ritazh / k8s-ml

🎱 A demonstration of existing machine learning toolkits on Kubernetes

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Tensorflow Image Classification Demo on Kubernetes

This repo serves as an example to demonstrate the typical machine learning workflow and how to leverage existing machine learning toolkits for Kubernetes to enhance the development and operations lifecycle.

Here is what you will find in this repo:

  • Train - how to take an Inception v3 model trained on ImageNet images to retrain a new top layer that can recognize new classes of images.
  • Automate - how to run repeatable machine learning experiments using containers.
  • Visualize - how to visualize machine learning trainings with Tensorboard.
  • Operate - how to automate and provision production-ready immutable infrastructure with Kubernetes.
  • Deploy - how to deploy machine learning components to Kubernetes with Kubeflow.
  • Scale - how to scale and test machine learning experiments in parallel using Kubernetes and helm.
  • Manage - how to manage training jobs by creating end to end training pipelines with Argo.
  • Serve - how to serve a trained model for inference with TF Serving as part of Kubeflow.
  • Rapid Prototype - how to enable rapid prototyping with self-service Jupyter Notebook from JupyterHub with GitHub OAuth.

This example is based on the Tensorflow Image Retraining example.

We have modified the example to retrain inception v3 model to identify a particular celebrity. Using the retrained model, we can get predictions like the following:

inference-fbb

Retrain Model Locally

  • Train with Inception v3 and Automate with containers

    # build
    docker build -t ritazh/image-retrain-kubecon:1.9-gpu -f train/Dockerfile.gpu ./train
    
    # push
    docker push ritazh/image-retrain-kubecon:1.9-gpu
    
    # run
    docker run --rm -v $PWD/tf-output:/tf-output ritazh/image-retrain-kubecon:1.9-gpu "--how_many_training_steps=4000" "--learning_rate=0.01" "--bottleneck_dir=/tf-output/bottlenecks" "--model_dir=/tf-output/inception" "--summaries_dir=/tf-output/training_summaries/baseline" "--output_graph=/tf-output/retrained_graph.pb" "--output_labels=/tf-output/retrained_labels.txt" "--image_dir=images" "--saved_model_dir=/tf-output/saved_models/1"

    retrain-tfcontainer

  • Visualize with Tensorboard

    # build
    docker build -t ritazh/tensorboard:1.9 -f ./train/Dockerfile.tensorboard ./train
    
    # push
    docker push ritazh/tensorboard:1.9 
    
    # run
    docker run -d --name tensorboard -p 80:6006 --rm -v $PWD/tf-output:/tf-output ritazh/tensorboard:1.9 "--logdir" "/tf-output/training_summaries"

    tensorboard

  • Serve Trained Model for Inference with TF Serving

    docker run -d --rm --name serving_base tensorflow/serving
    docker cp tf-output/saved_models serving_base:/models/inception
    docker commit --change "ENV MODEL_NAME inception" serving_base $USER/inception_serving
    docker kill serving_base
    docker run -p 8500:8500 -t $USER/inception_serving &
    
    python serving/inception_client.py --server localhost:8500 --image test/fbb1.jpeg
    

    You should get an output as follows:

    outputs {
        key: "classes"
        value {
            dtype: DT_STRING
            tensor_shape {
            dim {
                size: 2
            }
            }
            string_val: "fbb"
            string_val: "notfbb"
        }
        }
        outputs {
        key: "prediction"
        value {
            dtype: DT_FLOAT
            tensor_shape {
            dim {
                size: 1
            }
            dim {
                size: 2
            }
            }
            float_val: 0.95451271534
            float_val: 0.0454873144627
        }
        }
        model_spec {
        name: "inception"
        version {
            value: 1
        }
        signature_name: "serving_default"
    }

    tfserving-inference

Provision Immutable Infrastructure with Kubernetes

Deploy Machine Learning Components to Kubernetes with Kubeflow

  • Install ksonnet version 0.13.1, or you can download a prebuilt binary for your OS.

