orient_express 0.4.1

A library to simplify model deployment to Vertex AI

Orient Express

A library to accelerate model deployments to Vertex AI directly from colab notebooks

Installation

pip install orient_express

Example

Train Model

Train a regular model. In the example below, it's xgboost model, trained on the Titanic dataset.

# Import necessary libraries
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer

# Load the Titanic dataset
data = sns.load_dataset('titanic').dropna(subset=['survived'])  # Dropping rows with missing target labels

# Select features and target
X = data[['pclass', 'sex', 'age', 'sibsp', 'parch', 'fare', 'embarked']]
y = data['survived']

# Define preprocessing for numeric columns (impute missing values and scale features)
numeric_features = ['age', 'fare', 'sibsp', 'parch']
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())
])

# Define preprocessing for categorical columns (impute missing values and one-hot encode)
categorical_features = ['pclass', 'sex', 'embarked']
categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))
])

# Combine preprocessing steps
preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features),
        ('cat', categorical_transformer, categorical_features)
    ])

# Create a pipeline that first transforms the data, then trains an XGBoost model
model = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('classifier', xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss'))
])

# Split the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model.fit(X_train, y_train)

Upload Model To Model Registry

model_wrapper  = ModelExpress(model=model,
                             project_name='my-project-name',
                             region='us-central1',
                             bucket_name='my-artifacts-bucket',
                             model_name='titanic')
model_wrapper.upload()

Local Inference (Without Online Prediction Endpoint)

The following code will download the last model from the model registry and run the inference locally.

# create input dataframe
titanic_data = {
    "pclass": [1],          # Passenger class (1st, 2nd, 3rd)
    "sex": ["female"],      # Gender
    "age": [29],            # Age
    "sibsp": [0],           # Number of siblings/spouses aboard
    "parch": [0],           # Number of parents/children aboard
    "fare": [100.0],        # Ticket fare
    "embarked": ["S"]       # Port of Embarkation (C = Cherbourg, Q = Queenstown, S = Southampton)
}
input_df = pd.DataFrame(titanic_data)

# init the model wrapper
model_wrapper  = ModelExpress(project_name='my-project-name',
                             region='us-central1',
                             model_name='titanic')

# Run inference locally
# It will download the most recent version from the model registry automatically
model_wrapper.local_predict(input_df)

Pin Model Version

In many cases, the pipeline should be pinned to a specific model version so the model can only be updated explicitly. Just pass a model_version parameter when instantiating the ModelExpress wrapper.

# init the model wrapper
model_wrapper  = ModelExpress(project_name='my-project-name',
                             region='us-central1',
                             model_name='titanic',
                             model_version=11)

Remote Inference (With Online Prediction Endpoint)

Make sure the model is deployed:

model_wrapper  = ModelExpress(model=model,
                             project_name='my-project-name',
                             region='us-central1',
                             bucket_name='my-artifacts-bucket',
                             model_name='titanic')

# upload the version to the registry and deploy it to the endpoint
model_wrapper.deploy()

Run inference with remote_predict method. It will make a remote call to the endpoint without fetching the model locally.

titanic_data = {
    "pclass": [1],             # Passenger class (1st, 2nd, 3rd)
    "sex": ["female"],         # Gender
    "age": [29],               # Age
    "sibsp": [0],              # Number of siblings/spouses aboard
    "parch": [0],              # Number of parents/children aboard
    "fare": [100.0],           # Ticket fare
    "embarked": ["S"]          # Port of Embarkation (C = Cherbourg, Q = Queenstown, S = Southampton)
}
df = pd.DataFrame(titanic_data)

model_wrapper.remote_predict(df)

Pipeline Deployment Function

Orient express library also have a helper function to simplify Vertex AI pipeline deployment.

Create deploy.py script

from orient_express.deployment import deploy_pipeline

import argparse
import conf

from pipeline import pipeline
from orient_express.deployment import deploy_pipeline


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--run-type", required=True)

    args = parser.parse_args()
    deploy_pipeline(run_type=args.run_type,
                    pipeline_dsl=pipeline,
                    pipeline_root=conf.PIPELINE_ROOT,
                    pipeline_name=conf.PIPELINE_NAME,
                    pipeline_display_name=conf.PIPELINE_DISPLAY_NAME,
                    pipeline_schedule_name=conf.SCHEDULE_NAME,
                    gcp_project=conf.PROJECT_ID,
                    gcp_location='us-central1',
                    gcp_service_account=conf.SERVICE_ACCOUNT,
                    gcp_network=conf.NETWORK_NAME,
                    gcp_labels={"team": "ml"})

And conf.py, make sure to replace the sample values with yours.

import os

BASE_PATH = "gs://pipelines-bucket/vertex-ai/pipelines"

PIPELINE_NAME = "my-pipeline"
PIPELINE_ROOT = f"{BASE_PATH}/{PIPELINE_NAME}"
PIPELINE_TEMP_ROOT = f"{BASE_PATH}/{PIPELINE_NAME}-temp"

PIPELINE_DISPLAY_NAME = "My Pipeline"
PIPELINE_DESCRIPTION = "My example pipeline"

NETWORK_NAME = "project network id"

DOCKER_IMAGE = "us-docker.pkg.dev/my-project/my-artifactory/my-pipeline:latest
BASE_IMAGE = "python:3.11"
PROJECT_ID = "my-project"
PROJECT_REGION = "us-central1"

SERVICE_ACCOUNT = "[e-mail]"
SCHEDULE_NAME = "My Pipeline"

For testing it on a local machine, make sure to authorize to GCP first

gcloud auth application-default login

Finally, run the pipeline (it will execute once)

python deploy.py --run-type single-run

Or, create a scheduler to run continuously

python deploy.py --run-type scheduled