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Posted to commits@dolphinscheduler.apache.org by GitBox <gi...@apache.org> on 2022/04/24 14:26:04 UTC
[GitHub] [dolphinscheduler] jieguangzhou opened a new issue, #9724: [Discussion][Machine Learning] Support AI task and the open source project about MLops
jieguangzhou opened a new issue, #9724:
URL: https://github.com/apache/dolphinscheduler/issues/9724
### Search before asking
- [X] I had searched in the [issues](https://github.com/apache/dolphinscheduler/issues?q=is%3Aissue) and found no similar issues.
### What happened
I have seen a Machine Learning Platform post on Medium. The post talk about Lizhi Machine Learning Platform&Apache DolphinScheduler.
https://medium.com/@DolphinScheduler/a-formidable-combination-of-lizhi-machine-learning-platform-dolphinscheduler-creates-new-paradigm-e445938f1af
Like this, I have a try to do something like this.
Figure 1 shows the training workflow startup screen
<img width="496" alt="image" src="https://user-images.githubusercontent.com/31528124/164979694-b55493e8-c8e7-4fda-9489-5d23b929cf05.png">
In this workflow, I implemented four algorithms (SVM, LR, LGBM, XGboost) using the API of Sklearn, Lightgbm, and Xgboost.
Every algorithm's parameters can fill in the value of key "params". In this case, the parameters of LGBM is "n_estimators=200;num_leaves=20".
The experiment tracking module is supported by MLFlow.The picture below shows the report of the experiment.
I register the model every time I run it.
When the model is trained, run the deployment workflow. Like this:
<img width="495" alt="image" src="https://user-images.githubusercontent.com/31528124/164980301-451e8fa1-c105-493b-9415-5c292fb1c3de.png">
We can deploy the version 2 model to the k8s cluster.
<img width="500" alt="image" src="https://user-images.githubusercontent.com/31528124/164980398-35dd3ec8-17b9-4603-b57e-ebb0c666b7d2.png">
And then we can see the deployment and pods
At the same time, we can access the service through the interface.
BTW, we can also connect the training workflow with the deployment workflow as a sub-workflow, like this.
<img width="613" alt="image" src="https://user-images.githubusercontent.com/31528124/164980627-63517bcd-aca0-4e74-b16e-0113381ef279.png">
### What you expected to happen
None
### How to reproduce
None
### Anything else
The above workflow is based on the Shell task. But it is too complex to ml engineer. I hope to write new types of tasks that make them easier for users to use.
The training workflow contains one task. The code is as follows
`data_path=${data_path}
export MLFLOW_TRACKING_URI=${MLFLOW_TRACKING_URI}
echo $data_path
repo=https://github.com/jieguangzhou/mlflow_sklearn_gallery.git
mlflow run $repo -P algorithm=${algorithm} -P data_path=$data_path -P params="${params}" -P param_file=${param_file} -P model_name=${model_name} --experiment-name=${experiment_name}
echo "training finish"`
The deployment workflow contains two task.
<img width="639" alt="image" src="https://user-images.githubusercontent.com/31528124/164980832-fd6a524e-daeb-4e93-a62d-3fcbcfe3a74b.png">
The code of "build docker" workflow is as follows
`eval $(minikub -p minikube docker-env)
export MLFLOW_TRACKING_URI=${MLFLOW_TRACKING_URI}
image_name=mlflow/${model_name}:${version}
echo $image_name
mlflow models build-docker -m "models:/${model_name}/${version}" -n $image_name --enable-mlserver`
The code of the "create deployment" workflow which deploys the model to the k8s cluster is as follows
`version_lower=$(echo "${version}" | tr '[:upper:]' '[:lower:]')
kubectl apply -f - << END
apiVersion: apps/v1
kind: Deployment
metadata:
name: mlflow-${model_name}-$version_lower
spec:
selector:
matchLabels:
app: mlflow
replicas: 3 # tells deployment to run 2 pods matching the template
template:
metadata:
labels:
app: mlflow
spec:
containers:
- name: mlflow-iris
image: mlflow/${model_name}:${version}
imagePullPolicy: IfNotPresent
ports:
- containerPort: 8080
---
apiVersion: v1
kind: Service
metadata:
name: mlflow-${model_name}-$version_lower
spec:
ports:
- port: 8080
targetPort: 8080
selector:
app: mlflow
END
sleep 5s
kubectl port-forward deployment/mlflow-${model_name}-$version_lower ${deployment_port}:8080`
### Version
dev
### Are you willing to submit PR?
- [X] Yes I am willing to submit a PR!
### Code of Conduct
- [X] I agree to follow this project's [Code of Conduct](https://www.apache.org/foundation/policies/conduct)
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[GitHub] [dolphinscheduler] github-actions[bot] commented on issue #9724: [Discussion][Machine Learning] Support AI task and the open source project about MLops
Posted by GitBox <gi...@apache.org>.
github-actions[bot] commented on issue #9724:
URL: https://github.com/apache/dolphinscheduler/issues/9724#issuecomment-1107852439
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[GitHub] [dolphinscheduler] jieguangzhou commented on issue #9724: [Discussion][Machine Learning] Support AI task and the open source project about MLops
Posted by GitBox <gi...@apache.org>.
jieguangzhou commented on issue #9724:
URL: https://github.com/apache/dolphinscheduler/issues/9724#issuecomment-1107853867
I build a wrong type of issue
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[GitHub] [dolphinscheduler] jieguangzhou closed issue #9724: [Discussion][Machine Learning] Support AI task and the open source project about MLops
Posted by GitBox <gi...@apache.org>.
jieguangzhou closed issue #9724: [Discussion][Machine Learning] Support AI task and the open source project about MLops
URL: https://github.com/apache/dolphinscheduler/issues/9724
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