https://github.com/kubeflow/katib
Revision c8cb2cbbe3aaaebb257531ae4d4b9f3b4b48d694 authored by Ce Gao on 06 January 2020, 04:17:41 UTC, committed by Kubernetes Prow Robot on 06 January 2020, 04:17:41 UTC
* feat: Support resource in sidecar Signed-off-by: Ce Gao <gaoce@caicloud.io> * feat: Support webhook service name Signed-off-by: Ce Gao <gaoce@caicloud.io> * feat: Fix Signed-off-by: Ce Gao <gaoce@caicloud.io> * fix: Have a large mem Signed-off-by: Ce Gao <gaoce@caicloud.io> * fix: Fix import Signed-off-by: Ce Gao <gaoce@caicloud.io> * fix: Add comma Signed-off-by: Ce Gao <gaoce@caicloud.io>
1 parent f3e8405
Tip revision: c8cb2cbbe3aaaebb257531ae4d4b9f3b4b48d694 authored by Ce Gao on 06 January 2020, 04:17:41 UTC
feat: Support resource in sidecar (#991)
feat: Support resource in sidecar (#991)
Tip revision: c8cb2cb
README.md
<h1 align="center">
<img src="./docs/images/Katib_Logo.png" alt="logo" width="200">
<br>
</h1>
[![Build Status](https://travis-ci.org/kubeflow/katib.svg?branch=master)](https://travis-ci.org/kubeflow/katib)
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Katib is a Kubernetes Native System for [Hyperparameter Tuning][1] and [Neural Architecture Search][2].
The system is inspired by [Google vizier][3] and supports multiple ML/DL frameworks (e.g. TensorFlow, MXNet, and PyTorch).
<!-- START doctoc generated TOC please keep comment here to allow auto update -->
<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
**Table of Contents** *generated with [DocToc](https://github.com/thlorenz/doctoc)*
- [Name](#name)
- [Concepts in Katib](#concepts-in-katib)
- [Experiment](#experiment)
- [Suggestion](#suggestion)
- [Trial](#trial)
- [Worker Job](#worker-job)
- [Components in Katib](#components-in-katib)
- [Getting Started](#getting-started)
- [Web UI](#web-ui)
- [API Documentation](#api-documentation)
- [Installation](#installation)
- [TF operator](#tf-operator)
- [Pytorch operator](#pytorch-operator)
- [Katib](#katib)
- [Running examples](#running-examples)
- [Cleanups](#cleanups)
- [CONTRIBUTING](#contributing)
<!-- END doctoc generated TOC please keep comment here to allow auto update -->
## Name
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.
## Concepts in Katib
Katib has the concepts of Experiment, Trial, Job and Suggestion.
### Experiment
`Experiment` represents a single optimization run over a feasible space.
Each `Experiment` contains a configuration
1. Objective: What we are trying to optimize
2. Search Space: Constraints for configurations describing the feasible space.
3. Search Algorithm: How to find the optimal configurations
`Experiment` is defined as a CRD. Refer to [here](docs/user-guide.md) about how to customize a `Experiment`.
### Suggestion
A Suggestion is a proposed solution to the optimization problem which is one set of hyperparameter values or a list of parameter assignments. Then a `Trial` will be created to evaluate the parameter assignments.
`Suggestion` is defined as a CRD
### Trial
A `Trial` is one iteration of the optimization process, which is one `worker job` instance with a list of parameter assignments(corresponding to a suggestion).
`Trial` is defined as a CRD
### Worker Job
A `Worker Job` refers to a process responsible for evaluating a `Trial` and calculating its objective value.
The worker kind can be [Kubernetes Job](https://kubernetes.io/docs/concepts/workloads/controllers/jobs-run-to-completion/) which is a non distributed execution, [Kubeflow TFJob](https://www.kubeflow.org/docs/guides/components/tftraining/) or [Kubeflow PyTorchJob](https://www.kubeflow.org/docs/guides/components/pytorch/) which are distributed executions.
Thus, Katib supports multiple frameworks with the help of different job kinds.
Currently Katib supports the following exploration algorithms:
* random search
* grid search
* [hyperband](https://arxiv.org/pdf/1603.06560.pdf)
* [bayesian optimization](https://arxiv.org/pdf/1012.2599.pdf)
* [NAS based on reinforcement learning](https://github.com/kubeflow/katib/tree/master/pkg/suggestion/v1alpha3/NAS_Reinforcement_Learning)
## Components in Katib
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 `pkg/apis/manager/v1alpha3/api.proto`.
- katib: main components.
- katib-manager: GRPC API server of katib which is the DB Interface.
- katib-db: Data storage backend of katib.
- katib-ui: User interface of katib.
- katib-controller: Controller for katib CRDs in Kubernetes.
## Getting Started
Please see [here](./docs/quick-start.md) for more details.
## Web UI
Katib provides a Web UI.
You can visualize general trend of Hyper parameter space and each training history. You can use
[random-example](https://github.com/kubeflow/katib/blob/master/examples/v1alpha3/random-example.yaml) or
[other examples](https://github.com/kubeflow/katib/blob/master/examples/v1alpha3) to generate a similar UI.
![katibui](./docs/images/katib-ui.png)
## API Documentation
Please refer to [api.md](./pkg/apis/manager/v1alpha3/gen-doc/api.md).
## Installation
For standard installation of Katib with support for all job operators, refer to [Kubeflow Official Docs](http://kubeflow.org) and skip this section. Or if you want to install Katib manually, follow these steps
```
git clone git@github.com:kubeflow/manifests.git
Set `MANIFESTS_DIR` to the cloned folder.
