Machine Learning is one of the hottest area nowadays. New algorithms and models are widely used in commercial solutions thus the whole ML process as a software development and deployment process needs to be optimized.

Kubeflow is an opensource platform which allows to build complete multi-user analytical environment. It is setup on the Kubernetes thus it can be simply installed on a public cloud, on premise Kubernetes cluster or on your workstation.

On the other hand MLFlow is a platform which can be run as standalone application. It doesn’t require Kubernetes thus the setup much more simpler then Kubeflow but it doesn’t support multi-user/multi-team separation.

In this article we will use Kubeflow and MLflow to build the isolated workspace and MLOps pipelines for analytical teams.

Currently we use Kubeflow platform in @BankMillennium to build AI solutions and conduct MLOPS process and this article is inspired by the experience gained while launching and using the platform.

Before you will continue reading please watch short introduction:

AI Platform

The core of the platform will be setup using Kubeflow (version 1.0.1) on Kubernetes (v1.17.0). The Kuberenetes was setup using Rancher RKE which simplifies the installation.

The Kubeflow gives complete analytical multi-user/multi-teams environment with: authentication (dex), jupyter notebook workspace, pipelines, metadata store, artifact store, models deployment engines (kfserving, seldon).

Namespace isolation

The user namespaces by default are isolated in Kubeflow UI but in fact are not isolated at all.

The ServiceRoleBinding configuration is very naive and checks only kubeflow-userid header to check RBAC access.

apiVersion: rbac.istio.io/v1alpha1
kind: ServiceRoleBinding
metadata:
  annotations:
    role: admin
    user: admin@kubeflow.org
  namespace: qooba
  ownerReferences:
  - apiVersion: kubeflow.org/v1
    blockOwnerDeletion: true
    controller: true
    kind: Profile
    name: qooba
    uid: 400b5e7b-4b58-40e7-8613-7b0ef01a55ba
spec:
  roleRef:
    kind: ServiceRole
    name: ns-access-istio
  subjects:
  - properties:
      request.headers[kubeflow-userid]: admin@kubeflow.org

Thus we can simply access other namespace notebook from notebooks in different namespace setting kubeflow-userid header:

import requests
url='http://{{notebook_name}}.{{namespace}}.svc.cluster.local'

headers={    
        'kubeflow-userid': "admin@kubeflow.org"
}

requests.get(url,headers=headers).text

To fix this we can setup appropriate Kubernetes NetworkPolicies eg.

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-ingress-default
  namespace: {{namespace}}
spec:
  podSelector: {}
  ingress:
  - from:
    - namespaceSelector:
        matchExpressions:
          - {key: namespace, operator: In, values: [{{namespace}}, kubeflow, istio-system, kube-system]}
  policyTypes:
  - Ingress
---
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: deny-egress-all
  namespace: {{namespace}}
spec:
  podSelector:
    matchLabels: {}
  policyTypes:
  - Egress
---
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-egress-dns
  namespace: {{namespace}}
spec:
  podSelector:
    matchLabels: {}
  policyTypes:
  - Egress
  egress:
  - to:
    - namespaceSelector:
        matchLabels:
          namespace: kube-system
    ports:
    - protocol: UDP
      port: 53
---
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-egress-istio
  namespace: {{namespace}}
spec:
  podSelector:
    matchLabels: {}
  policyTypes:
  - Egress
  egress:
  - to:
    - namespaceSelector:
        matchLabels:
          namespace: istio-system
---
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-egress-kubeflow
  namespace: {{namespace}}
spec:
  podSelector:
    matchLabels: {}
  policyTypes:
  - Egress
  egress:
  - to:
    - namespaceSelector:
        matchLabels:
          namespace: kubeflow
---
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-egress-internal
  namespace: {{namespace}}
spec:
  podSelector:
    matchLabels: {}
  policyTypes:
  - Egress
  egress:
  - to:
    - namespaceSelector:
        matchLabels:
          namespace: {{namespace}}

Isolated model registry

By default Kubeflow is equipped with metadata and artifact store shared between namespaces which makes it difficult to secure and organize spaces for teams. To fix this we will setup separate MLflow Tracking Server and Model Registry for each team namespace.

