Are you ready to unlock the true potential of your machine learning models? Google Cloud's Vertex AI is a game-changer, providing a unified platform to build, deploy, and scale ML models with unprecedented ease and efficiency. Gone are the days of wrestling with disparate tools and complex infrastructure. Vertex AI streamlines the entire ML lifecycle, empowering data scientists and ML engineers to focus on what they do best: innovating.
This comprehensive guide will walk you through the process of training models in Vertex AI, from the very first spark of an idea to a fully deployed and performing model. Let's dive in!
Understanding Vertex AI Training Options
Before we jump into the steps, it's crucial to understand the different ways you can train a model in Vertex AI:
AutoML: This is your go-to if you have limited ML expertise or want to rapidly prototype models. AutoML automates much of the ML process, including data preparation, model selection, hyperparameter tuning, and deployment. You simply provide your data, and Vertex AI does the heavy lifting.
Custom Training: For those who need fine-grained control over their model architecture, training logic, and computational resources, custom training is the answer. You bring your own training code (in any ML framework like TensorFlow, PyTorch, scikit-learn, etc.), and Vertex AI provides the scalable infrastructure to run it. This guide will focus primarily on custom training, as it offers the most flexibility.
Ray on Vertex AI: For distributed computing and parallel processing, Ray on Vertex AI allows you to leverage the open-source Ray framework directly within the Vertex AI platform. This is ideal for complex distributed training scenarios.
Let's begin our journey into custom model training!
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| How To Train Model In Vertex Ai |
Step 1: Getting Your Google Cloud Environment Ready
Feeling excited to build? The first crucial step is to ensure your Google Cloud environment is properly set up. Think of this as preparing your workshop before you start building something amazing.
1.1. Set Up Your Google Cloud Project
Create a Project: If you don't already have one, create a new Google Cloud project. This acts as a container for all your Vertex AI resources.
Navigate to the Google Cloud Console.
Click on the project selector dropdown (usually at the top left) and select "New Project."
Give your project a meaningful name and click "Create."
Enable APIs: Vertex AI relies on several Google Cloud APIs. You need to enable them for your project.
In the Google Cloud Console, navigate to "APIs & Services" > "Enabled APIs & Services."
Click "+ Enable APIs and Services."
Search for and enable the following APIs:
Vertex AI API
Cloud Storage API
Artifact Registry API (if you plan to use custom container images)
1.2. Prepare Your Billing Account
Crucial Note: Vertex AI services incur costs. You must have an active billing account linked to your Google Cloud project.
Navigate to "Billing" in the Google Cloud Console.
Ensure a billing account is linked. If not, set one up. Google often offers a free trial with credits for new users, so you can experiment without immediate cost.
1.3. Install Necessary Tools (Local Setup)
Google Cloud SDK (gcloud CLI): This command-line interface is indispensable for interacting with Google Cloud services.
Follow the official Google Cloud documentation to install and initialize the gcloud CLI on your local machine.
Run
gcloud auth loginto authenticate your account.Set your project:
gcloud config set project YOUR_PROJECT_ID
Vertex AI SDK for Python: This powerful SDK allows you to programmatically interact with Vertex AI from your Python code.
Install it using pip:
pip install google-cloud-aiplatform google-cloud-storageThis will be vital for writing your training scripts and orchestrating jobs.
Step 2: Prepare Your Data and Training Code
Now that your environment is ready, it's time to gather your ingredients: your dataset and the recipe for your model (your training code).
2.1. Organize Your Dataset in Cloud Storage
Cloud Storage Bucket: Vertex AI training jobs typically access data stored in Google Cloud Storage (GCS) buckets.
Create a GCS bucket:
gsutil mb -p YOUR_PROJECT_ID -l YOUR_REGION gs://your-training-data-bucketUpload your training and validation data to this bucket. For example, if you have CSV files or image directories, upload them to specific paths within the bucket.
Best Practice: Organize your data logically within the bucket (e.g.,
gs://your-data-bucket/images/train/,gs://your-data-bucket/labels.csv).
