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, which was designed to facilitate model development and is capable of exporting these models for use with the DeepCell Kiosk.
An example of the DeepCell Kiosk is live at DeepCell.org.
Cloud-based deployment of deep-learning models
Scalable platform that minimizes cost and inference time
Drag and drop interface for running predictions
|Raw Image||Tracked Image|
Start a terminal shell and install the DeepCell Kiosk wrapper script:
docker run -e DOCKER_TAG=1.5.0 vanvalenlab/kiosk-console:1.5.0 | sudo bash
To start the kiosk, just run
kiosk-console from the terminal shell.
Check out our docs for more information on how to start your own kiosk.
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: Serves models over a gRPC API, allowing consumers to send data and get back predictions.
GPU Autoscaler: Automatically and efficiently scales Kubernetes GPU resources.
Frontend: API for creating and managing jobs, and a React-based web interface for the DeepCell Kiosk.
Additional Data Entry Tools:
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This software is license under a modified Apache-2.0 license. See LICENSE for full details.
All other trademarks referenced herein are the property of their respective owners.
Copyright © 2018-2021 The Van Valen Lab 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.