MLflow docker image qooba/mlflow:

FROM continuumio/miniconda3
RUN apt update && apt install python3-mysqldb default-libmysqlclient-dev  -yq
RUN pip install mlflow sklearn jupyterlab watchdog[watchmedo] boto3
RUN conda install pymysql
ENV NB_PREFIX /
CMD ["sh","-c", "jupyter notebook --notebook-dir=/home/jovyan --ip=0.0.0.0 --no-browser --allow-root --port=8888 --NotebookApp.token='' --NotebookApp.password='' --NotebookApp.allow_origin='*' --NotebookApp.base_url=${NB_PREFIX}"]

mlflow.yaml:

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: mlflow-pv-claim
  namespace: qooba
spec:
  accessModes:
  - ReadWriteOnce
  resources:
    requests:
      storage: 10Gi
  storageClassName: managed-nfs-storage
  volumeMode: Filesystem
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: mlflow
  namespace: qooba
---
apiVersion: v1
kind: Service
metadata:
  name: mlflow
  namespace: qooba
  labels:
    app: mlflow
spec:
  ports:
  - name: http
    port: 5000
    targetPort: 5000
  selector:
    app: mlflow
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mlflow
  namespace: qooba
spec:
  replicas: 1
  selector:
    matchLabels:
      app: mlflow
      version: v1
  template:
    metadata:
      labels:
        app: mlflow
        version: v1
    spec:
      serviceAccountName: mlflow
      containers:
      - image: qooba/mlflow
        imagePullPolicy: IfNotPresent
        name: mlflow
        command: ["mlflow","server","-h","0.0.0.0","--backend-store-uri","sqlite:///mlflow/mlflow.db","--default-artifact-root","/mlflow/mlruns"]]
        ports:
        - containerPort: 5000
        volumeMounts:
          - mountPath: /mlflow
            name: mlflow
          - mountPath: /dev/shm
            name: dshm
      volumes:
        - name: mlflow
          persistentVolumeClaim:
            claimName: mlflow-pv-claim
        - emptyDir:
            medium: Memory
          name: dshm
---
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
  name: mlflow
  namespace: qooba
spec:
  hosts:
  - "*"
  gateways:
  - qooba/mlflow-gateway
  http:
  - match:
    - uri:
        prefix: /
    rewrite:
        uri: /
    route:
    - destination:
        port:
          number: 5000
        host: mlflow
---
apiVersion: networking.istio.io/v1alpha3
kind: Gateway
metadata:
  name: mlflow-gateway
  namespace: qooba
spec:
  selector:
    istio: ingressgateway
  servers:
  - hosts:
    - '*'
    port:
      name: http
      number: 5000
      protocol: HTTP
---
apiVersion: networking.istio.io/v1alpha3
kind: EnvoyFilter
metadata:
  name: mlflow-filter
  namespace: istio-system
spec:
  filters:
  - filterConfig:
      httpService:
        authorizationRequest:
          allowedHeaders:
            patterns:
            - exact: cookie
            - exact: X-Auth-Token
        authorizationResponse:
          allowedUpstreamHeaders:
            patterns:
            - exact: kubeflow-userid
        serverUri:
          cluster: outbound|8080||authservice.istio-system.svc.cluster.local
          failureModeAllow: false
          timeout: 10s
          uri: http://authservice.istio-system.svc.cluster.local
      statusOnError:
        code: GatewayTimeout
    filterName: envoy.ext_authz
    filterType: HTTP
    insertPosition:
      index: FIRST
    listenerMatch:
      listenerProtocol: HTTP
      listenerType: GATEWAY
      portNumber: 5000
  workloadLabels:
    istio: ingressgateway
---
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
  name: dex-mlflow
  namespace: auth
spec:
  gateways:
  - qooba/mlflow-gateway
  hosts:
  - '*'
  http:
  - match:
    - uri:
        prefix: /dex/
    route:
    - destination:
        host: dex.auth.svc.cluster.local
        port:
          number: 5556