2.2. Develop Your Training Application
Tip: Compare what you read here with other sources.
Your ML Model's Brain: This is where you write the Python (or other language) code that defines your model, loads data, trains the model, and saves the trained artifacts.
Key Considerations for Vertex AI:
Input Data Paths: Your training script should be able to read data from the GCS paths you provide. Libraries like
tensorflow.io.gfileorgoogle.cloud.storageare helpful for this.Output Model Path: Vertex AI sets an environment variable,
AIP_MODEL_DIR, which points to a Cloud Storage location where your trained model artifacts should be saved. Your script must save your model to this location. For example, if you're saving a TensorFlow Keras model:Pythonimport os model.save(os.environ['AIP_MODEL_DIR'])Or for a scikit-learn model using
joblib:Pythonimport os import joblib joblib.dump(model, os.path.join(os.environ['AIP_MODEL_DIR'], 'model.pkl'))Dependencies: Make sure your training script specifies all necessary external libraries (e.g.,
tensorflow,scikit-learn,pandas,numpy). You'll use these to define your training environment.Entry Point: Your script should have a clear entry point (e.g., a
mainfunction) that Vertex AI will execute.
2.3. Package Your Training Application
Vertex AI needs your training code in a specific format to run it. You have a few options:
2.3.1. Option A: Single Python File (Simplest for Prototyping)
For small, self-contained scripts, you can simply provide a single Python file. Vertex AI will package it into a Python source distribution and install it onto a prebuilt container.
This is great for quick tests and initial development.
2.3.2. Option B: Python Source Distribution
For more complex projects with multiple Python files, package your training application into one or more Python source distributions (e.g., a
.tar.gzfile).Upload these to a Cloud Storage bucket. Vertex AI will install them onto a prebuilt container image.
2.3.3. Option C: Custom Container Image (Recommended for Production)
This offers the most control and reproducibility. You create your own Docker container image that has your training application, all its dependencies, and environment configurations pre-installed.
Steps for Custom Container:
Create a
Dockerfile: This file specifies how to build your container image.Dockerfile# Choose a base image (e.g., a pre-built TensorFlow image, or a generic Python image) FROM us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-17.py310:latest WORKDIR /app # Copy your training code into the container COPY trainer/ /app/trainer/ # Install any additional dependencies (if not in the base image) RUN pip install -r trainer/requirements.txt # Set the entry point for your training script ENTRYPOINT ["python", "-m", "trainer.train"]Create
requirements.txt: List all Python dependencies for your training code.tensorflow==2.17.0 pandas scikit-learn google-cloud-storageBuild your Docker image:
Bashdocker build -t gcr.io/YOUR_PROJECT_ID/your-training-image:latest .Push to Artifact Registry: You'll need an Artifact Registry repository.
Bashgcloud artifacts repositories create your-repo --repository-format=docker --location=YOUR_REGION --description="Docker repository for ML models" gcloud auth configure-docker YOUR_REGION-docker.pkg.dev docker push YOUR_REGION-docker.pkg.dev/YOUR_PROJECT_ID/your-repo/your-training-image:latest
Using custom containers ensures that your training environment is exactly as you define it, minimizing discrepancies between development and production.
Step 3: Configure Your Training Job
With your data ready and your code packaged, it's time to tell Vertex AI how to run your training job. This involves specifying compute resources, where to find your code, and any other parameters.
3.1. Choose Your Training Method
As discussed, you'll primarily use Custom Training for flexibility. Within custom training, you can configure:
CustomJob: For single-run training.
Hyperparameter Tuning Job: To systematically find the best set of hyperparameters for your model.
Training Pipeline: To orchestrate a multi-step ML workflow (e.g., data preprocessing, training, evaluation, deployment).
3.2. Specify Compute Resources
This is where you decide the horsepower for your training.
Machine Type: Select appropriate machine types (e.g.,
n1-standard-4,n1-highmem-8).Accelerators (GPUs/TPUs): If your model benefits from accelerated computing, specify the type and number of GPUs (e.g.,
NVIDIA_TESLA_T4,NVIDIA_TESLA_V100) or TPUs.Remember: Using GPUs/TPUs will significantly increase costs, so choose wisely based on your model's needs.
