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Creating a custom prediction routine with scikit-learn

Read on cloud.google.com View on GitHub

Beta

This is a beta release of custom prediction routines. This feature might be changed in backward-incompatible ways and is not subject to any SLA or deprecation policy.

Overview

This tutorial shows how to deploy a trained scikit-learn model to AI Platform and serve predictions using a custom prediction routine. This lets you customize how AI Platform responds to each prediction request.

In this example, you will use a custom prediction routine to preprocess prediction input by scaling it, and to postprocess prediction output by converting class numbers to label strings.

The tutorial walks through several steps:

  • Training a simple scikit-learn model locally (in this notebook)
  • Creating and deploy a custom prediction routine to AI Platform
  • Serving prediction requests from that deployment

Dataset

This tutorial uses R.A. Fisher's Iris dataset, a small dataset that is popular for trying out machine learning techniques. Each instance has four numerical features, which are different measurements of a flower, and a target label that marks it as one of three types of iris: Iris setosa, Iris versicolour, or Iris virginica.

This tutorial uses the copy of the Iris dataset included in the scikit-learn library.

Objective

The goal is to train a model that uses a flower's measurements as input to predict what type of iris it is.

This tutorial focuses more on using this model with AI Platform than on the design of the model itself.

Costs

This tutorial uses billable components of Google Cloud Platform (GCP):

  • AI Platform
  • Cloud Storage

Learn about AI Platform pricing and Cloud Storage pricing, and use the Pricing Calculator to generate a cost estimate based on your projected usage.

Before you begin

You must do several things before you can train and deploy a model in AI Platform:

  • Set up your local development environment.
  • Set up a GCP project with billing and the necessary APIs enabled.
  • Authenticate your GCP account in this notebook.
  • Create a Cloud Storage bucket to store your training package and your trained model.

Set up your local development environment

If you are using Colab or AI Platform Notebooks, your environment already meets all the requirements to run this notebook. You can skip this step.

Otherwise, make sure your environment meets this notebook's requirements. You need the following:

  • The Google Cloud SDK
  • Python 3
  • virtualenv
  • Jupyter notebook running in a virtual environment with Python 3

The Google Cloud guide to Setting up a Python development environment and the Jupyter installation guide provide detailed instructions for meeting these requirements. The following steps provide a condensed set of instructions:

  1. Install and initialize the Cloud SDK.

  2. Install Python 3.

  3. Install virtualenv and create a virtual environment that uses Python 3.

  4. Activate that environment and run pip install jupyter in a shell to install Jupyter.

  5. Run jupyter notebook in a shell to launch Jupyter.

  6. Open this notebook in the Jupyter Notebook Dashboard.

Set up your GCP project

The following steps are required, regardless of your notebook environment.

  1. Select or create a GCP project.

  2. Make sure that billing is enabled for your project.

  3. Enable the AI Platform ("Cloud Machine Learning Engine") and Compute Engine APIs.

  4. Enter your project ID in the cell below. Then run the cell to make sure the Cloud SDK uses the right project for all the commands in this notebook.

Note: Jupyter runs lines prefixed with ! as shell commands, and it interpolates Python variables prefixed with $ into these commands.





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PROJECT_ID = "<your-project-id>" #@param {type:"string"}
! gcloud config set project $PROJECT_ID

Authenticate your GCP account

If you are using AI Platform Notebooks, your environment is already authenticated. Skip this step.

If you are using Colab, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.

Otherwise, follow these steps:

  1. In the GCP Console, go to the Create service account key page.

  2. From the Service account drop-down list, select New service account.

  3. In the Service account name field, enter a name.

  4. From the Role drop-down list, select Machine Learning Engine > AI Platform Admin and Storage > Storage Object Admin.

  5. Click Create. A JSON file that contains your key downloads to your local environment.

  6. Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell.





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import sys

# If you are running this notebook in Colab, run this cell and follow the
# instructions to authenticate your GCP account. This provides access to your
# Cloud Storage bucket and lets you submit training jobs and prediction
# requests.

if 'google.colab' in sys.modules:
  from google.colab import auth as google_auth
  google_auth.authenticate_user()

# If you are running this notebook locally, replace the string below with the
# path to your service account key and run this cell to authenticate your GCP
# account.
else:
  %env GOOGLE_APPLICATION_CREDENTIALS '<path-to-your-service-account-key.json>'

Create a Cloud Storage bucket

The following steps are required, regardless of your notebook environment.