additionally we have to edit istio gateway and add mlflow to access the mlflow UI:

kubectl edit svc istio-ingressgateway -n istio-system

and add:

spec:
  ports:
  ...
  - name: mlflow
    nodePort: 31382
    port: 5000
    protocol: TCP
    targetPort: 5000

The MLflow repository can be accessed from web browser:

Additionally we have to mount PersistentVolume mlflow-pv-claim to user notebook where we will store the training artifacts:

kubectl edit Notebook -n qooba sklearn

apiVersion: kubeflow.org/v1
kind: Notebook
metadata:
  labels:
    app: sklearn
  name: sklearn
  namespace: qooba
spec:
  template:
    spec:
      containers:
      - env: []
        image: qooba/mlflow
        name: sklearn
        resources:
          requests:
            cpu: "0.5"
            memory: 1.0Gi
        volumeMounts:
        - mountPath: /home/jovyan
          name: workspace-sklearn
        - mountPath: /mlflow
          name: mlflow
        - mountPath: /dev/shm
          name: dshm
      serviceAccountName: default-editor
      ttlSecondsAfterFinished: 300
      volumes:
      - name: workspace-sklearn
        persistentVolumeClaim:
          claimName: workspace-sklearn
      - name: mlflow
        persistentVolumeClaim:
          claimName: mlflow-pv-claim
      - emptyDir:
          medium: Memory
        name: dshm  

Now analysts can log models and metrics from jupyter notebook workspace
(code example from https://www.mlflow.org/docs/latest/tutorials-and-examples/tutorial.html):

import os
import warnings
import sys
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet
from urllib.parse import urlparse
import mlflow
import mlflow.sklearn

import logging

remote_server_uri='http://mlflow:5000'
mlflow.set_tracking_uri(remote_server_uri)

mlflow.set_experiment("/my-experiment2")


logging.basicConfig(level=logging.WARN)
logger = logging.getLogger(__name__)

def eval_metrics(actual, pred):
    rmse = np.sqrt(mean_squared_error(actual, pred))
    mae = mean_absolute_error(actual, pred)
    r2 = r2_score(actual, pred)
    return rmse, mae, r2

warnings.filterwarnings("ignore")
np.random.seed(40)

# Read the wine-quality csv file from the URL
csv_url = (
    "./winequality-red.csv"
)
try:
    data = pd.read_csv(csv_url, sep=";")
except Exception as e:
    logger.exception(
        "Unable to download training & test CSV, check your internet connection. Error: %s", e
    )

train, test = train_test_split(data)

train_x = train.drop(["quality"], axis=1)
test_x = test.drop(["quality"], axis=1)
train_y = train[["quality"]]
test_y = test[["quality"]]

alpha = 0.5
l1_ratio = 0.5


with mlflow.start_run():
    lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
    lr.fit(train_x, train_y)

    predicted_qualities = lr.predict(test_x)

    (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)

    print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio))
    print("  RMSE: %s" % rmse)
    print("  MAE: %s" % mae)
    print("  R2: %s" % r2)

    mlflow.log_param("alpha", alpha)
    mlflow.log_param("l1_ratio", l1_ratio)
    mlflow.log_metric("rmse", rmse)
    mlflow.log_metric("r2", r2)
    mlflow.log_metric("mae", mae)

    tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme

    if tracking_url_type_store != "file":
        mlflow.sklearn.log_model(lr, "model", registered_model_name="ElasticnetWineModel2")
    else:
        mlflow.sklearn.log_model(lr, "model")

I definitely recommend to use git versioned MLflow projects instead of running code directly from jupyter because
MLflow model registry will keep the git commit hash used for the run which will help to reproduce the results.

