Mastering Vertex AI API with Python: Your Complete Step-by-Step Guide
Hey there, aspiring AI enthusiast! Are you ready to unlock the incredible power of Google Cloud's Vertex AI directly from your Python code? If you've been curious about building, training, and deploying machine learning models with enterprise-grade capabilities, then you're in the right place. This comprehensive guide will walk you through every step of leveraging the Vertex AI API in Python, making your AI journey smoother and more efficient. Let's dive in!
Step 1: Setting the Stage – Your Google Cloud Environment
Before we write a single line of Python, we need to ensure our Google Cloud environment is properly set up. This is a crucial first step, so pay close attention!
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| How To Use Vertex Ai Api In Python |
1.1 Create a Google Cloud Project
If you don't already have one, head over to the
1.2 Enable Billing
Vertex AI services, while powerful, incur costs. You'll need to enable billing for your Google Cloud project. Google often offers free credits for new accounts, which is a great way to experiment!
1.3 Enable Necessary APIs
Within your Google Cloud project, navigate to "APIs & Services" -> "Enabled APIs & Services". You'll need to enable the following APIs:
Vertex AI API
Compute Engine API (often a prerequisite for Vertex AI's underlying infrastructure)
1.4 Install Google Cloud CLI (gcloud)
The gcloud CLI is your command-line interface to Google Cloud. It's essential for authentication and various administrative tasks.
Download and install
gcloudby following the instructionshere Once installed, initialize it:
gcloud initLog in with your Google account and select your newly created project:
gcloud auth loginSet up Application Default Credentials (ADC), which Python libraries use for authentication:
gcloud auth application-default loginThis command will open a browser window for you to authenticate your Google account. Your credentials will be stored locally.
Step 2: Preparing Your Python Environment
Now that your Google Cloud project is ready, let's get your local Python environment squared away.
2.1 Install Python
Ensure you have Python 3.7 or higher installed. You can download it from pip to your system's PATH.
2.2 Create a Virtual Environment (Highly Recommended!)
Using a virtual environment prevents dependency conflicts between your projects.
Tip: Slow down when you hit important details.
python -m venv vertex-ai-env
2.3 Activate Your Virtual Environment
On Windows:
Bash.\vertex-ai-env\Scripts\activateOn macOS/Linux:
Bashsource vertex-ai-env/bin/activate
You'll see (vertex-ai-env) prefixing your terminal prompt, indicating the virtual environment is active.
2.4 Install the Vertex AI SDK for Python
The Vertex AI SDK is a high-level library that simplifies interactions with the Vertex AI API. It also includes the lower-level client library.
pip install --upgrade google-cloud-aiplatform
This command will install all necessary dependencies.
Step 3: Authenticating Your Python Application
As mentioned, gcloud auth application-default login sets up Application Default Credentials, which is the most common way to authenticate for local development. However, for production environments or specific scenarios, you might use service accounts.
3.1 Using Application Default Credentials (ADC)
Once you've run gcloud auth application-default login, your Python code will automatically pick up these credentials when you initialize the Vertex AI SDK. This is the simplest and recommended method for local development.
3.2 Using Service Accounts (for Production/Automated Workflows)
For automated workflows or when deploying your application, using a service account is more secure and manageable.
Create a Service Account: Go to "IAM & Admin" -> "Service Accounts" in the Google Cloud Console and create a new service account. Grant it the necessary roles (e.g., "Vertex AI User", "Storage Object Admin" if you're working with data in Cloud Storage).
Generate a JSON Key: After creating the service account, click on it, go to "Keys" -> "ADD KEY" -> "Create new key" -> "JSON". Download this JSON file. Keep this file secure and do not commit it to version control!
Authenticate in Python using the JSON Key: You can set the
GOOGLE_APPLICATION_CREDENTIALSenvironment variable to the path of your JSON key file.Bashexport GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/service-account-key.json"Alternatively, you can explicitly pass the credentials:
Pythonfrom google.cloud import aiplatform from google.oauth2 import service_account # Replace with the path to your service account key file SERVICE_ACCOUNT_KEY_PATH = "/path/to/your/service-account-key.json" PROJECT_ID = "your-gcp-project-id" REGION = "us-central1" # Or your desired region credentials = service_account.Credentials.from_service_account_file(SERVICE_ACCOUNT_KEY_PATH) aiplatform.init(project=PROJECT_ID, location=REGION, credentials=credentials) print("Vertex AI initialized successfully with service account.")
