I’m currently exploring how to set up notebook instances in Google Cloud’s Vertex AI platform. While going through various tutorials and documentation, I noticed that there should be multiple environment options available when creating a new notebook instance.
From what I’ve seen in different guides, there are supposed to be various pre-configured environments to choose from during the notebook creation process. However, when I actually try to create a notebook in my Google Cloud Platform account, I’m only seeing three options:
Python 3
PyTorch 1.12
TensorFlow Enterprise
I expected to find additional environment choices like R, different Python versions, or other machine learning frameworks. Has anyone else encountered this limitation? Is there a specific reason why my GCP project might have restricted options, or am I missing a configuration step that would unlock more environment choices?
You’re likely experiencing limited notebook options due to billing settings or regional restrictions. I encountered this issue myself when starting with Vertex AI. Ensure that the Notebooks API is activated and that billing is set up for your project. It’s also important to note the distinction between the old AI Platform Notebooks and the newer Vertex AI Workbench, as they offer different environment choices. If you are using a corporate GCP account, organizational policies may restrict access to certain environments.
You’re probably using the wrong service interface in GCP. If you’re creating notebooks through the AI Platform section instead of Vertex AI Workbench, you’ll only see those three basic environments. I hit this same issue when Google moved from AI Platform Notebooks to Vertex AI Workbench. The old interface is pretty limited. Go straight to Vertex AI Workbench in your console and check the managed notebooks section - you should see way more options like R, different Python versions, and specialized ML environments. Also check that you’ve got both Vertex AI API and Compute Engine API enabled. Some environments need extra backend services that aren’t turned on by default in newer projects.
check ur region first - some zones dont have all environmants available. also verify your perms since admins sometimes restrict environ access. i had the same problem and switching zones fixed it.
I’ve hit this exact problem too. The limited environment options come from quota restrictions or your machine type choice during setup.
Most people miss this: Google limits environments based on your project’s compute quotas and region. Also, managed notebook instances have way fewer options than user-managed ones.
What worked for me: I ditched fighting GCP’s limitations and started using Latenode to automate notebook provisioning. You can build workflows that auto-configure your preferred environments and dependencies instead of being stuck with Google’s limited choices.
The best part? Latenode spins up notebooks with custom environments across different cloud providers - you’re not trapped in GCP’s restrictions. Set up the automation once and reuse it for new instances.
Saves me hours compared to wrestling with console settings every time I need a new notebook.
Yeah, this trips up tons of people moving to Vertex AI. I’ve seen it happen over and over. Usually it’s one of two things.
First - check your service account permissions. You might see the notebook interface but still lack the IAM roles for environment provisioning. Basic editor role often isn’t enough.
Second thing that got me when we migrated our notebooks - the environment list loads dynamically based on your enabled APIs and available compute in your zone. If you’re in a constrained region or hit quota limits, Google just hides environments that would fail anyway.
Try bumping up to a higher machine type temporarily. Sometimes the environment dropdown shows more options when you pick configs that can actually handle more frameworks.
This walkthrough saved me when setting up our infrastructure:
Still getting limited options? Just start with Python 3 and install what you need through pip or conda. Not ideal but it works.