MLOps

Now I’d like to propose the process of building and deploying ML models.

Training

As described before the model is prepared and trained by the analyst which works in the Jupyter workspace and logs metrics and model to the MLflow tracking and model registry.

MLflow UI

Senior Analyst (currently the MLflow doesn’t support roles assignment) checks the model metrics and decides to promote it to Staging/Production stage in MLflow UI.

Model promotion

We will create additional application which will track the changes in the MLflow registry and initialize the deployment process.

The on each MLflow registry change the python application will check the database, prepare and commit k8s deployments and upload models artifacts to minio.

Because the applications commits the deployments to git repository we need to generate ssh keys:

ssh-keygen

and store them as a secrets:

kubectl create secret generic ssh-key-secret --from-file=id_rsa=./id_rsa --from-file=id_rsa.pub=./id_rsa.pub -n qooba

Now we can deploy the application:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: mlflowwatch
  namespace: qooba
spec:
  replicas: 1
  selector:
    matchLabels:
      app: mlflowwatch
      version: v1
  template:
    metadata:
      labels:
        app: mlflowwatch
        version: v1
    spec:
      containers:
      - image: qooba/mlflow:watchdog
        imagePullPolicy: IfNotPresent
        name: mlflowwatch
        command: ["/mlflow/start-watch.sh"]
        env:
        - name: GIT_REPO_URL
          value: ...
        - name: GIT_REPO_IP
          value: ...
        - name: BUCKET_NAME
          value: qooba
        - name: AWS_ACCESS_KEY_ID
          value: minio
        - name: AWS_SECRET_ACCESS_KEY
          value: minio123
        - name: MLFLOW_S3_ENDPOINT_URL
          value: http://minio.qooba.svc.cluster.local:9000
        ports:
        - containerPort: 5000
        volumeMounts:
          - mountPath: /mlflow
            name: mlflow
          - mountPath: /dev/shm
            name: dshm
          - mountPath: /etc/ssh-key
            name: ssh-key-secret-volume
            readOnly: true
      volumes:
        - name: mlflow
          persistentVolumeClaim:
            claimName: mlflow
        - emptyDir:
            medium: Memory
          name: dshm
        - name: ssh-key-secret-volume
          secret:
            defaultMode: 256
            secretName: ssh-key-secret

start-watch.sh:

#!/bin/bash
watchmedo shell-command --patterns='*.db' --recursive --wait --command='/mlflow/watch.sh' /mlflow

watch.sh

#!/bin/bash
cd /mlflow

if [ ! -d "/root/.ssh" ] 
then
  cp -r /etc/ssh-key /root/.ssh
  chmod -R 700 /root/.ssh

  ssh-keygen -R $GIT_REPO_URL
  ssh-keygen -R $GIT_REPO_IP
  ssh-keygen -R $GIT_REPO_URL,$GIT_REPO_IP
  ssh-keyscan -H $GIT_REPO_URL,$GIT_REPO_IP >> ~/.ssh/known_hosts
  ssh-keyscan -H $GIT_REPO_IP >> ~/.ssh/known_hosts
  ssh-keyscan -H $GIT_REPO_URL >> ~/.ssh/known_hosts

  git config --global user.name "mlflowwatch"
  git config --global user.email "mlflowwatch@qooba.net"
  git branch --set-upstream-to=origin/master master

fi

python3 /mlflow/watch.py

git add .
git commit -a -m "mlflow autocommit"
git push origin master

watch.py:

import os
import jinja2
import sqlite3
from collections import defaultdict
import boto3
import botocore

class Watcher:

    def __init__(self):
        self._model_deployment=ModelDeployment()
        self._model_registry=ModelRegistry()
        self._model_store=ModelStore()