Step 4: Initializing the Vertex AI SDK
With authentication set up, the next step in your Python script is to initialize the Vertex AI SDK. This establishes the connection to your specified Google Cloud project and region.
from google.cloud import aiplatform
# Replace with your actual project ID and region
PROJECT_ID = "your-gcp-project-id"
REGION = "us-central1" # Choose a region where Vertex AI is available
aiplatform.init(project=PROJECT_ID, location=REGION)
print(f"Vertex AI SDK initialized for project '{PROJECT_ID}' in region '{REGION}'.")
It's good practice to use environment variables for PROJECT_ID and REGION in real applications.
Step 5: Working with Data on Vertex AI
Before you can train models, you often need data. Vertex AI integrates seamlessly with Google Cloud Storage and other data sources.
5.1 Uploading Data to Google Cloud Storage (GCS)
Vertex AI often uses GCS for storing datasets, model artifacts, and other files.
Tip: Keep the flow, don’t jump randomly.
Using
gsutil(CLI):Bashgsutil cp /local/path/to/your/data.csv gs://your-bucket-name/data/data.csvUsing the Python Client Library:
Pythonfrom google.cloud import storage bucket_name = "your-bucket-name" source_file_name = "/local/path/to/your/data.csv" destination_blob_name = "data/data.csv" # Path within the bucket storage_client = storage.Client(project=PROJECT_ID) bucket = storage_client.bucket(bucket_name) blob = bucket.blob(destination_blob_name) blob.upload_from_filename(source_file_name) print(f"File {source_file_name} uploaded to {destination_blob_name} in bucket {bucket_name}.")
5.2 Creating a Managed Dataset in Vertex AI
Vertex AI allows you to create managed datasets, which provide versioning and easier integration with AutoML and custom training jobs.
from google.cloud import aiplatform
dataset_display_name = "my_iris_dataset"
gcs_source_uri = "gs://your-bucket-name/data/data.csv" # Must be in CSV format for tabular
# For tabular data
dataset = aiplatform.TabularDataset.create(
display_name=dataset_display_name,
gcs_source=[gcs_source_uri]
)
print(f"Tabular Dataset '{dataset.display_name}' created with ID: {dataset.name}")
# For other data types, use:
# aiplatform.ImageDataset.create(...)
# aiplatform.TextDataset.create(...)
# aiplatform.VideoDataset.create(...)
Step 6: Training Models on Vertex AI
Vertex AI offers several ways to train models: AutoML (no-code/low-code) and Custom Training (for more control).
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6.1 Using AutoML for Tabular Data (Simplified Training)
AutoML is fantastic for quickly building high-quality models without deep ML expertise.
from google.cloud import aiplatform
# Assuming 'dataset' was created in Step 5.2
# Or retrieve an existing dataset:
# dataset = aiplatform.TabularDataset(dataset_name="projects/PROJECT_ID/locations/REGION/datasets/DATASET_ID")
model_display_name = "my_automl_tabular_model"
target_column = "species" # Replace with your target column name
job = aiplatform.AutoMlTablesTrainingJob(
display_name=model_display_name,
optimization_prediction_type="classification", # or "regression"
column_transformations=[
# Define transformations for your columns (e.g., categorical, numeric)
# This is simplified; you'd define this based on your dataset
{"auto": {"column_name": "sepal_length"}},
{"auto": {"column_name": "sepal_width"}},
{"auto": {"column_name": "petal_length"}},
{"auto": {"column_name": "petal_width"}},
{"categorical": {"column_name": "species"}} # Example for classification target
]
)
model = job.run(
dataset=dataset,
target_column=target_column,
training_fraction_split=0.8,
validation_fraction_split=0.1,
test_fraction_split=0.1
)
print(f"AutoML training job started. Model will be '{model.display_name}'.")
# The job might take a while to complete. You can monitor its status in the GCP console.
Remember to adjust column_transformations and optimization_prediction_type according to your specific dataset and problem.
6.2 Performing Custom Training (Bring Your Own Code)
For more complex models or specific frameworks, custom training is your go-to. This typically involves packaging your training code.
6.2.1 Prepare Your Training Script
Create a Python script (e.g., trainer.py) that contains your model training logic. It should take arguments for input data paths, output model directory, etc.
# trainer.py (example for a simple scikit-learn model)
import argparse
import pandas as pd
from sklearn.linear_model import LogisticRegression
import joblib
import os
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--training_data_path", type=str, required=True)
parser.add_argument("--model_output_dir", type=str, required=True)
args = parser.parse_args()
# Load data (assuming CSV from GCS)
df = pd.read_csv(args.training_data_path)
X = df[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]
y = df['species']
# Train model
model = LogisticRegression(max_iter=1000)
model.fit(X, y)
# Save model
model_path = os.path.join(args.model_output_dir, "model.joblib")
joblib.dump(model, model_path)
print(f"Model saved to: {model_path}")
6.2.2 Create a Custom Training Job
You can run your training script on Vertex AI by creating a CustomTrainingJob. Vertex AI will handle provisioning the compute resources, running your code, and saving the model artifacts.
