Fix Vertex AI model not found error
Quick answer
The Vertex AI model not found error occurs when the specified
model_id is incorrect, missing, or not deployed in the given project and location. Ensure you use the full resource name or correct model ID with proper project and location initialization in the vertexai SDK to fix this error. ERROR TYPE
config_error ⚡ QUICK FIX
Verify and use the correct full model resource name or model ID with matching project and location in your Vertex AI client initialization.
Why this happens
The model not found error in Vertex AI typically arises when the model_id provided to the vertexai SDK does not match any deployed model in the specified project and location. This can happen if you use an incomplete or incorrect model name, omit the project or location, or if the model was not deployed or is deleted.
Example of broken code triggering the error:
import vertexai
from vertexai.language_models import TextGenerationModel
vertexai.init(project="my-gcp-project", location="us-central1")
# Incorrect or incomplete model ID
model = TextGenerationModel.from_pretrained("nonexistent-model")
response = model.generate_text("Hello")
print(response.text) output
google.api_core.exceptions.NotFound: Model 'nonexistent-model' not found in project 'my-gcp-project' and location 'us-central1'.
The fix
Use the correct full model resource name or the exact model ID as registered in your Vertex AI project. Also, ensure vertexai.init() is called with the right project and location. This lets the SDK locate the model properly.
Example of fixed code:
import os
import vertexai
from vertexai.language_models import TextGenerationModel
vertexai.init(
project=os.environ["GOOGLE_CLOUD_PROJECT"],
location="us-central1"
)
# Use the exact model ID or full resource name
model = TextGenerationModel.from_pretrained("gemini-2.0-flash")
response = model.generate_text("Hello")
print(response.text) output
Hello! How can I assist you today?
Preventing it in production
- Validate the
project,location, andmodel_idenvironment variables before initializing the client. - Use Google Cloud Console or
gcloud ai models listto confirm model availability. - Implement retry logic with exponential backoff for transient errors.
- Log detailed errors and fallback to a default model if the specified one is unavailable.
Key Takeaways
- Always initialize Vertex AI with correct project and location before loading models.
- Use the exact model ID or full resource name to avoid model not found errors.
- Validate model existence via Google Cloud Console or CLI before deployment.
- Implement retries and fallback models to improve production reliability.