Disk Size: Allocate sufficient boot disk space for your training job.
3.3. Define Your Training Application Details
Container Image URI:
If using a prebuilt container, specify its URI (e.g.,
us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-17.py310:latest).If using a custom container, provide the URI from Artifact Registry (e.g.,
YOUR_REGION-docker.pkg.dev/YOUR_PROJECT_ID/your-repo/your-training-image:latest).
Python Package URIs (if applicable): If you're providing your training code as a Python source distribution, specify the GCS URI of your
.tar.gzfile.Entry Point: Specify the entry point for your training application (e.g.,
trainer.trainif you havetrain.pyinside atrainerpackage).Command-line Arguments: Pass any arguments your training script expects (e.g.,
--learning-rate 0.01,--epochs 10).
3.4. Configure Output Location
Tip: Pause, then continue with fresh focus.
Vertex AI will automatically handle the output directory for your model artifacts, typically setting the
AIP_MODEL_DIRenvironment variable. You don't usually need to specify this directly when configuring the job, but it's good to be aware of.
3.5. (Optional) Distributed Training
For very large models or datasets, you might want to distribute your training across multiple machines.
Vertex AI supports distributed training by allowing you to define multiple "worker pools" with different machine types and roles (primary, worker, parameter server, evaluator).
Your training code needs to be written to leverage distributed strategies (e.g., TensorFlow's
MirroredStrategyorMultiWorkerMirroredStrategy).
3.6. (Optional) Hyperparameter Tuning Configuration
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If you're running a hyperparameter tuning job, you'll need to define:
Hyperparameters to tune: Specify their names, types (e.g.,
DOUBLE,INTEGER,CATEGORICAL), and ranges of values.Objective Metric: The metric your tuning job should optimize (e.g.,
accuracy,loss) and whether to maximize or minimize it.Max Trials and Max Parallel Trials: Control the search space and concurrency.
Algorithm: Choose a search algorithm (e.g., Bayesian optimization, grid search, random search).
Step 4: Create and Run Your Training Job
The moment of truth! With everything configured, it's time to launch your training job.
4.1. Using the Google Cloud Console
Navigate to Training: In the Google Cloud Console, go to "Vertex AI" > "Training."
Create a new training job: Click "CREATE."
Follow the wizard:
Select "Custom training (advanced)."
Choose "Train new model."
Provide a model name and optionally a managed dataset.
Specify your custom container image URI or Python package.
Configure machine type, accelerators, and other compute resources.
Add command-line arguments.
Review and "START TRAINING."
4.2. Using the Vertex AI SDK for Python (Programmatic Approach)
This is often preferred for automation, reproducibility, and integration into MLOps pipelines.
from google.cloud import aiplatform
import os
# Set your project ID and region
PROJECT_ID = "your-project-id"
REGION = "us-central1" # Or your desired region
STAGING_BUCKET = f"gs://{PROJECT_ID}-aiplatform-staging" # Or your own staging bucket
# Initialize Vertex AI SDK
aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)
# Define your training job details
DISPLAY_NAME = "my-custom-model-training-job"
CONTAINER_URI = f"{REGION}-docker.pkg.dev/{PROJECT_ID}/your-repo/your-training-image:latest" # If using custom container
# OR for pre-built container:
# CONTAINER_URI = "us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-17.py310:latest"
# PYTHON_PACKAGE_URI = "gs://your-training-data-bucket/trainer.tar.gz" # If using Python package
# Command-line arguments for your training script
ARGUMENTS = [
"--epochs", "10",
"--learning-rate", "0.001",
"--data-dir", "gs://your-training-data-bucket/data/"
]
# Machine configuration
MACHINE_TYPE = "n1-standard-4"
ACCELERATOR_TYPE = "NVIDIA_TESLA_T4" # Or None if no GPU
ACCELERATOR_COUNT = 1 # Or 0 if no GPU
# Create and run the custom training job
job = aiplatform.CustomContainerTrainingJob(
display_name=DISPLAY_NAME,
container_uri=CONTAINER_URI,
# If using Python package:
# python_package_gcs_uri=PYTHON_PACKAGE_URI,
# python_module_name="trainer.train",
)
# Run the job
model = job.run(
args=ARGUMENTS,
replica_count=1,
machine_type=MACHINE_TYPE,
accelerator_type=ACCELERATOR_TYPE,
accelerator_count=ACCELERATOR_COUNT,
model_display_name=f"{DISPLAY_NAME}-model" # Name for the registered model
)
print(f"Training job started: {job.resource_name}")
print(f"Model will be registered as: {model.resource_name}")
4.3. Monitoring Your Training Job
Keep an eye on its progress!