    # install ks v0.13.1
    export KS_VER=0.13.1
    export KS_PKG=ks_${KS_VER}_linux_amd64
    wget -O /tmp/${KS_PKG}.tar.gz https://github.com/ksonnet/ksonnet/releases/download/v${KS_VER}/${KS_PKG}.tar.gz --no-check-certificate
    mkdir -p ${HOME}/bin
    tar -xvf /tmp/$KS_PKG.tar.gz -C ${HOME}/bin
    echo "PATH=$PATH:${HOME}/bin/$KS_PKG" >> ~/.bashrc
    source ~/.bashrc
  • Install argo CLI

  • Run the following commands to deploy Kubeflow components in your Kubernetes cluster:

    NOTE: This demo has been updated to use kfctl.sh.

    # download kubeflow
    KUBEFLOW_SOURCE=kubeflow # directory to download the kubeflow source
    mkdir ${KUBEFLOW_SOURCE}
    cd ${KUBEFLOW_SOURCE}
    
    export KUBEFLOW_TAG=v0.4.1 # tag corresponding to kubeflow version
    curl https://raw.githubusercontent.com/kubeflow/kubeflow/${KUBEFLOW_TAG}/scripts/download.sh | bash
    # init kubeflow app
    KFAPP=mykubeflowapp
    ${KUBEFLOW_SOURCE}/scripts/kfctl.sh init ${KFAPP} --platform none
    
    # generate kubeflow app
    cd ${KFAPP}
    ${KUBEFLOW_SOURCE}/scripts/kfctl.sh generate k8s
    
    # apply kubeflow to cluster
    ${KUBEFLOW_SOURCE}/scripts/kfctl.sh apply k8s
    
    # view created components
    kubectl get po -n kubeflow
    kubectl get crd
    kubectl get svc -n kubeflow

Persist Data and Logs With Azure Storage

  • Setup PVC components to persist data in pods. https://docs.microsoft.com/en-us/azure/aks/azure-disks-dynamic-pv

    1. Setup storage account for Azure Files
    export RG_NAME=kubecon
    export STORAGE=kubeconstorage
    
    az storage account create --resource-group $RG_NAME --name $STORAGE --sku Standard_LRS
    1. Setup StorageClass, Roles, and PVC's
    # kubectl create -f ./deployments/azure-pvc-roles.yaml
    
    kubectl create -f ./deployments/azure-file-sc.yaml
    kubectl create -f ./deployments/azure-file-pvc.yaml

    Check status

    kubectl get pvc
    
    NAME          STATUS    VOLUME                                     CAPACITY   ACCESS MODES   STORAGECLASS          AGE
    azurefile     Bound     pvc-d686be3e-bc75-11e8-a08d-000d3a4f8d49   5Gi        RWX            azurefile             4h
  • [OPTIONAL] If you want to use a static Azure files instead of creating PVCs,

    1. Run the following to create a Kubernetes secret
    kubectl create secret generic azure-file-secret --from-literal=azurestorageaccountname=$STORAGE_ACCOUNT_NAME --from-literal=azurestorageaccountkey=$STORAGE_KEY
    1. From the deployment yaml, you can reference the static Azure file instance like:
    volumes:
      - name: azure-files
        azureFile:
          secretName: azure-files-secret
          shareName: data
          readOnly: false

Retrain Model with Kubeflow (TFJob)

  • Deploy TFJob and tensorboard

    1. Run training job with TFJob

      kubectl create -f ./deployments/tfjob-retrain.yaml
      
      # check created tfjob
      kubectl get tfjob
      # check created pvc
      kubectl get pvc
      
      # check status of the training
      kubectl logs tfjob-retrain-master-0
      
      # after completed, clean up:
      kubectl delete tfjob tfjob-retrain
      
    2. Run Tensorboard (after the previous training is completed)

      kubectl create -f ./deployments/tfjob-tensorboard.yaml
      
      # Get public IP of tensorboard service
      kubectl get svc
    3. Clean up

      kubectl delete -f ./deployments/tfjob-tensorboard.yaml

Hyperparameter Sweep - Scale and Test Experiments in Parallel using Kubernetes, Helm, Virtual Kubelet, and ACI

This step requires Azure Files mount to be available. Please refer to the Persist Data and Logs With Azure Storage section.