    def process(self):
        model_groups = self._model_registry.models_info()
        for model_name, models_data in model_groups.items():
            print(f'{model_name}:')
            for model_data in models_data:
                print(f"- stage: {model_data['stage']}")
                print(f"  path: {model_data['path']}")
                self._model_deployment.generate_deployment(model_name, model_data)
                self._model_store.upload_model(model_data)


class ModelDeployment:

    def __init__(self):
        self._create_dir('deployments')
        self._template=self._prepare_template()

    def generate_deployment(self, model_name, model_data):
        self._create_dir(f'deployments/{model_name}')
        stage = model_data['stage'].lower()
        path = model_data['path'].replace('/mlflow/mlruns','s3://qooba/mlflow')
        self._create_dir(f'deployments/{model_name}/{stage}')
        outputText = self._template.render(model=path)
        with open(f'deployments/{model_name}/{stage}/deployment.yaml','w') as f:
            f.write(outputText)

    def _create_dir(self, directory):    
        if not os.path.exists(directory):
            os.makedirs(directory)

    def _prepare_template(self):
        templateLoader = jinja2.FileSystemLoader(searchpath="./")
        templateEnv = jinja2.Environment(loader=templateLoader)
        return templateEnv.get_template("deployment.yaml")

class ModelRegistry:

    def __init__(self):
        self._conn = sqlite3.connect('/mlflow/mlflow.db')

    def models_info(self):
        models=self._conn.execute("SELECT distinct name, version, current_stage, source FROM model_versions where current_stage in ('Staging','Production') order by version desc;").fetchall()
        res=defaultdict(list)

        for s in models:
            res[s[0].lower()].append({"tag": str(s[1]), "stage": s[2], "path": s[3]})

        return dict(res)


class ModelStore:

    def __init__(self):
        self._bucket_name=os.environ['BUCKET_NAME']
        self._s3=self._create_s3_client()
        self._create_bucket(self._bucket_name)

    def upload_model(self, model_data):  
        path = model_data['path']
        s3_path = path.replace('/mlflow/mlruns','mlflow')
        try:
            self._s3.head_object(Bucket=self._bucket_name, Key=f'{s3_path}/MLmodel')
        except botocore.errorfactory.ClientError as e:
            files = [(f'{path}/{f}',f'{s3_path}/{f}') for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]
            for file in files:
                self._s3.upload_file(file[0], self._bucket_name, file[1])

    def _create_s3_client(self):
        return boto3.client('s3',
                  aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"],
                  aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"],
                  endpoint_url=os.environ["MLFLOW_S3_ENDPOINT_URL"])

    def _create_bucket(self, bucket_name):
        try:
            self._s3.head_bucket(Bucket=bucket_name)
        except botocore.client.ClientError as e:
            self._s3.create_bucket(Bucket=bucket_name)


if __name__ == "__main__":
    Watcher().process()

The model deployments will be prepared using the template:
deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: mlflow-t1
  namespace: qooba
spec:
  replicas: 1
  selector:
    matchLabels:
      app: mlflow-t1
      version: v1
  template:
    metadata:
      labels:
        app: mlflow-t1
        version: v1
    spec:
      containers:
      - image: qooba/mlflow:serving
        imagePullPolicy: IfNotPresent
        name: mlflow-t1
        env:
        - name: AWS_ACCESS_KEY_ID
          value: minio
        - name: AWS_SECRET_ACCESS_KEY
          value: minio123
        - name: MLFLOW_S3_ENDPOINT_URL
          value: http://minio.qooba.svc.cluster.local:9000
        - name: MODEL
          value: {{model}}
        ports:
        - containerPort: 5000
        volumeMounts:
          - mountPath: /dev/shm
            name: dshm
      volumes:
        - emptyDir:
            medium: Memory
          name: dshm

If the model is promoted to the Staging/Production the process prepares the deployment yaml and uploads model to S3 store.