Google Cloud Console: Navigate to "Vertex AI" > "Training" and click on your job. You'll see logs, resource utilization, and status updates.
Vertex AI TensorBoard: Integrate TensorBoard with your training jobs to visualize metrics, graphs, and model performance in real-time. This is highly recommended for deeper insights.
Enable Vertex AI TensorBoard instance for your project.
Configure your training code to write TensorBoard logs to a GCS bucket that your TensorBoard instance can access.
Step 5: Model Registration and Versioning
Once your training job successfully completes and saves its artifacts, Vertex AI automatically registers your model in the Model Registry.
5.1. View Your Registered Model
Go to "Vertex AI" > "Models" in the Google Cloud Console. You'll see your newly trained model listed.
The Model Registry is a central hub for managing your models, including their versions, metadata, and associated artifacts.
5.2. Model Versioning
This is a powerful MLOps feature. Every time you run a training job that registers a model, you create a new version of that model.
This allows you to track changes, compare performance across versions, and easily roll back to previous versions if needed.
When training a new version of an existing model, you can specify the parent model during job creation to group it correctly in the registry.
Tip: Highlight sentences that answer your questions.
Step 6: Deploying Your Model for Prediction
A trained model is only useful if you can get predictions from it. Vertex AI simplifies this by providing robust deployment options.
6.1. Create an Endpoint
Before you can serve predictions, you need an Endpoint. An endpoint is a dedicated resource that hosts your model and serves predictions via a REST API.
You can deploy multiple models to a single endpoint or deploy the same model to multiple endpoints.
From Console: Go to your model in the Model Registry, click "Deploy to endpoint."
From SDK:
# Assuming 'model' is the Vertex AI Model object returned from job.run()
# or retrieved from Model Registry:
# model = aiplatform.Model(model_name="projects/YOUR_PROJECT_ID/locations/YOUR_REGION/models/YOUR_MODEL_ID")
# Configure deployment resources
DEPLOYED_MODEL_NAME = "my-deployed-model"
MACHINE_TYPE = "n1-standard-2"
MIN_REPLICA_COUNT = 1
MAX_REPLICA_COUNT = 2 # For autoscaling
endpoint = model.deploy(
deployed_model_display_name=DEPLOYED_MODEL_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 deployed to endpoint: {endpoint.resource_name}")
6.2. Configure Serving Container (If Custom Model)
If you used a custom training container, you also need a serving container that can load your model artifacts and handle prediction requests.
Vertex AI provides prebuilt serving containers for popular frameworks (TensorFlow, PyTorch, scikit-learn, XGBoost). These are often sufficient.
If your serving logic is complex or requires specific dependencies, you'll create a custom serving container (similar to the custom training container, but with an HTTP server, e.g., Flask, and a
predictendpoint).
6.3. Get Online Predictions
Once deployed, you can send online prediction requests to your endpoint.
# Example for a simple prediction (adjust input format based on your model)
# data_to_predict = {"instances": [[1.0, 2.0, 3.0, 4.0]]} # Example numeric data
data_to_predict = {"instances": [{"feature_a": 10, "feature_b": 20}]} # Example structured data
prediction = endpoint.predict(instances=data_to_predict["instances"])
print(f"Prediction results: {prediction.predictions}")
6.4. Batch Predictions
For large datasets where low latency isn't critical, you can run batch predictions. You provide input data in a GCS bucket, and Vertex AI writes the predictions to another GCS bucket.