  • Ensure Helm and Virtual Kubelet are installed

    1. Ensure helm is installed and tiller is running in the cluster
    helm version
    1. Install Virtual Kubelet so we can scale our training to run on Azure Container Instance with GPU

    Provide the following in hyperparameter/virtual-kubelet/values.yaml

    targetAKS:
    clientId:
    clientKey:
    tenantId:
    subscriptionId:
    aciRegion:

    Then install the Virtual Kubelet chart in your cluster.

    export VK_RELEASE=virtual-kubelet-latest
    CHART_URL=https://github.com/virtual-kubelet/virtual-kubelet/raw/master/charts/$VK_RELEASE.tgz
    helm install --name vk "$CHART_URL" -f ./hyperparameter/virtual-kubelet/values.yaml
    
    kubectl get nodes
    ...
    virtual-kubelet                     Ready     agent     5s        v1.11.2
  • Deploy hyperparameter chart to run our experiments in parallel on Azure Container Instance

    helm install --name image-retrain-hyperparam ./hyperparameter/chart
    
    az container list -g <ACI RESOURCE GROUP>

    hyperparam-vk-part1

    hyperparam-vk-part2

Creating End to End Pipelines with Argo

  • [OPTIONAL] If you do not want to use the minio component deployed as part of Kubeflow. Deploy your own Minio for S3 compatibility

    1. Update Minio deploy yaml with Azure storage account
    - name: MINIO_ACCESS_KEY
      value: "PUT AZURE STORAGE ACCOUNT NAME HERE"
    - name: MINIO_SECRET_KEY
      value: "PUT AZURE STORAGE ACCOUNT KEY HERE"
    1. Deploy Minio
    # Deploy Minio
    kubectl create -f ./deployments/minio-azurestorage.yaml
    
    # Get endpoint of Minio server
    kubectl get service minio-service
  • Create a pipeline with Argo workflow

    1. Set environment variables for Argo workflow (here we are using the minio shipped as part of Kubeflow)
    # namespace of all the kubeflow components
    export NAMESPACE=kubeflow
    # set to "minio" if using minio shipped as part of kubeflow
    # set to "AZURE STORAGE ACCOUNT NAME" if minio is deployed as a proxy 
    # to Azure storage account from the previous step
    export AZURE_STORAGEACCOUNT_NAME=minio 
    # set to "minio123" if using minio shipped as part of kubeflow
    # set to "AZURE STORAGE ACCOUNT KEY" if minio is deployed as a proxy
    # to Azure storage account from the previous step
    export AZURE_STORAGEACCOUNT_KEY=minio123
    MINIOIP=$(kubectl get svc minio-service -n ${NAMESPACE} -o jsonpath='{.spec.clusterIP}')
    MINIOPORT=$(kubectl get svc minio-service -n ${NAMESPACE} -o jsonpath='{.spec.ports[0].port}')
    
    export S3_ENDPOINT=${MINIOIP}:$MINIOPORT
    export AWS_ENDPOINT_URL=${S3_ENDPOINT}
    export AWS_ACCESS_KEY_ID=$AZURE_STORAGEACCOUNT_NAME
    export AWS_SECRET_ACCESS_KEY=$AZURE_STORAGEACCOUNT_KEY
    export BUCKET_NAME=mybucket
    
    export DOCKER_BASE_URL=docker.io/ritazh # Update this to fit your scenario
    export S3_DATA_URL=s3://${BUCKET_NAME}/data/retrain/
    export S3_TRAIN_BASE_URL=s3://${BUCKET_NAME}/models
    export AWS_REGION=us-east-1
    export JOB_NAME=myjob-$(uuidgen  | cut -c -5 | tr '[:upper:]' '[:lower:]')
    export TF_MODEL_IMAGE=${DOCKER_BASE_URL}/image-retrain-kubecon:1.9-gpu # Retrain image from previous step
    export TF_WORKER=3
    export MODEL_TRAIN_STEPS=200
    
