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https://github.com/vanvalenlab/kiosk-console
DeepCell Kiosk Distribution for Kubernetes on GKE and AWS
https://github.com/vanvalenlab/kiosk-console
helm helmfile kubernetes tensorflow-serving
Last synced: about 2 months ago
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DeepCell Kiosk Distribution for Kubernetes on GKE and AWS
- Host: GitHub
- URL: https://github.com/vanvalenlab/kiosk-console
- Owner: vanvalenlab
- License: other
- Created: 2018-07-31T02:24:22.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-07-12T23:38:51.000Z (over 1 year ago)
- Last Synced: 2023-07-13T00:26:32.898Z (over 1 year ago)
- Topics: helm, helmfile, kubernetes, tensorflow-serving
- Language: Shell
- Homepage: https://deepcell-kiosk.readthedocs.io
- Size: 18.7 MB
- Stars: 32
- Watchers: 9
- Forks: 5
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# 
[](https://github.com/vanvalenlab/kiosk-console/actions)
[](https://deepcell-kiosk.readthedocs.io/en/master/?badge=master)
[](https://github.com/vanvalenlab/kiosk-console/blob/master/LICENSE)
The `kiosk-console` is the entry point for users to spin up a DeepCell Kiosk, a cloud-native implementation of the DeepCell ecosystem.
The DeepCell Kiosk is designed to allow researchers to easily deploy and scale a deep learning platform for biological image analysis. Once launched, users can drag-and-drop images to be processed in parallel using publicly available, or custom-built, TensorFlow models. To train custom models, please refer to [deepcell-tf](https://github.com/vanvalenlab/deepcell-tf), which was designed to facilitate model development and is capable of exporting these models for use with the DeepCell Kiosk.
The scalability of the DeepCell Kiosk software is enabled by [Kubernetes](https://kubernetes.io/). At present, the Kiosk is only compatible with [Google Cloud](https://cloud.google.com/).
An example of the DeepCell Kiosk is live at [DeepCell.org](https://deepcell.org).
## Features
- Cloud-based deployment of deep-learning models
- Scalable platform that minimizes cost and inference time
- Drag and drop interface for running predictions
### Examples
Raw Image
Tracked Image
## Getting Started
Start a terminal shell and install the DeepCell Kiosk wrapper script:
```bash
docker run -e DOCKER_TAG=1.8.1 vanvalenlab/kiosk-console:1.8.1 | sudo bash
```
To start the kiosk, just run `kiosk-console` from the terminal shell.
Check out our [docs](https://deepcell-kiosk.readthedocs.io/en/master/GETTING_STARTED.html) for more information on how to start your own kiosk.
## Software Architecture
- [Consumer](https://github.com/vanvalenlab/kiosk-redis-consumer): Retrieves items from the Job Queue and handles the processing pipeline for that item. Each consumer only works on one item at a time.
- [Model Server](https://github.com/vanvalenlab/kiosk-tf-serving): Serves models over a gRPC API, allowing consumers to send data and get back predictions.
- [GPU Autoscaler](https://github.com/vanvalenlab/kiosk-autoscaler): Automatically and efficiently scales Kubernetes GPU resources.
- [Frontend](https://github.com/vanvalenlab/kiosk-frontend): API for creating and managing jobs, and a React-based web interface for the DeepCell Kiosk.
- _Additional Data Entry Tools_:
- [ImageJ Plugin](https://github.com/vanvalenlab/kiosk-imageJ-plugin): An ImageJ 1.x plugin for processing images with an existing cluster.
- [Command-line Interface](https://github.com/vanvalenlab/kiosk-client): A python-based CLI for submitting and managing DeepCell Kiosk jobs.
- _Not pictured above_:
- [Bucket Monitor](https://github.com/vanvalenlab/kiosk-bucket-monitor): Purges the bucket of uploaded and processed files that are older than `AGE_THRESHOLD`, 3 days by default.
- [Janitor](https://github.com/vanvalenlab/kiosk-redis-janitor): Monitors in-progress items and makes sure no jobs get left un-finished.
## Contribute
We welcome contributions to the kiosk. If you are interested, please refer to our [Developer Documentation](https://deepcell-kiosk.readthedocs.io/en/master/DEVELOPER.html), [Code of Conduct](https://github.com/vanvalenlab/kiosk-console/blob/master/CODE_OF_CONDUCT.md) and [Contributing Guidelines](https://github.com/vanvalenlab/kiosk-console/blob/master/CONTRIBUTING.md).
## Support
Issues are managed through [Github](https://github.com/vanvalenlab/kiosk-console/issues).
Documentation is hosted on [Read the Docs](https://deepcell-kiosk.readthedocs.io/en/master).
A [FAQ](http://www.deepcell.org/faq) page is also available.
## License
This software is license under a modified Apache-2.0 license. See [LICENSE](https://github.com/vanvalenlab/kiosk-console/blob/master/LICENSE) for full details.
## Trademarks
All other trademarks referenced herein are the property of their respective owners.
## Credits
[](http://www.vanvalen.caltech.edu/)
This kiosk was developed with [Cloud Posse, LLC](https://cloudposse.com). They can be reached at
## Copyright
Copyright © 2018-2022 [The Van Valen Lab](http://www.vanvalen.caltech.edu/) at the California Institute of Technology (Caltech), with support from the Shurl and Kay Curci Foundation, the Paul Allen Family Foundation, Google, & National Institutes of Health (NIH) under Grant U24CA224309-01.
All rights reserved.