We will use minio as a S3 model store.

minio.yaml:

apiVersion: v1
kind: ServiceAccount
metadata:
  name: minio
  namespace: qooba
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: minio-pv-claim
  namespace: qooba
spec:
  accessModes:
  - ReadWriteOnce
  resources:
    requests:
      storage: 10Gi
  storageClassName: managed-nfs-storage
  volumeMode: Filesystem
---
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: minio
    namespace: qooba
  name: minio
  namespace: qooba
spec:
  progressDeadlineSeconds: 600
  replicas: 1
  revisionHistoryLimit: 10
  selector:
    matchLabels:
      app: minio
      namespace: qooba
  template:
    metadata:
      labels:
        app: minio
        namespace: qooba
    spec:
      serviceAccountName: minio
      containers:
      - args:
        - server
        - /data
        env:
        - name: MINIO_ACCESS_KEY
          value: minio
        - name: MINIO_SECRET_KEY
          value: minio123
        image: minio/minio:RELEASE.2018-02-09T22-40-05Z
        imagePullPolicy: IfNotPresent
        name: minio
        ports:
        - containerPort: 9000
          protocol: TCP
        resources: {}
        terminationMessagePath: /dev/termination-log
        terminationMessagePolicy: File
        volumeMounts:
        - mountPath: /data
          name: data
          subPath: minio
      volumes:
      - name: data
        persistentVolumeClaim:
          claimName: minio-pv-claim
---
apiVersion: v1
kind: Service
metadata:
  labels:
    app: minio
    namespace: qooba
  name: minio
  namespace: qooba
spec:
  ports:
  - port: 9000
    protocol: TCP
    targetPort: 9000
  selector:
    app: minio
    namespace: qooba

ArgoCD

No it is time to setup ArgoCD which will sync the Git deployments changes with Kubernetes configuration and automatically deploy newly promoted models.

To deploy MLflow models we will use docker image

qooba/mlflow:serving

FROM continuumio/miniconda3
RUN pip install mlflow==1.11.0 cloudpickle==1.6.0 scikit-learn==0.23.2 gevent boto3
ENV GUNICORN_CMD_ARGS="--timeout 60 -k gevent"
WORKDIR /opt/mlflow
ENV PORT=5000
ENV WORKER_NUMBER=4
CMD mlflow models serve -m $MODEL -h 0.0.0.0 -p $PORT -w $WORKER_NUMBER --no-conda

and configuration:
mlflow.serving.yaml:

apiVersion: v1
kind: Service
metadata:
  name: mlflow-t1
  namespace: qooba
  labels:
    app: mlflow-t1
spec:
  ports:
  - name: http
    port: 5000
    targetPort: 5000
  selector:
    app: mlflow-t1
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mlflow-t1
  namespace: qooba
spec:
  replicas: 1
  selector:
    matchLabels:
      app: mlflow-t1
      version: v1
  template:
    metadata:
      labels:
        app: mlflow-t1
        version: v1
    spec:
      containers:
      - image: qooba/mlflow:serving
        imagePullPolicy: IfNotPresent
        name: mlflow-t1
        env:
        - name: AWS_ACCESS_KEY_ID
          value: minio
        - name: AWS_SECRET_ACCESS_KEY
          value: minio123
        - name: MLFLOW_S3_ENDPOINT_URL
          value: http://minio.qooba.svc.cluster.local:9000
        - name: MODEL
          value: s3://qooba/mlflow/1/e0167f65abf4429b8c58f56b547fe514/artifacts/model
        ports:
        - containerPort: 5000
        volumeMounts:
          - mountPath: /dev/shm
            name: dshm
      volumes:
        - emptyDir:
            medium: Memory
          name: dshm
---
apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
  name: mlflow-t1
  namespace: qooba
spec:
  hosts:
  - "*"
  gateways:
  - qooba/mlflow-serving-gateway
  http:
  - match:
    - uri:
        prefix: /serving/qooba/t1
    rewrite:
        uri: /
    route:
    - destination:
        port:
          number: 5000
        host: mlflow-t1
---
apiVersion: networking.istio.io/v1alpha3
kind: Gateway
metadata:
  name: mlflow-serving-gateway
  namespace: qooba
spec:
  selector:
    istio: ingressgateway
  servers:
  - hosts:
    - '*'
    port:
      name: http
      number: 5000
      protocol: HTTP