To deploy a custom prediction routine, you must upload your trained model artifacts and your custom code to Cloud Storage.

Set the name of your Cloud Storage bucket below. It must be unique across all Cloud Storage buckets.

You may also change the REGION variable, which is used for operations throughout the rest of this notebook. Make sure to choose a region where Cloud AI Platform services are available. You may not use a Multi-Regional Storage bucket for training with AI Platform.





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BUCKET_NAME = "<your-bucket-name>" #@param {type:"string"}
REGION = "us-central1" #@param {type:"string"}

Only if your bucket doesn't already exist: Run the following cell to create your Cloud Storage bucket.





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! gsutil mb -l $REGION gs://$BUCKET_NAME

Finally, validate access to your Cloud Storage bucket by examining its contents:





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! gsutil ls -al gs://$BUCKET_NAME

Building and training a scikit-learn model

Often, you can't use your data in its raw form to train a machine learning model. Even when you can, preprocessing the data before using it for training can sometimes improve your model.

Assuming that you expect the input for prediction to have the same format as your training data, you must apply identical preprocessing during training and prediction to ensure that your model makes consistent predictions.

In this section, create a preprocessing module and use it as part of training. Then export a preprocessor with characteristics learned during training to use later in your custom prediction routine.

Install dependencies for local training

Training locally (in the notebook) requires several dependencies:





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! pip install numpy>=1.16.0 scikit-learn==0.20.2

Write your preprocessor

Scaling training data so each numerical feature column has a mean of 0 and a standard deviation of 1 can improve your model.

Create preprocess.py, which contains a class to do this scaling:





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%%writefile preprocess.py
import numpy as np

class MySimpleScaler(object):
  def __init__(self):
    self._means = None
    self._stds = None

  def preprocess(self, data):
    if self._means is None: # during training only
      self._means = np.mean(data, axis=0)

    if self._stds is None: # during training only
      self._stds = np.std(data, axis=0)
      if not self._stds.all():
        raise ValueError('At least one column has standard deviation of 0.')

    return (data - self._means) / self._stds

Notice that an instance of MySimpleScaler saves the means and standard deviations of each feature column on first use. Then it uses these summary statistics to scale data it encounters afterward.

This lets you store characteristics of the training distribution and use them for identical preprocessing at prediction time.

Train your model

Next, use preprocess.MySimpleScaler to preprocess the iris data, then train a model using scikit-learn.

At the end, export your trained model as a joblib (.joblib) file and export your MySimpleScaler instance as a pickle (.pkl) file:





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import pickle

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.externals import joblib

from preprocess import MySimpleScaler

iris = load_iris()
scaler = MySimpleScaler()
X = scaler.preprocess(iris.data)
y = iris.target

model = RandomForestClassifier()
model.fit(X, y)

joblib.dump(model, 'model.joblib')
with open ('preprocessor.pkl', 'wb') as f:
  pickle.dump(scaler, f)

Deploying a custom prediction routine

To deploy a custom prediction routine to serve predictions from your trained model, do the following:

  • Create a custom predictor to handle requests
  • Package your predictor and your preprocessing module
  • Upload your model artifacts and your custom code to Cloud Storage
  • Deploy your custom prediction routine to AI Platform

Create a custom predictor

To deploy a custom prediction routine, you must create a class that implements the Predictor interface. This tells AI Platform how to load your model and how to handle prediction requests.

Write the following code to predictor.py:





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%%writefile predictor.py
import os
import pickle

import numpy as np
from sklearn.datasets import load_iris
from sklearn.externals import joblib

class MyPredictor(object):
  def __init__(self, model, preprocessor):
    self._model = model
    self._preprocessor = preprocessor
    self._class_names = load_iris().target_names

  def predict(self, instances, **kwargs):
    inputs = np.asarray(instances)
    preprocessed_inputs = self._preprocessor.preprocess(inputs)
    if kwargs.get('probabilities'):
      probabilities = self._model.predict_proba(preprocessed_inputs)
      return probabilities.tolist()
    else:
      outputs = self._model.predict(preprocessed_inputs)
      return [self._class_names[class_num] for class_num in outputs]

  @classmethod
  def from_path(cls, model_dir):
    model_path = os.path.join(model_dir, 'model.joblib')
    model = joblib.load(model_path)

    preprocessor_path = os.path.join(model_dir, 'preprocessor.pkl')
    with open(preprocessor_path, 'rb') as f:
      preprocessor = pickle.load(f)

    return cls(model, preprocessor)

Notice that, in addition to using the preprocessor that you defined during training, this predictor performs a postprocessing step that converts the prediction output from class indexes (0, 1, or 2) into label strings (the name of the flower type).

