October 2018
Beginner
362 pages
9h 32m
English
Content preview from Hands-On Artificial Intelligence for Beginners
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Deploying for online learning on GCP
When we deploy a TensorFlow SavedModel to the GCP platform, we either need to upload the entire SavedModel directory to a storage location on GCP or train in the cloud as we did previously. Regardless of what method you main use, your TensorFlow model's binaries should be stored in a Google Cloud Storage location.
Your model binaries will be the final that was created after training, and will have the extension .pb.
To start our deployment process, we first need to create a deployed model object. You can create it with the command as follows:
gcloud ml-engine models create "deployed_classifier"
Next, we'll create an environment variable that will let GCP know where our saved model binaries are:
DEPLOYMENT_SOURCE="gs://classifier_bucket123/classifier_model/binaries" ...
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