<aside>
β Like other notes on this site, this note contains only a few noteworthy points of the topic.
</aside>
π My Github Repo for this note: dinhanhthi/google-vertex-ai
π All services needed for Data Science on Google Cloud.
Good to know
- You should choose the same location/region for all services (google project, notebook instances,...). π Check section βChoose the same locationsβ.
- Troubleshooting.
- Access Cloud Storage buckets.
- Google Cloud Pricing Calculator
gcloud ai
references (for Vertex AI)
- ALways use Logging service to track the problems.
- When making models, especially for serving on prod, don't forget to use
logging
services.
- When creating a new notebook instance, consider to choose a larger size for "boot disk" (100GB is not enough as it is).
- If you run the gcp command lines in workbench, you don't have to give the credential for the connecting to gcp. It's automatically passed.
Tutorials & references
- What is Vertex AI? -- Official video.
- Google Cloud Vertex AI Samples -- Official github repository.
- Vertex AI Documentation AIO: Samples - References -- Guides.
Notebooks (Workbench)
<aside>
β If you are going to create images with docker
inside the virtual machine, you should choose more boot disk space (default = 100GB but you should choose more than that). In case you wanna change the size of disk, you can go to Compute Engine / Disks (ref).
</aside>
<aside>
π¨ Remember to shutdown the notebook if you don't use it!!
</aside>