Overview

In this tutorial, you will learn how to configure deployed Applications.

By the end of this tutorial you will know how to:

• Train and upload an example model version​

• Create a Deployment Configuration​

• Create an Application from the uploaded model version with previously created deployment configuration

• Examine settings of a Kubernetes cluster

Prerequisites

• ​Hydrosphere platform installed in Kubernetes cluster​

• ​Python SDK / CLI​

Upload a Model

In this section, we describe the resources required to create and upload an example model used in further sections. If you have no prior experience with uploading models to the Hydrosphere platform we suggest that you visit the Getting Started Tutorial.

Here are the resources used to train sklearn.ensemble.GradientBoostingClassifier and upload it to the Hydrosphere cluster.

numpy~=1.18
scipy==1.4.1
scikit-learn~=0.23
pandas==1.3.1

serving.yaml
kind: Model
name: my-model
runtime: hydrosphere/serving-runtime-python-3.7:3.0.0
install-command: pip install -r requirements.txt
payload:
  - src/
  - requirements.txt
  - model.joblib
contract:
  name: infer
  inputs:
    x:
      shape: [30]
      type: double
      profile: numerical
  outputs:
    y:
      shape: scalar
      type: int64
      profile: numerical

train.py
import joblib
import pandas as pd
from sklearn.datasets import make_blobs
from sklearn.ensemble import GradientBoostingClassifier
​
# initialize data
X, y = make_blobs(n_samples=3000, n_features=30)
​
# create a model
model = GradientBoostingClassifier(n_estimators=200)
model.fit(X, y)
​
# Save training data and model
pd.DataFrame(X).to_csv("training_data.csv", index=False)
joblib.dump(model, "model.joblib")

func_main.py
import joblib
import numpy as np
​
# Load model once
model = joblib.load("/model/files/model.joblib")
​
​
def infer(x):
    # Make a prediction
    y = model.predict(x[np.newaxis])
​
    # Return the scalar representation of y
    return {"y": np.asscalar(y)}

Our folder structure should look like this:

dep_config_tutorial
├── model.joblib
├── train.py
├── requirements.txt
├── serving.yaml
└── src
    └── func_main.py

After we have made sure that all files are placed correctly, we can upload the model to the Hydrosphere platform by running hs apply from the command line.

hs apply -f serving.yaml

Create a Deployment Configuration

Next, we are going to create and upload an instance of Deployment Configuration to the Hydrosphere platform.

Deployment Configurations describe with which Kubernetes settings Hydrosphere should deploy servables. You can specify Pod Affinity and Tolerations, the number of desired pods in deployment, ResourceRequirements, and Environment Variables for the model container, and HorizontalPodAutoScaler settings.

Created Deployment Configurations can be attached to Servables and Model Variants inside of Application.

Deployment Configurations are immutable and cannot be changed after they've been uploaded to the Hydrosphere platform.

You can create and upload Deployment Configuration to Hydrosphere via YAML Resource definition or via Python SDK.

For this tutorial, we'll create a deployment configuration with 2 initial pods per deployment, HPA, and FOO environment variable with value bar.

deployment_configuration.yaml
kind: DeploymentConfiguration
name: my-dep-config
deployment:
  replicaCount: 2
hpa:
  minReplicas: 2
  maxReplicas: 4
  cpuUtilization: 70
container:
  env:
    FOO: bar

hs apply -f deployment_configuration.yaml

from hydrosdk import Cluster, DeploymentConfigurationBuilder
​
cluster = Cluster("http://localhost")
​
dep_config_builder = DeploymentConfigurationBuilder("my-dep-config-sdk")
dep_config = dep_config_builder \
    .with_replicas(replica_count=2) \
    .with_env({"FOO":"bar"}) \
    .with_hpa(max_replicas=4,
             min_replicas=2,
             target_cpu_utilization_percentage=70).build(cluster)

Create an Application

application.yaml
kind: Application
name: my-app-with-config
singular:
  model: my-model:1
  deployment-config: my-dep-config

hs apply -f application.yaml

from application import ApplicationBuilder, ExecutionStageBuilder
from hydrosdk import ModelVersion, Cluster, DeploymentConfiguration
​
cluster = Cluster('http://localhost')
my_model = ModelVersion.find(cluster, "my-model", 1)
my_config = DeploymentConfiguration.find(cluster, "my-dep-config")
​
stage = ExecutionStageBuilder().with_model_variant(model_version=my_model,
                                                   weight=100,
                                                   deployment_configuration=my_config).build()
​
app = ApplicationBuilder("my-app-with-config-sdk").with_stage(stage).build(cluster)

Examine Kubernetes Settings

Replicas

You can check whether with_replicas was successful by calling kubectl get deployment -A -o wide and checking the READYcolumn.

HPA

To check whether with_hpa was successful you should get a list of all created Horizontal Pod Autoscaler Resources. You can do so by calling kubectl get hpa -A

The output is similar to:

NAME                        REFERENCE                                            TARGETS    MINPODS   MAXPODS   REPLICAS   AGE
my-model-1-tumbling-star    CrossVersionObjectReference/my-model-1-tumbling-star 20%/70%    2         4         2          1d

Environment Variables

To list all environment variables run kubectl exec my-model-1-tumbling-star -it /bin/bash and then execute the printenv command which prints ann system variables.

The output is similar to:

MY_MODEL_1_TUMBLING_STAR_SERVICE_PORT_GRPC=9091
...
FOO=bar