Step 7: Continuous Improvement and MLOps Practices
Training a model isn't a one-time event. MLOps (Machine Learning Operations) is about continuously integrating, deploying, and monitoring your ML systems. Vertex AI provides tools to help you with this.
7.1. Model Monitoring
Detect Drift and Skew: Vertex AI Model Monitoring helps detect training-serving skew (discrepancy between training and serving data) and prediction drift (changes in incoming data over time).
Set up alerts to be notified when your model's performance might be deteriorating due to data changes.
7.2. Vertex AI Pipelines
Automate Workflows: Orchestrate your entire ML workflow from data ingestion and preprocessing to training, evaluation, and deployment using Vertex AI Pipelines. This ensures consistency and reproducibility.
7.3. Vertex ML Metadata
Track Everything: Automatically track artifacts (datasets, models), executions (training jobs, evaluation runs), and parameters used throughout your ML lifecycle. This is crucial for debugging, auditing, and understanding your experiments.
7.4. Vertex AI Experiments
QuickTip: Reflect before moving to the next part.
Compare and Iterate: Track and compare different model architectures, hyperparameter settings, and training environments to identify the best-performing model for your use case.
10 Related FAQ Questions
How to choose between AutoML and Custom Training in Vertex AI?
Quick Answer: Choose AutoML if you prioritize speed, ease of use, and have limited ML expertise. Opt for Custom Training when you need full control over your model architecture, code, and advanced configurations (e.g., custom loss functions, specific network architectures, distributed training).
How to prepare my data for Vertex AI training?
Quick Answer: Upload your datasets to Google Cloud Storage (GCS). Organize them logically. For custom training, ensure your training script can read data from GCS paths. For AutoML, follow specific data formatting guidelines (e.g., CSV, TFRecord, image directories) as per the model type.
How to specify hardware resources (GPUs/TPUs) for my training job?
Quick Answer: When configuring your custom training job (via console or SDK), specify the
machine_typeand optionallyaccelerator_type(e.g.,NVIDIA_TESLA_T4) andaccelerator_count(e.g.,1) for GPUs, orTPUfor TPUs.
How to use pre-built containers for model training in Vertex AI?
Quick Answer: Provide the URI of the desired pre-built container image (e.g., for TensorFlow, PyTorch, scikit-learn) when defining your custom training job. Your Python training script will be installed and run within this container.
How to create a custom container for my Vertex AI training job?
Quick Answer: Create a
Dockerfilethat specifies your base image, copies your training code, installs dependencies, and sets the entry point. Build the Docker image locally and push it to Google Artifact Registry. Then, use its URI when configuring your training job.
How to monitor the progress of my Vertex AI training job?
Quick Answer: You can monitor job status, logs, and resource utilization directly in the "Training" section of the Vertex AI console. For detailed metric visualization, integrate Vertex AI TensorBoard by configuring your training script to write logs to a GCS bucket accessible by TensorBoard.
How to perform hyperparameter tuning with Vertex AI?
Quick Answer: Create a Hyperparameter Tuning Job in Vertex AI. Define the hyperparameters to tune (name, type, range), the objective metric (e.g., accuracy, loss), and the optimization goal (maximize/minimize). Vertex AI will run multiple trials to find optimal values.
How to version my models in Vertex AI?
Quick Answer: Vertex AI automatically manages model versions in the Model Registry. Each successful training job that registers a model creates a new version. You can view, compare, and manage different versions within the "Models" section of the console.
How to deploy a trained model from Vertex AI Model Registry to an endpoint?
Quick Answer: From the Vertex AI console, go to "Models," select your trained model version, and click "Deploy to endpoint." Choose the machine type, scaling options (min/max replicas), and the serving container image.
How to get predictions from a deployed model endpoint in Vertex AI?
Quick Answer: Once deployed, your model endpoint will have a unique URI. You can send online prediction requests to this URI using the Vertex AI SDK for Python's
endpoint.predict()method or by making direct HTTP POST requests with your input data.
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