Each time new model is promoted the ArgoCD applies new deployment with the new model s3 path:

- name: MODEL
  value: s3://qooba/mlflow/1/e0167f65abf4429b8c58f56b547fe514/artifacts/model

Inference services

Finally we can access model externally and generate predictions. Please note that in article the model is deployed in the same k8s namespace (in real solution model will be deployed on the separate k8s cluster) thus to access the model I have to send authservice_session otherwise request will redirected to the dex login page.

import json
import requests
import getpass

authservice_session = getpass.getpass()

headers={
    'Cookie': f'authservice_session={authservice_session}',
    'Content-Type': 'application/json'
}

data={
    "columns": ["fixed acidity","volatile acidity","citric acid","residual sugar","chlorides","free sulfur dioxide","total sulfur dioxide","density","pH","sulphates","alcohol"],
    "data": [[7.4,0.7,0,1.9,0.076,11,34,0.9978,3.51,0.56,9.4],
[7.8,0.88,0,2.6,0.098,25,67,0.9968,3.2,0.68,9.8]]
}

url='http://qooba-ai:31382/serving/qooba/t1/invocations'
requests.post(url, headers=headers,data=json.dumps(data)).text

# Response: [5.576883967129615, 5.50664776916154]

Jupyter Notebook is one of the most useful tool for data exploration, machine learning and fast prototyping. There are many plugins and projects which make it even more powerful:
* jupyterlab-git
* nbdev
* jupyter debugger

But sometimes you simply need IDE …

One of my favorite text editor is vim. It is lightweight, fast and with appropriate plugins it can be used as a IDE.
Using Dockerfile you can build jupyter environment with fully equipped vim:

FROM continuumio/miniconda3
RUN apt update && apt install curl git cmake ack g++ python3-dev vim-youcompleteme tmux -yq
RUN sh -c "$(curl -fsSL https://raw.githubusercontent.com/qooba/vim-python-ide/master/setup.sh)"
RUN conda install xeus-python jupyterlab jupyterlab-git -c conda-forge
RUN jupyter labextension install @jupyterlab/debugger @jupyterlab/git
RUN pip install nbdev
RUN echo "alias ls='ls --color=auto'" >> /root/.bashrc
CMD bin/bash

Now you can run the image:

docker run --name jupyter -d --rm -p 8888:8888 -v $(pwd)/jupyter:/root/.jupyter -v $(pwd)/notebooks:/opt/notebooks qooba/miniconda3 /bin/bash -c "jupyter lab --notebook-dir=/opt/notebooks --ip='0.0.0.0' --port=8888 --no-browser --allow-root --NotebookApp.password='' --NotebookApp.token=''"

In the jupyter lab start terminal session, run bash (it works better in bash) and then vim.
The online IDE is ready:

References

[1] Top image Boskampi from Pixabay

With big pleasure I would like to invite you to join Azuronet – .NET & Azure Meetup #2 in Warsaw, where I will talk (in polish) about Milla project and give you some insights into the world of chatbots and intelligent assistants.

I am pleased to hear that the first Polish banking chatbot with which you can make a transfer was awarded in a competition organized by a Gazeta Bankowa. With Milla you can talk in the Bank Millennium mobile application. Currently, Milla can speak (text to speech), listen (automatic speak recognition) and understand what you write to her (intent detection with slot filling).

This is not a sponsored post 🙂 but I’ve been developing Milla for the few months and I’m really happy that I had opportunity to do this.

Have a nice talk with Milla.