Learning Objectives
In this notebook, we'll deploying our Keras model to Cloud AI Platform and creating predictions.
We will set up the environment, deploy a trained Keras model to Cloud AI Platform, online predict from deployed model on Cloud AI Platform, and batch predict from deployed model on Cloud AI Platform.
Each learning objective will correspond to a #TODO in this student lab notebook -- try to complete this notebook first and then review the solution notebook.
Import necessary libraries.
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!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
In [1]:
import os
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%%bash
PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
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# Change these to try this notebook out
PROJECT = "your-project-name-here" # Replace with your PROJECT
BUCKET = PROJECT # defaults to PROJECT
REGION = "us-central1" # Replace with your REGION
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os.environ["BUCKET"] = BUCKET
os.environ["REGION"] = REGION
os.environ["TFVERSION"] = "2.1"
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%%bash
gcloud config set compute/region $REGION
Let's check the directory structure of our outputs of our trained model in folder we exported the model to in our last lab. We'll want to deploy the saved_model.pb within the timestamped directory as well as the variable values in the variables folder. Therefore, we need the path of the timestamped directory so that everything within it can be found by Cloud AI Platform's model deployment service.
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%%bash
gsutil ls gs://${BUCKET}/babyweight/trained_model
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%%bash
MODEL_LOCATION=$(gsutil ls -ld -- gs://${BUCKET}/babyweight/trained_model/2* \
| tail -1)
gsutil ls ${MODEL_LOCATION}
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%%bash
MODEL_NAME="babyweight"
MODEL_VERSION="ml_on_gcp"
MODEL_LOCATION=$(gsutil ls -ld -- gs://${BUCKET}/babyweight/trained_model/2* \
| tail -1 | tr -d '[:space:]')
echo "Deleting and deploying $MODEL_NAME $MODEL_VERSION from $MODEL_LOCATION"
# gcloud ai-platform versions delete ${MODEL_VERSION} --model ${MODEL_NAME}
# gcloud ai-platform models delete ${MODEL_NAME}
gcloud ai-platform models create ${MODEL_NAME} --regions ${REGION}
gcloud ai-platform versions create ${MODEL_VERSION} \
--model=${MODEL_NAME} \
--origin=${MODEL_LOCATION} \
--runtime-version=2.1 \
--python-version=3.7
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from oauth2client.client import GoogleCredentials
import requests
import json
MODEL_NAME = "babyweight"
MODEL_VERSION = "ml_on_gcp"
token = GoogleCredentials.get_application_default().get_access_token().access_token
api = "https://ml.googleapis.com/v1/projects/{}/models/{}/versions/{}:predict" \
.format(PROJECT, MODEL_NAME, MODEL_VERSION)
headers = {"Authorization": "Bearer " + token }
data = {
"instances": [
{
"is_male": "True",
"mother_age": 26.0,
"plurality": "Single(1)",
"gestation_weeks": 39
},
{
"is_male": "False",
"mother_age": 29.0,
"plurality": "Single(1)",
"gestation_weeks": 38
},
{
"is_male": "True",
"mother_age": 26.0,
"plurality": "Triplets(3)",
"gestation_weeks": 39
},
{
"is_male": "Unknown",
"mother_age": 29.0,
"plurality": "Multiple(2+)",
"gestation_weeks": 38
},
]
}
response = requests.post(api, json=data, headers=headers)
print(response.content)
The predictions for the four instances were: 5.33, 6.09, 2.50, and 5.86 pounds respectively when I ran it (your results might be different).
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%%writefile inputs.json
{"is_male": "True", "mother_age": 26.0, "plurality": "Single(1)", "gestation_weeks": 39}
{"is_male": "False", "mother_age": 26.0, "plurality": "Single(1)", "gestation_weeks": 39}
Now call gcloud ai-platform predict using the JSON we just created and point to our deployed model and version.
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%%bash
gcloud ai-platform predict \
--model=babyweight \
--json-instances=inputs.json \
--version=ml_on_gcp
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%%bash
INPUT=gs://${BUCKET}/babyweight/batchpred/inputs.json
OUTPUT=gs://${BUCKET}/babyweight/batchpred/outputs
gsutil cp inputs.json $INPUT
gsutil -m rm -rf $OUTPUT
gcloud ai-platform jobs submit prediction babypred_$(date -u +%y%m%d_%H%M%S) \
--data-format=TEXT \
--region ${REGION} \
--input-paths=$INPUT \
--output-path=$OUTPUT \
--model=babyweight \
--version=ml_on_gcp
Copyright 2019 Google Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License