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MinikubeDemo.md

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Simple Minikube Demo

You can deploy katib components and try a simple mnist demo on your laptop!

Requirement

  • VirtualBox
  • Minikube
  • kubectl

deploy

Start Katib on Minikube with deploy.sh. A Minikube cluster and Katib components will be deployed!

You can check them with kubectl -n katib get pods. Don't worry if the vizier-core get an error. It will be recovered after DB will be prepared. Wait until all components will be Running status.

Then, start port-forward for katib services 6789 -> manager and 3000 -> UI.

$ kubectl -n katib port-forward svc/vizier-core 6789:6789 &
$ kubectl -n katib port-forward svc/modeldb-frontend 3000:3000 &

To start HyperParameter Tuning, you need a katib client. It will call API of Katib to create study, get suggestions, run trial, and get metrics. The details of the system flow for the client and katib components is here.

An example of client is here. The client will read three config files.

  • study-config.yml: Define study property and feasible space of parameters.
  • suggesiton-config.yml: Define suggesiton parameter for each study and suggestion service. In this file, the config is for grid suggestion service.
  • worker-config.yml: Define config for evaluation worker.

Create Study

Random Suggestion Demo

You can run rundom suggesiton demo.

go run client-example.go -a random

In this demo, 2 random parameters in

  • Learning Rate (--lr) - type: double
  • Number of NN Layer (--num-layers) - type: int
  • optimizer (--optimizer) - type: categorical

Logs

2018/04/26 17:43:26 Study ID n9debe3de9ef67c8
2018/04/26 17:43:26 Study ID n9debe3de9ef67c8 StudyConfname:"random-demo" owner:"katib" optimization_type:MAXIMIZE optimization_goal:0.99 parameter_configs:<configs:<name:"--lr" parameter_type:DOUBLE feasible:<max:"0,03" min:"0.07" > > > default_suggestion_algorithm:"random" default_early_stopping_algorithm:"medianstopping" objective_value_name:"Validation-accuracy" metrics:"accuracy" metrics:"Validation-accuracy"
2018/04/26 17:43:26 Get Random Suggestions [trial_id:"i988add515f1ca4c" study_id:"n9debe3de9ef67c8" parameter_set:<name:"--lr" parameter_type:DOUBLE value:"0.0611" >  trial_id:"g7afad58be7da888" study_id:"n9debe3de9ef67c8" parameter_set:<name:"--lr" parameter_type:DOUBLE value:"0.0444" > ]
2018/04/26 17:43:26 WorkerID p4482bfb5cdc17ee start
2018/04/26 17:43:26 WorkerID c19ca08ca4e6aab1 start

Grid Demo

Almost same as random suggestion.

go run client-example.go -a grid

In this demo, make 4 grids for learning rate (--lr) Min 0.03 and Max 0.07.

UI

You can check your Model with Web UI. Acsess to http://127.0.0.1:3000/ The Results will be saved automatically.

ModelManagement

You can export model data to yaml file with CLI.

katib-cli -s {{server-cli}} pull study {{study ID or name}}  -o {{filename}}

And you can push your existing models to Katib with CLI. mnist-models.yaml is traind 22 models using random suggestion with this Parameter Config.

configs:
    - name: --lr
      parametertype: 1
      feasible:
        max: "0.07"
        min: "0.03"
        list: []
    - name: --lr-factor
      parametertype: 1
      feasible:
        max: "0.05"
        min: "0.005"
        list: []
    - name: --lr-step
      parametertype: 2
      feasible:
        max: "20"
        min: "5"
        list: []
    - name: --optimizer
      parametertype: 4
      feasible:
        max: ""
        min: ""
        list:
        - sgd
        - adam
        - ftrl

You can easy to explore the model on ModelDB.

katib-cli push md -f mnist-models.yaml

Clean

Clean up with ./destroy.sh script. It will stop port-forward process and delete minikube cluster.