Overview of Google Cloud for Machine Learning

  • GCP provides the core building blocks (compute, storage, networking) for ML research.
  • A notebook can act as a controller to organize cloud workflows and keep experiments reproducible.
  • Using raw infrastructure instead of a fully managed platform gives researchers flexibility while still benefiting from scalable cloud resources.

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 Workbench Instance notebook as a controller to manage larger, resource-intensive tasks.
  • Always navigate to the “Instances” tab in Workbench, since older notebook types are deprecated.
  • Choose the same region for your Workbench Instance and storage bucket to avoid extra transfer costs.
  • 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.
  • Workbench Instances come with JupyterLab 3 and GPU frameworks preinstalled, making them an easy entry point for ML workflows.
  • Enable idle auto-stop to avoid unexpected charges when notebooks are left running.

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

  • Use CustomTrainingJob with a prebuilt PyTorch container; let your script control outputs via --model_out.
  • Keep artifacts together (model, metrics, history, log) in one folder for reproducibility.
  • .npz speeds up loading and plays nicely with PyTorch.
  • Start on CPU for small datasets; use GPU only when profiling shows a clear win.
  • Skip base_output_dir unless you specifically want Vertex’s default run directory; staging bucket is just for the SDK packaging tarball.

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.