This example showcases how to use a model from TF-hub and deploy it on to AI-platform.
The model used is the Universal sentence encoder.
To extract a SavedModel from the TF-hub model, run:
python tfhub_to_savedmodel.py
First, we need to copy to model to a Google Cloud Storage bucket.
In order to create a bucket:
BUCKET_NAME=[YOUR-BUCKET-NAME]
gsutil mb gs://$BUCKET_NAME
To copy over the SavedModel:
MODEL_DIR=gs://$BUCKET_NAME/model1/
gsutil -m cp -r saved_model/* $MODEL_DIR
Define a model name and create a model on AI-platform:
MODEL_NAME=sentence
gcloud ai-platform models create $MODEL_NAME --regions europe-west1
Define a model version:
VERSION_NAME=v1
FRAMEWORK=TENSORFLOW
Deploy the model to AI-platform:
gcloud ai-platform versions create $VERSION_NAME \
--model $MODEL_NAME \
--origin $MODEL_DIR \
--runtime-version=1.15 \
--framework $FRAMEWORK \
--python-version=3.7
In order to use the google-api-client, we need to reference a service account.
So the first step is to make sure you can reference a valid service account
in the aiplatform_request.py. In the example code, the service account is
called sa.json and is at the root directory of the repository.
Subsequently, run:
PROJECT=[YOUR-PROJECT-ID]
python aiplatform_request.py input.json --project=$PROJECT --model=sentence --version=v1
Please note that the first request will take signicantly more time as the model is in a 'cold start'.