from google.cloud import aiplatform
model_display_name = "my_custom_sklearn_model"
training_script_path = "trainer.py"
gcs_training_data_uri = "gs://your-bucket-name/data/data.csv"
gcs_model_output_uri = "gs://your-bucket-name/models/my_custom_sklearn_model_output/"
job = aiplatform.CustomTrainingJob(
display_name=model_display_name,
script_path=training_script_path,
container_uri="us-docker.pkg.dev/vertex-ai/training/scikit-learn-cpu.0-23:latest", # Use a pre-built container or your own
requirements=["scikit-learn==0.23.2", "pandas"], # Add any Python package dependencies
model_serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-23:latest" # For deployment
)
model = job.run(
dataset=dataset, # Optional, but good for tracking lineage
model_display_name=model_display_name,
args=[
"--training_data_path", gcs_training_data_uri,
"--model_output_dir", gcs_model_output_uri
]
)
print(f"Custom training job started. Model will be '{model.display_name}'.")
Note the container_uri and model_serving_container_image_uri – these specify the Docker images that Vertex AI uses for training and serving your model, respectively.
Step 7: Deploying Your Model to an Endpoint
Once your model is trained, the next step is to deploy it to an endpoint so you can get online predictions.
7.1 Creating an Endpoint
An endpoint is a dedicated resource that hosts your model and serves predictions.
QuickTip: Read a little, pause, then continue.
from google.cloud import aiplatform
# If you have an existing model, load it:
# model = aiplatform.Model(model_name="projects/PROJECT_ID/locations/REGION/models/MODEL_ID")
endpoint_display_name = "my_model_endpoint"
endpoint = aiplatform.Endpoint.create(display_name=endpoint_display_name)
print(f"Endpoint '{endpoint.display_name}' created with ID: {endpoint.name}")
7.2 Deploying the Model to the Endpoint
Now, deploy your trained model to the created endpoint. You'll specify the machine type and the number of replicas.
from google.cloud import aiplatform
# Assuming 'model' and 'endpoint' were created in previous steps.
deployed_model_display_name = "my_model_deployment_version_1"
machine_type = "n1-standard-2" # Choose an appropriate machine type
min_replica_count = 1
max_replica_count = 1 # Adjust for scaling needs
model.deploy(
endpoint=endpoint,
deployed_model_display_name=deployed_model_display_name,
machine_type=machine_type,
min_replica_count=min_replica_count,
max_replica_count=max_replica_count,
sync=True # Wait for deployment to complete
)
print(f"Model '{model.display_name}' deployed to endpoint '{endpoint.display_name}'.")
Deployment can take several minutes to complete, as Vertex AI provisions resources.
Step 8: Getting Online Predictions
With your model deployed, you can now send data to the endpoint and receive predictions in real-time.
from google.cloud import aiplatform
# Assuming 'endpoint' is the deployed endpoint object.
# Or load an existing endpoint:
# endpoint = aiplatform.Endpoint(endpoint_name="projects/PROJECT_ID/locations/REGION/endpoints/ENDPOINT_ID")
# Example instance for prediction (based on the Iris dataset features)
# The format of instances depends on how your model expects input.
instances = [
[5.1, 3.5, 1.4, 0.2], # Example for Iris Setosa
[6.3, 3.3, 6.0, 2.5] # Example for Iris Virginica
]
# For tabular models, instances are usually lists of values.
# For custom models, you might need to convert to protobuf Value format.
prediction_response = endpoint.predict(instances=instances)
print("Predictions:")
for prediction in prediction_response.predictions:
print(prediction)
# If your model returns probabilities or specific classes, parse the response accordingly.
Step 9: Performing Batch Predictions
For large datasets where immediate responses aren't needed, batch predictions are more efficient.
from google.cloud import aiplatform
# Assuming 'model' is your trained model object.
# Or load an existing model:
# model = aiplatform.Model(model_name="projects/PROJECT_ID/locations/REGION/models/MODEL_ID")
input_gcs_uri = "gs://your-bucket-name/data/batch_prediction_input.csv" # CSV file with instances
output_gcs_uri_prefix = "gs://your-bucket-name/batch_predictions_output/"
batch_prediction_job = model.batch_predict(
job_display_name="my_batch_prediction_job",
instances_format="csv", # Or "jsonl", "tf-record", etc.
predictions_format="csv", # Or "jsonl"
gcs_source=[input_gcs_uri],
gcs_destination_prefix=output_gcs_uri_prefix,
machine_type="n1-standard-2",
starting_replica_count=1,
max_replica_count=10
)
print(f"Batch prediction job '{batch_prediction_job.display_name}' started.")
batch_prediction_job.wait() # Wait for the job to complete
print(f"Batch prediction job completed. Results in: {output_gcs_uri_prefix}")
# You can then download and inspect the prediction results from GCS.
Step 10: Cleaning Up Resources (Important!)
