.
docker run --name hyperdock-mongo -p 27017:27017 -d mongo
```
## Building
If you want to develop Hyperdock it is recommended that you use [Pipenv](https://docs.pipenv.org/) to manage the Python version the package dependencies.
The WebUI is built using [Meteor](https://www.meteor.com/) which needs to be installed prior to development.
## License
Copyright 2018 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
http://www.apache.org/licenses/LICENSE-2.0
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A simple framework for distributed hyperparameter optimization in Docker.
What is Hyperdock?
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 currently only implements grid search of the parameter space but future release may contain other methods such as sampling from distributions over the parameters.
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
Pelajaran sbk kelas 2 semester 2. To start the Hyperdock Supervisor using the Docker image run the following command:
Or run it on your host with Python >= 3.6 and install with pip:
Options
--mongo mongodb://localhost:27017/hyperdock
URL 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.sock
gives 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:
![Hyperdock License File Download Hyperdock License File Download](https://forums.macrumors.com/data/attachments/407/407925-9d6dcb07da8e744369d7bdbfa2efdbfd.jpg)
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/hyperdock
sets the Mongo database-p 3000:3000
publish the Web UI's http port
Or run it on your host with Meteor:
Target Image
Each optimization target needs a target image. 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.json
write the loss here; format described hereparams.json
parameters for the rungraphs.json
optional file, contains graphs for Web UI plots; format described hereout/
persistent folder, use this to write any other files to the result folder
/data
a read only folder that contains any external data needed
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.
![Hyperdock License File Download Hyperdock License File Download](https://s3-eu-west-1.amazonaws.com/eurekar-v2/uploads/images/small/focfrontact_0.jpg)
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!
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
Copyright 2016-2019 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