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Using Deployment Configurations

Estimated completion time: 11m.

This tutorial is relevant only for Kubernetes installation of Hydrosphere. Please refer to How to Install Hydrosphere on Kubernetes cluster.

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

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.

requirements.txt is a list of Python dependencies used during the process of building model image.

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

serving.yaml is a resource definition that describes how model should be built and uploaded to Hydrosphere platform.

serving.yaml

kind: Model
name: my-model
runtime: hydrosphere/serving-runtime-python-3.7:2.3.2
install-command: pip install -r requirements.txt
payload:
  - src/
  - requirements.txt
  - model.joblib
contract:
  name: infer
  inputs:
    x:
      shape: [30]
      type: double
  outputs:
    y:
      shape: scalar
      type: int64

train.py is used to generate a model.joblib which is loaded from func_main.py during model serving.

Run python train.py to generate model.joblib

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 is a script which serves requests and produces responses.

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

Do not forget to run python train.py to generate model.joblib!

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

hs upload

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.

Create the deployment configuration resource definition:

deployment_configuration.yaml

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

To upload it to the Hydrosphere platform, run:

hs apply -f deployment_configuration.yaml

from hydrosdk import Cluster, DeploymentConfigurationBuilder

cluster = Cluster("http://localhost")

dep_config_builder = DeploymentConfigurationBuilder("my-dep-config", cluster)
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()

Create an Application

Create the application resource definition:

application.yaml

kind: Application
name: my-app-with-config
pipeline:
  - - model: my-model:1
      weight: 100
      deploymentConfiguartion: my-config

To upload it to the Hydrosphere platform, run:

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-config")

stage = ExecutionStageBuilder().with_model_variant(model_version=my_model,
                                                   weight=100,
                                                   deployment_configuration=my_config).build()

app = ApplicationBuilder(cluster, "my-app-with-config").with_stage(stage).build()

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

Using Deployment Configurations

Estimated completion time: 11m.

This tutorial is relevant only for Kubernetes installation of Hydrosphere. Please refer to How to Install Hydrosphere on Kubernetes cluster.

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

Upload a Model

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

requirements.txt is a list of Python dependencies used during the process of building model image.

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

serving.yaml is a resource definition that describes how model should be built and uploaded to Hydrosphere platform.

serving.yaml

kind: Model
name: my-model
runtime: hydrosphere/serving-runtime-python-3.7:2.3.2
install-command: pip install -r requirements.txt
payload:
  - src/
  - requirements.txt
  - model.joblib
contract:
  name: infer
  inputs:
    x:
      shape: [30]
      type: double
  outputs:
    y:
      shape: scalar
      type: int64

train.py is used to generate a model.joblib which is loaded from func_main.py during model serving.

Run python train.py to generate model.joblib

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 is a script which serves requests and produces responses.

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

Do not forget to run python train.py to generate model.joblib!

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

hs upload

Create a Deployment Configuration

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

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.

Create the deployment configuration resource definition:

deployment_configuration.yaml

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

To upload it to the Hydrosphere platform, run:

hs apply -f deployment_configuration.yaml

from hydrosdk import Cluster, DeploymentConfigurationBuilder

cluster = Cluster("http://localhost")

dep_config_builder = DeploymentConfigurationBuilder("my-dep-config", cluster)
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()

Create an Application

Create the application resource definition:

application.yaml

kind: Application
name: my-app-with-config
pipeline:
  - - model: my-model:1
      weight: 100
      deploymentConfiguartion: my-config

To upload it to the Hydrosphere platform, run:

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-config")

stage = ExecutionStageBuilder().with_model_variant(model_version=my_model,
                                                   weight=100,
                                                   deployment_configuration=my_config).build()

app = ApplicationBuilder(cluster, "my-app-with-config").with_stage(stage).build()

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