However, if the predictor receives a probabilities keyword argument with the value True, it returns a probability array instead, denoting the probability that each of the three classes is the correct label (according to the model). The last part of this tutorial shows how to provide a keyword argument during prediction.

Package your custom code

You must package predictor.py and preprocess.py as a .tar.gz source distribution package and provide the package to AI Platform so it can use your custom code to serve predictions.

Write the following setup.py to define your package:





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%%writefile setup.py
from setuptools import setup

setup(
    name='my_custom_code',
    version='0.1',
    scripts=['predictor.py', 'preprocess.py'])

Then run the following command to createdist/my_custom_code-0.1.tar.gz:





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! python setup.py sdist --formats=gztar

Upload model artifacts and custom code to Cloud Storage

Before you can deploy your model for serving, AI Platform needs access to the following files in Cloud Storage:

  • model.joblib (model artifact)
  • preprocessor.pkl (model artifact)
  • my_custom_code-0.1.tar.gz (custom code)

Model artifacts must be stored together in a model directory, which your Predictor can access as the model_dir argument in its from_path class method. The custom code does not need to be in the same directory. Run the following commands to upload your files:





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! gsutil cp ./dist/my_custom_code-0.1.tar.gz gs://$BUCKET_NAME/custom_prediction_routine_tutorial/my_custom_code-0.1.tar.gz
! gsutil cp model.joblib preprocessor.pkl gs://$BUCKET_NAME/custom_prediction_routine_tutorial/model/

Deploy your custom prediction routine

Create a model resource and a version resource to deploy your custom prediction routine. First define variables with your resource names:





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MODEL_NAME = 'IrisPredictor'
VERSION_NAME = 'v1'

Then create your model:





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! gcloud ai-platform models create $MODEL_NAME \
  --regions $REGION

Next, create a version. In this step, provide paths to the artifacts and custom code you uploaded to Cloud Storage:





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# --quiet automatically installs the beta component if it isn't already installed 
! gcloud --quiet beta ai-platform versions create $VERSION_NAME \
  --model $MODEL_NAME \
  --runtime-version 1.13 \
  --python-version 3.5 \
  --origin gs://$BUCKET_NAME/custom_prediction_routine_tutorial/model/ \
  --package-uris gs://$BUCKET_NAME/custom_prediction_routine_tutorial/my_custom_code-0.1.tar.gz \
  --prediction-class predictor.MyPredictor

Learn more about the options you must specify when you deploy a custom prediction routine.

Serving online predictions

Try out your deployment by sending an online prediction request. First, install the Google APIs Client Library for Python:





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! pip install --upgrade google-api-python-client

Then send two instances of iris data to your deployed version:





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import googleapiclient.discovery

instances = [
  [6.7, 3.1, 4.7, 1.5],
  [4.6, 3.1, 1.5, 0.2],
]

service = googleapiclient.discovery.build('ml', 'v1')
name = 'projects/{}/models/{}/versions/{}'.format(PROJECT_ID, MODEL_NAME, VERSION_NAME)

response = service.projects().predict(
    name=name,
    body={'instances': instances}
).execute()

if 'error' in response:
    raise RuntimeError(response['error'])
else:
  print(response['predictions'])

Note: This code uses the credentials you set up during the authentication step to make the online prediction request.

Sending keyword arguments

When you send a prediction request to a custom prediction routine, you can provide additional fields on your request body. The Predictor's predict method receives these as fields of the **kwargs dictionary.

The following code sends the same request as before, but this time it adds a probabilities field to the request body:





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response = service.projects().predict(
    name=name,
    body={'instances': instances, 'probabilities': True}
).execute()

if 'error' in response:
    raise RuntimeError(response['error'])
else:
  print(response['predictions'])

Cleaning up

To clean up all GCP resources used in this project, you can delete the GCP project you used for the tutorial.

Alternatively, you can clean up individual resources by running the following commands:





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# Delete version resource
! gcloud ai-platform versions delete $VERSION_NAME --quiet --model $MODEL_NAME 

# Delete model resource
! gcloud ai-platform models delete $MODEL_NAME --quiet

# Delete Cloud Storage objects that were created
! gsutil -m rm -r gs://$BUCKET_NAME/custom_prediction_routine_tutorial

What's next





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Content source: GoogleCloudPlatform/cloudml-samples

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