Kiso helps researchers run and reproduce experiments across edge, cloud, and testbed environments. Define your experiments declaratively, and let Kiso handle the infrastructure complexity.
# Install Kiso
$ pip install kiso[all]
# Define your experiment in YAML
$ vim experiment.yml
# Provision resources across multiple testbeds
$ kiso up
# Run across multiple testbeds
$ kiso run
# Deprovision resources
$ kiso down
Instead of writing custom scripts to provision resources, install software, and manage execution, Kiso lets you describe your experiments declaratively using simple configuration files.
Kiso handles resource provisioning, software setup, experiment execution, and result collection—allowing you to focus on designing and evaluating your experiments rather than managing infrastructure.
Kiso manages every stage of your experiment with YAML-based configuration—no custom orchestration code required
Declaratively provision computing resources across one or more supported testbeds. Kiso handles authentication, allocation, and configuration across diverse infrastructure providers.
Automatically install and configure software stacks, workload management systems, and execution environments. Deploy containers, workflow engines, agent runtimes, and custom dependencies.
Run experiments in a controlled, repeatable manner across distributed infrastructure. Automatically collect results from all resources back to a central location for analysis.
Everything you need to run reliable, reproducible experiments across distributed environments
YAML-based experiment specifications capture what should run, where, and how—ensuring complete reproducibility without custom code.
Seamlessly run experiments across edge, and testbed environments through a unified interface. Support for major research testbeds providers.
No more writing and maintaining custom orchestration code. Define everything in YAML and let Kiso handle the complexity.
Plugin architecture supports custom testbeds, software installers, and experiment orchestrators to fit your specific needs.
Robust error handling and automated resource cleanup ensure your experiments run smoothly and don't leave resources stranded.
Automatically collect experiment results from all distributed resources to a central location for easy analysis and sharing.
Real-world research experiments powered by Kiso
Plankton are effective indicators of environmental change and ecosystem health in freshwater habitats, but collection of plankton data using manual microscopic methods is extremely labor-intensive and expensive. Automated plankton imaging offers a promising way forward to monitor plankton communities with high frequency and accuracy in real-time. Yet, manual annotation of millions of images proposes a serious challenge to taxonomists. Deep learning classifiers have been successfully applied here to categorize marine plankton images.
View on GitHubThis workflow is based on an open-source software and hardware project that trains itself using the audio files generated via the hydrophone sensors deployed in three locations in the state of Washington (San Juan Island, Point Bush, and Port Townsend) in order to study Orca whales in the Pacific Northwest region. This workflow uses code and ideas available in the Orcasound GitHub actions workflow and the Orca Hello real-time notification system.
View on GitHubCOLMENA is an open framework designed to simplify the development, deployment, and operation of hyper-distributed applications across the compute continuum. It enables collections of heterogeneous devices to collaborate as a decentralized swarm of agents, presenting the infrastructure as a single, unified computing platform.
View on GitHubJoin researchers using Kiso to simplify their edge-to-cloud experimentation workflows