    # Create a secret for accessing the Minio server
    kubectl create secret generic aws-creds --from-literal=awsAccessKeyID=${AWS_ACCESS_KEY_ID} \
    --from-literal=awsSecretAccessKey=${AWS_SECRET_ACCESS_KEY} -n ${NAMESPACE}
    
    # Create a user for the workflow
    kubectl apply -f workflow/tf-user.yaml -n ${NAMESPACE}
    
    1. Submit a workflow to Argo
    # Submit a workflow to argo
    argo submit workflow/model-train-serve-workflow.yaml -n ${NAMESPACE} --serviceaccount tf-user \
    -p aws-endpoint-url=${AWS_ENDPOINT_URL} \
    -p s3-endpoint=${S3_ENDPOINT} \
    -p aws-region=${AWS_REGION} \
    -p tf-model-image=${TF_MODEL_IMAGE} \
    -p s3-data-url=${S3_DATA_URL} \
    -p s3-train-base-url=${S3_TRAIN_BASE_URL} \
    -p job-name=${JOB_NAME} \
    -p tf-worker=${TF_WORKER} \
    -p model-train-steps=${MODEL_TRAIN_STEPS} \
    -p namespace=${NAMESPACE} \
    -p tf-tensorboard-image=tensorflow/tensorflow:1.7.0 \
    -p s3-use-https=0 \
    -p s3-verify-ssl=0
    
    # Check status of the workflow
    argo list -n ${NAMESPACE}
    NAME                STATUS    AGE    DURATION
    tf-workflow-s8k24   Running   5m     5m 
    
    # Check pods that are created by the workflow
    kubectl get pod -n ${NAMESPACE} -o wide -w
    
    # Monitor training from tensorboard
    PODNAME=$(kubectl get pod -n ${NAMESPACE} -l app=tensorboard-${JOB_NAME} -o jsonpath='{.items[0].metadata.name}')
    kubectl port-forward ${PODNAME} -n ${NAMESPACE} 6006:6006
    
    # Get logs from the training pod(s)
    kubectl logs ${JOB_NAME}-master-0 -n ${NAMESPACE} 

    workflow-train

    1. Get Serving IP
    # Get public ip of the serving service once we reach the last stage of the workflow
    SERVINGIP=$(kubectl get svc inception-${JOB_NAME} -n ${NAMESPACE} -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
    
    # Get detailed information of the workflow
    argo get tf-workflow-s8k24 -n ${NAMESPACE} 
    Name:             tf-workflow-s8k24
    Namespace:        tfworkflow
    ServiceAccount:   tf-user
    Status:           Succeeded
    Created:          Tue Oct 30 23:44:27 -0700 (26 minutes ago)
    Started:          Tue Oct 30 23:44:27 -0700 (26 minutes ago)
    Finished:         Tue Oct 30 23:53:56 -0700 (16 minutes ago)
    Duration:         9 minutes 29 seconds
    Parameters:       
    aws-endpoint-url: 40.76.11.177:9012
    s3-endpoint:    40.76.11.177:9012
    aws-region:     us-east-1
    tf-model-image: docker.io/ritazh/image-retrain-kubecon:1.9-gpu
    s3-data-url:    s3://mybucket/data/retrain/
    s3-train-base-url: s3://mybucket/models
    job-name:       myjob-b5e18
    tf-worker:      3
    model-train-steps: 200
    namespace:      tfworkflow
    tf-tensorboard-image: tensorflow/tensorflow:1.7.0
    s3-use-https:   0
    s3-verify-ssl:  0
    tf-ps:          2
    tf-serving-image: elsonrodriguez/model-server:1.6
    ks-image:       elsonrodriguez/ksonnet:0.10.1
    model-name:     inception
    model-hidden-units: 100
    model-batch-size: 100
    model-learning-rate: 0.01
    model-serving:  true
    model-serving-servicetype: LoadBalancer
    model-serving-ks-url: github.com/kubeflow/kubeflow/tree/master/kubeflow
    model-serving-ks-tag: 1f474f30
    aws-secret:     aws-creds
    
