Introduction to Google Cloud Machine Learning Engine

This file contains text you can copy and paste for the examples in Cloud Academy's Introduction to Google Cloud Machine Learning Engine course.

TensorFlow

TensorFlow website: https://www.tensorflow.org
TensorFlow installation: https://www.tensorflow.org/install

python -V       # Check which version of Python 2 is installed
python3 -V      # Check which version of Python 3 is installed
pip install --user --upgrade pip
pip install --user --upgrade virtualenv
virtualenv mlenv
source mlenv/bin/activate
pip install tensorflow==1.10
pip install pandas

git clone https://github.com/cloudacademy/mlengine-intro.git
cd mlengine-intro/iris/trainer
python iris.py

Training a Model with ML Engine

Google Cloud SDK installation: https://cloud.google.com/sdk

cd ..
gcloud ai-platform local train --module-name trainer.iris --package-path trainer

BUCKET=gs://[ProjectID]-ml  # Replace [ProjectID] with your Google Cloud Project ID  
REGION=[Region]  # Replace [Region] with a Google Cloud Platform region, such as us-central1  

gcloud ai-platform jobs submit training iris1 \
    --module-name trainer.iris \
    --package-path trainer \
    --staging-bucket $BUCKET \
    --region $REGION \
    --runtime-version 1.10

Feature Engineering

Google's original sample code: https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/census

cd ../census/estimator

gcloud ai-platform local train \
    --module-name trainer.task \
    --package-path trainer \
    -- \
    --train-files data/adult.data.csv \
    --eval-files data/adult.test.csv \
    --model-type wide

A Wide and Deep Model

gcloud ai-platform local train \
    --module-name trainer.task \
    --package-path trainer \
    -- \
    --train-files data/adult.data.csv \
    --eval-files data/adult.test.csv \
    --model-type deep

Distributed Training on ML Engine

Hyperparameter Tuning: https://cloud.google.com/ml-engine/docs/concepts/hyperparameter-tuning-overview

gsutil cp -r gs://cloudml-public/census/data $BUCKET  
TRAIN_DATA=$BUCKET/data/adult.data.csv  
EVAL_DATA=$BUCKET/data/adult.test.csv  
JOB=census1  

gcloud ai-platform jobs submit training $JOB \
    --job-dir $BUCKET/$JOB \
    --runtime-version 1.10 \
    --module-name trainer.task \
    --package-path trainer \
    --region $REGION \
    --scale-tier STANDARD_1 \
    -- \
    --train-files $TRAIN_DATA \
    --eval-files $EVAL_DATA

Deploying a Model on ML Engine

gcloud ai-platform models create census --regions=$REGION  
gsutil ls -r $BUCKET/census1/export  

# Note: Replace [Path-to-model] below with your Cloud Storage path
gcloud ai-platform versions create v1 \
    --model census \
    --runtime-version 1.10 \
    --origin [Path-to-model]

gcloud ai-platform predict \
    --model census \
    --version v1 \
    --json-instances \
    ../test.json

Conclusion

Cloud Machine Learning Engine documentation: https://cloud.google.com/ai-platform/docs