```
### TF operator
For installing tfjob operator, run the following
```
cd "${MANIFESTS_DIR}/tf-training/tf-job-crds/base"
kustomize build . | kubectl apply -f -
cd "${MANIFESTS_DIR}/tf-training/tf-job-operator/base"
kustomize build . | kubectl apply -n kubeflow -f -
```
### Pytorch operator
For installing pytorch operator, run the following
```
cd "${MANIFESTS_DIR}/pytorch-job/pytorch-job-crds/base"
kustomize build . | kubectl apply -f -
cd "${MANIFESTS_DIR}/pytorch-job/pytorch-operator/base/"
kustomize build . | kubectl apply -n kubeflow -f -
```
### Katib
Finally, you can install Katib
```
cd "${MANIFESTS_DIR}/katib/katib-crds/base"
kustomize build . | kubectl apply -f -
cd "${MANIFESTS_DIR}/katib/katib-controller/base"
kustomize build . | kubectl apply -f -
```
If you want to use Katib in a cluster that doesn't have a StorageClass for dynamic volume provisioning at your cluster, you have to create persistent volume manually to bound your persistent volume claim.
This is sample yaml file for creating a persistent volume
```yaml
apiVersion: v1
kind: PersistentVolume
metadata:
name: katib-mysql
labels:
type: local
app: katib
spec:
capacity:
storage: 10Gi
accessModes:
- ReadWriteOnce
hostPath:
path: /data/katib
```
Create this pv after deploying Katib package
### Running examples
After deploy everything, you can run examples to verify the installation.
This is example for tfjob operator
```
kubectl create -f https://raw.githubusercontent.com/kubeflow/katib/master/examples/v1alpha3/tfjob-example.yaml
```
This is example for pytorch operator
```
kubectl create -f https://raw.githubusercontent.com/kubeflow/katib/master/examples/v1alpha3/pytorchjob-example.yaml
```
You can check status of experiment
```yaml
$ kubectl describe experiment tfjob-example -n kubeflow
Name: tfjob-example
Namespace: kubeflow
Labels: <none>
Annotations: <none>
API Version: kubeflow.org/v1alpha3
Kind: Experiment
Metadata:
Creation Timestamp: 2019-10-06T12:25:44Z
Generation: 1
Resource Version: 2110410
Self Link: /apis/kubeflow.org/v1alpha3/namespaces/kubeflow/experiments/tfjob-example
UID: 6b2bef2d-e834-11e9-93ee-42010aa00075
Spec:
Algorithm:
Algorithm Name: random
Max Failed Trial Count: 3
Max Trial Count: 12
Metrics Collector Spec:
Collector:
Kind: TensorFlowEvent
Source:
File System Path:
Kind: Directory
Path: /train
Objective:
Goal: 0.99
Objective Metric Name: accuracy_1
Type: maximize
Parallel Trial Count: 3
Parameters:
Feasible Space:
Max: 0.05
Min: 0.01
Name: --learning_rate
Parameter Type: double
Feasible Space:
Max: 200
Min: 100
Name: --batch_size
Parameter Type: int
Trial Template:
Go Template:
Raw Template: apiVersion: "kubeflow.org/v1"
kind: TFJob
metadata:
name: {{.Trial}}
namespace: {{.NameSpace}}
spec:
tfReplicaSpecs:
Worker:
replicas: 1
restartPolicy: OnFailure
template:
spec:
containers:
- name: tensorflow
image: gcr.io/kubeflow-ci/tf-mnist-with-summaries:1.0
imagePullPolicy: Always
command:
- "python"
- "/var/tf_mnist/mnist_with_summaries.py"
- "--log_dir=/train/metrics"
{{- with .HyperParameters}}
{{- range .}}
- "{{.Name}}={{.Value}}"
{{- end}}
{{- end}}
Status:
Completion Time: 2019-10-06T12:28:50Z
Conditions:
Last Transition Time: 2019-10-06T12:25:44Z
Last Update Time: 2019-10-06T12:25:44Z
Message: Experiment is created
Reason: ExperimentCreated
Status: True
Type: Created
Last Transition Time: 2019-10-06T12:28:50Z
Last Update Time: 2019-10-06T12:28:50Z
Message: Experiment is running
Reason: ExperimentRunning
Status: False
Type: Running
Last Transition Time: 2019-10-06T12:28:50Z
Last Update Time: 2019-10-06T12:28:50Z
Message: Experiment has succeeded because Objective goal has reached
Reason: ExperimentSucceeded
Status: True
Type: Succeeded
Current Optimal Trial:
Observation:
Metrics:
Name: accuracy_1
Value: 1
Parameter Assignments:
Name: --learning_rate
Value: 0.018532845700535087
Name: --batch_size
Value: 109
Start Time: 2019-10-06T12:25:44Z
Trials: 4
Trials Running: 2
Trials Succeeded: 2
Events: <none>
```
When the spec.Status.Condition becomes ```Succeeded```, the experiment is finished.
You can monitor your results in Katib UI.
Access Katib UI via Kubeflow dashboard if you have used standard installation or port-forward the `katib-ui` service if you have installed manually.
```
kubectl -n kubeflow port-forward svc/katib-ui 8080:80
```
You can access the Katib UI using this URL: ```http://localhost:8080/katib/```.
### Cleanups
Delete installed components using `kubectl delete -f` on the respective folders.
## CONTRIBUTING
Please feel free to test the system! [developer-guide.md](./docs/developer-guide.md) is a good starting point for developers.
[1]: https://en.wikipedia.org/wiki/Hyperparameter_optimization
[2]: https://en.wikipedia.org/wiki/Neural_architecture_search
[3]: https://static.googleusercontent.com/media/research.google.com/ja//pubs/archive/bcb15507f4b52991a0783013df4222240e942381.pdf
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