    STEP                            PODNAME                       DURATION  MESSAGE
    βœ” tf-workflow-s8k24                                                    
    β””---βœ” get-workflow-info                                                
        β””---βœ” tensorboard                                                  
            β””---βœ” train-model                                              
                β””---βœ” serve-model  tf-workflow-s8k24-3774882124  1m        
                                                                                    
    βœ” onExit                                                               
    β””---βœ” cleanup                  tf-workflow-s8k24-1936931737  1m
    

Serve a Trained Model for Inference with Kubeflow (TF Serving)

  • Run test client against the Serving endpoint to get predictions Using the serving IP from the previous step, run the following:

    python serving/inception_client.py --server ${SERVINGIP}:9000 --image test/fbb1.jpeg
    
    # You will see the following output
    outputs {
        key: "classes"
        value {
            dtype: DT_STRING
            tensor_shape {
            dim {
                size: 2
            }
            }
            string_val: "fbb"
            string_val: "notfbb"
        }
        }
        outputs {
        key: "prediction"
        value {
            dtype: DT_FLOAT
            tensor_shape {
            dim {
                size: 1
            }
            dim {
                size: 2
            }
            }
            float_val: 0.96350902319
            float_val: 0.0364910177886
        }
        }
        model_spec {
        name: "inception"
        version {
            value: 1
        }
        signature_name: "serving_default"
    }

    workflow-inference

Rapid Prototyping with Self-service Jupyter Notebook from JupyterHub with GitHub OAuth

  • Ensure the JupyterHub component is running and the service has a public IP

    1. Get the pod and the service
    kubectl get pod -n ${NAMESPACE} -l app=tf-hub
    NAME       READY     STATUS    RESTARTS   AGE
    tf-hub-0   1/1       Running   0          30m
    
    kubectl get svc -n ${NAMESPACE} -l app=tf-hub-lb  
    NAME        TYPE           CLUSTER-IP   EXTERNAL-IP       PORT(S)        AGE
    tf-hub-lb   LoadBalancer   10.0.91.30   137.xx.xx.xx   80:31137/TCP   1d
    1. Create an OAuth app on GitHub. Use the external IP of tb-hub-lb. GitHub OAuth app

    2. By default JupyterHub uses dummy authenticator to use plaintext username and password for login. Edit the JupyterHub configmap to enable GitHub OAuth.

    kubectl edit configmap jupyterhub-config -n ${NAMESPACE}
    
    # Replace the dummyauthenticator with GitHubOAuthenticator
    # c.JupyterHub.authenticator_class = 'dummyauthenticator.DummyAuthenticator'
    c.JupyterHub.authenticator_class = GitHubOAuthenticator
    c.GitHubOAuthenticator.oauth_callback_url = 'http://137.xx.xx.xx/hub/oauth_callback'
    c.GitHubOAuthenticator.client_id = 'GET THIS FROM GITHUB DEVELOPER SETTING'
    c.GitHubOAuthenticator.client_secret = 'GET THIS FROM GITHUB DEVELOPER SETTING'
    1. Restart the tf-hub-0 pod

    2. Launch JupyterHub from a browser using the external IP from the tf-hub-lb service. Everyone on the team can now create their own Jupyter Notebook instance after signing in with their github account and selecting the resources they need to create their own Jupyter Notebook instance.

    jupyterhub-github

    1. Start using your own Jupyter Notebook instance to prototype

    jupyternotebook

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🎱 A demonstration of existing machine learning toolkits on Kubernetes


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