Hyperparameter Tuning on Kubernetes. This project is inspired by Google vizier. Katib is a scalable and flexible hyperparameter tuning framework and is tightly integrated with kubernetes. Also it does not depend on a specific Deep Learning framework e.g. TensorFlow, MXNet, and PyTorch).
Katib stands for secretary in Arabic. As Vizier stands for a high official or a prime minister in Arabic, this project Katib is named in the honor of Vizier.
As in Google Vizier, Katib also has the concepts of Study, Trial and Suggestion.
Represents a single optimization run over a feasible space. Each Study contains a configuration describing the feasible space, as well as a set of Trials. It is assumed that objective function f(x) does not change in the course of a Study.
A Trial is a list of parameter values, x, that will lead to a single evaluation of f(x). A Trial can be “Completed”, which means that it has been evaluated and the objective value f(x) has been assigned to it, otherwise it is “Pending”. One trial corresponds to one k8s Job.
A Suggestion is an algorithm to construct a parameter set. Currently Katib supports the following exploration algorithms:
- random
- grid
- hyperband
- bayesian optimization
Katib consists of several components as shown below. Each component is running on k8s as a deployment.
Each component communicates with others via GRPC and the API is defined at api/api.proto.
- vizier: main components.
- vizier-core : API server of vizier.
- vizier-db
- suggestion : implementation of each exploration algorithm.
- vizier-suggestion-random
- vizier-suggestion-grid
- vizier-suggestion-hyperband
- vizier-suggestion-bayesianoptimization
- modeldb : WebUI
- modeldb-frontend
- modeldb-backend
- modeldb-db
Please see MinikubeDemo.md for more details.
In the Study config file, we define the feasible space of parameters and configuration of a kubernetes job. Examples of such Study configs are in the conf directory. The configuration items are as follows:
- name: Study name
- owner: Owner
- objectivevaluename: Name of the objective value. Your evaluated software should be print log
{objectivevaluename}={objective value}in std-io. - optimizationtype: Optimization direction of the objective value. 1=maximize 2=minimize
- suggestalgorithm: [random, grid, hyperband] now
- suggestionparameters: Parameter of the algorithm. Set name-value style.
- In random suggestion
- SuggestionNum: How many suggestions will Katib create.
- MaxParallel: Max number of run on kubernetes
- In grid suggestion
- MaxParallel: Max number of run on kubernetes
- GridDefault: default number of grid
- name: [parameter name] grid number of specified parameter.
- In random suggestion
- metrics: The value you want to save to modeldb besides objectivevaluename.
- image: docker image name
- mount
- pvc: pvc
- path: MountPath in container
- pullsecret: Name of Image pull secret
- gpu: number of GPU (If you want to run cpu task, set 0 or delete this parameter)
- command: commands
- parameterconfigs: define feasible space
- configs
- name : parameter space
- parametertype: 1=float, 2=int, 4=categorical
- feasible
- min
- max
- list (for categorical)
- configs
Katib provides a Web UI.
You can visualize general trend of Hyper parameter space and each training history.
Please refer to katib-cli.md.
Please feel free to test the system! developer-guide.md is a good starting point for developers.
We developed the neural architecture search function for Katib, thus extending it from a hyperparmeter tool to a general AutoML platform. We extended the Katib API, database API, added EnvelopNet suggetion and reinforcement learning suggetion. Our work is summaried in katib.pdf. This paper is currently under review for Operational Machine Learning 2019.
- Integrate KubeFlow (TensorFlow, Caffe2 and PyTorch operators)
- Support Early Stopping
- Enrich the GUI