To avoid incurring unnecessary costs, always clean up your Vertex AI resources when you're done.
10.1 Undeploy Model from Endpoint
from google.cloud import aiplatform
# Assuming 'endpoint' and 'model' objects are available
# Or load them:
# endpoint = aiplatform.Endpoint(endpoint_name="projects/PROJECT_ID/locations/REGION/endpoints/ENDPOINT_ID")
# model = aiplatform.Model(model_name="projects/PROJECT_ID/locations/REGION/models/MODEL_ID")
# Undeploy all models from the endpoint
endpoint.undeploy_all()
print(f"All models undeployed from endpoint '{endpoint.display_name}'.")
# Alternatively, undeploy a specific deployed model:
# endpoint.undeploy(deployed_model_id="YOUR_DEPLOYED_MODEL_ID")
10.2 Delete Endpoint
from google.cloud import aiplatform
# Assuming 'endpoint' object is available
# Or load it:
# endpoint = aiplatform.Endpoint(endpoint_name="projects/PROJECT_ID/locations/REGION/endpoints/ENDPOINT_ID")
endpoint.delete()
print(f"Endpoint '{endpoint.display_name}' deleted.")
10.3 Delete Model
from google.cloud import aiplatform
# Assuming 'model' object is available
# Or load it:
# model = aiplatform.Model(model_name="projects/PROJECT_ID/locations/REGION/models/MODEL_ID")
model.delete()
print(f"Model '{model.display_name}' deleted.")
10.4 Delete Dataset
from google.cloud import aiplatform
# Assuming 'dataset' object is available
# Or load it:
# dataset = aiplatform.TabularDataset(dataset_name="projects/PROJECT_ID/locations/REGION/datasets/DATASET_ID")
dataset.delete()
print(f"Dataset '{dataset.display_name}' deleted.")
Note: Deleting resources in the correct order (e.g., undeploying before deleting endpoints) is often necessary.
Frequently Asked Questions (FAQs)
How to set up my Google Cloud project for Vertex AI?
Tip: Read slowly to catch the finer details.
You need to create a Google Cloud project, enable billing, and enable the Vertex AI API and Compute Engine API within that project. Use the Google Cloud Console for these steps.
How to authenticate my Python application with Vertex AI?
For local development, the simplest way is to use gcloud auth application-default login. For production, use a Google Cloud service account with appropriate permissions, either by setting the GOOGLE_APPLICATION_CREDENTIALS environment variable or by explicitly passing credentials in your Python code.
How to upload data for Vertex AI training?
The most common method is to upload your data to Google Cloud Storage (GCS). You can do this using the gsutil command-line tool or the google-cloud-storage Python client library. Once in GCS, you can create a managed dataset in Vertex AI from the GCS URI.
How to train a model using AutoML in Vertex AI Python SDK?
After creating a TabularDataset (or other dataset type), you can use classes like aiplatform.AutoMlTablesTrainingJob to define and run an AutoML training job, specifying the dataset, target column, and optimization type.
How to perform custom model training on Vertex AI?
You'll need a Python training script that can run on a Docker container. Use aiplatform.CustomTrainingJob to specify your script, a base container image (pre-built or custom), and any required arguments. Vertex AI will handle the infrastructure.
How to deploy a trained model to an endpoint for online predictions?
First, create an aiplatform.Endpoint. Then, use the model.deploy() method, passing the endpoint object, a display name for the deployment, machine type, and replica counts.
How to get online predictions from a deployed Vertex AI model?
Once your model is deployed to an endpoint, you can use the endpoint.predict() method, passing a list of instances (your input data) in the format your model expects.
How to perform batch predictions with Vertex AI?
Use the model.batch_predict() method, specifying the input data format (e.g., 'csv', 'jsonl'), the GCS source URI of your input data, and a GCS destination prefix for the prediction results.
How to manage and monitor my Vertex AI training jobs?
You can monitor training jobs directly in the Google Cloud Console under the Vertex AI section ("Training" or "Pipelines"). The SDK methods often return objects that you can use to check status, or you can retrieve job details by ID.
How to clean up Vertex AI resources to avoid billing charges?
It's crucial to undeploy models from endpoints (endpoint.undeploy_all()), then delete endpoints (endpoint.delete()), and finally delete models (model.delete()) and datasets (dataset.delete()) when they are no longer needed. Always check your Google Cloud Console for active resources.
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