Summary and Schedule
This is a new lesson built with The Carpentries Workbench.
| Setup Instructions | Download files required for the lesson | |
| Duration: 00h 00m | 1. Overview of Google Cloud Vertex AI |
What problem does Google Cloud Vertex AI aim to solve? How does Vertex AI simplify machine learning workflows compared to running them on your own? |
| Duration: 00h 11m | 2. Data Storage: Setting up GCS |
How can I store and manage data effectively in GCP for Vertex AI
workflows? What are the advantages of Google Cloud Storage (GCS) compared to local or VM storage for machine learning projects? |
| Duration: 00h 31m | 3. Notebooks as Controllers |
How do you set up and use Vertex AI Workbench notebooks for machine
learning tasks? How can you manage compute resources efficiently using a “controller” notebook approach in GCP? |
| Duration: 01h 01m | 4. Accessing and Managing Data in GCS with Vertex AI Notebooks |
How can I load data from GCS into a Vertex AI Workbench
notebook? How do I monitor storage usage and costs for my GCS bucket? What steps are involved in pushing new data back to GCS from a notebook? |
| Duration: 01h 31m | 5. Using a GitHub Personal Access Token (PAT) to Push/Pull from a Vertex AI Notebook |
How can I securely push/pull code to and from GitHub within a Vertex AI
Workbench notebook? What steps are necessary to set up a GitHub PAT for authentication in GCP? How can I convert notebooks to .py files and ignore .ipynb files in version
control?
|
| Duration: 02h 06m | 6. Training Models in Vertex AI: Intro |
What are the differences between training locally in a Vertex AI
notebook and using Vertex AI-managed training jobs? How do custom training jobs in Vertex AI streamline the training process for various frameworks? How does Vertex AI handle scaling across CPUs, GPUs, and TPUs? |
| Duration: 02h 36m | 7. Training Models in Vertex AI: PyTorch Example |
When should you consider using a GPU or TPU instance for training neural
networks in Vertex AI, and what are the benefits and
limitations? How does Vertex AI handle distributed training, and which approaches are suitable for typical neural network training? |
| Duration: 03h 06m | 8. Hyperparameter Tuning in Vertex AI: Neural Network Example |
How can we efficiently manage hyperparameter tuning in Vertex
AI? How can we parallelize tuning jobs to optimize time without increasing costs? |
| Duration: 04h 06m | 9. Resource Management & Monitoring on Vertex AI (GCP) |
How do I monitor and control Vertex AI, Workbench, and GCS costs
day‑to‑day? What specifically should I stop, delete, or schedule to avoid surprise charges? How can I automate cleanup and set alerting so leaks get caught quickly? |
| Duration: 05h 11m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
FIXME: Setup instructions live in this document. Please specify the tools and the data sets the Learner needs to have installed.
Data Sets
Download the data zip file and unzip it to your Desktop
Software Setup
Details
Setup for different systems can be presented in dropdown menus via a
spoiler tag. They will join to this discussion block, so
you can give a general overview of the software used in this lesson here
and fill out the individual operating systems (and potentially add more,
e.g. online setup) in the solutions blocks.
Use PuTTY
Use Terminal.app
Use Terminal