Overview of Google Cloud Vertex AI

  • Vertex AI simplifies ML workflows by integrating data, training, tuning, and deployment in one managed platform.
  • It reduces the need for manual orchestration compared to traditional research computing environments.
  • Cost monitoring and resource tracking help keep cloud usage affordable for research projects.

Data Storage: Setting up GCS

  • Use GCS for scalable, cost-effective, and persistent storage in GCP.
  • Persistent disks are suitable only for small, temporary datasets.
  • Track your storage, transfer, and request costs to manage expenses.
  • Regularly delete unused data or buckets to avoid ongoing costs.

Notebooks as Controllers

  • Use a small Vertex AI Workbench notebook instance as a controller to manage larger, resource-intensive tasks.
  • Submit training and tuning jobs to scalable instances using the Vertex AI SDK.
  • Labels help track costs effectively, especially in shared or multi-project environments.
  • Vertex AI Workbench integrates directly with GCS and Vertex AI services, making it a hub for ML workflows.

Accessing and Managing Data in GCS with Vertex AI Notebooks

  • Load data from GCS into memory to avoid managing local copies when possible.
  • Periodically check storage usage and costs to manage your GCS budget.
  • Use Vertex AI Workbench notebooks to upload analysis results back to GCS, keeping workflows organized and reproducible.

Using a GitHub Personal Access Token (PAT) to Push/Pull from a Vertex AI Notebook

  • Use a GitHub PAT for HTTPS-based authentication in Vertex AI Workbench notebooks.
  • Securely enter sensitive information in notebooks using getpass.
  • Converting .ipynb files to .py files helps with cleaner version control.
  • Adding .ipynb files to .gitignore keeps your repository organized.

Training Models in Vertex AI: Intro

  • Environment initialization: Use aiplatform.init() to set defaults for project, region, and bucket.
  • Local vs managed training: Test locally before scaling into managed jobs.
  • Custom jobs: Vertex AI lets you run scripts as managed training jobs using pre-built or custom containers.
  • Scaling: Start small, then scale up to GPUs or distributed jobs as dataset/model size grows.
  • Monitoring: Track job logs and artifacts in the Vertex AI Console.

Training Models in Vertex AI: PyTorch Example

  • .npz files streamline PyTorch data handling and reduce I/O overhead.
  • GPUs may not speed up small models/datasets due to overhead.
  • Vertex AI supports both CPU and GPU training, with scaling via multiple replicas.
  • Data parallelism splits data, model parallelism splits layers — choose based on model size.
  • Test locally first before launching expensive training jobs.

Hyperparameter Tuning in Vertex AI: Neural Network Example

  • Vertex AI Hyperparameter Tuning Jobs let you efficiently explore parameter spaces using adaptive strategies.
  • Always test with max_trial_count=1 first to confirm your setup works.
  • Limit parallel_trial_count to a small number (2–4) to benefit from adaptive search.
  • Use GCS for input/output and monitor jobs through the Vertex AI Console.

Resource Management & Monitoring on Vertex AI (GCP)

  • Endpoints and running notebooks are the most common cost leaks; undeploy/stop first.
  • Prefer Managed Notebooks with Idle shutdown; schedule nightly auto‑stop.
  • Keep storage tidy with GCS lifecycle policies and avoid duplicate datasets.
  • Standardize labels, set budgets, and enable billing export for visibility.
  • Use gcloud/gsutil to audit and clean quickly; automate with Scheduler + Cloud Run/Functions.