Scaling up ML using Cloud AI Platform

In this notebook, we take a previously developed TensorFlow model to predict taxifare rides and package it up so that it can be run in Cloud AI Platform. For now, we'll run this on a small dataset. The model that was developed is rather simplistic, and therefore, the accuracy of the model is not great either. However, this notebook illustrates how to package up a TensorFlow model to run it within Cloud AI Platform.

Later in the course, we will look at ways to make a more effective machine learning model.

Environment variables for project and bucket

Note that:

Your project id is the *unique* string that identifies your project (not the project name). You can find this from the GCP Console dashboard's Home page. My dashboard reads: Project ID: cloud-training-demos

Cloud training often involves saving and restoring model files. If you don't have a bucket already, I suggest that you create one from the GCP console (because it will dynamically check whether the bucket name you want is available). A common pattern is to prefix the bucket name by the project id, so that it is unique. Also, for cost reasons, you might want to use a single region bucket.

</ol> Change the cell below to reflect your Project ID and bucket name.



In [ ]:

    
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst



In [ ]:

    
import os
PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID
BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME
REGION = 'us-central1' # REPLACE WITH YOUR BUCKET REGION e.g. us-central1



In [ ]:

    
# For Python Code
# Model Info
MODEL_NAME = 'taxifare'
# Model Version
MODEL_VERSION = 'v1'
# Training Directory name
TRAINING_DIR = 'taxi_trained'



In [ ]:

    
# For Bash Code
os.environ['PROJECT'] = PROJECT
os.environ['BUCKET'] = BUCKET
os.environ['REGION'] = REGION
os.environ['MODEL_NAME'] = MODEL_NAME
os.environ['MODEL_VERSION'] = MODEL_VERSION
os.environ['TRAINING_DIR'] = TRAINING_DIR 
os.environ['TFVERSION'] = '2.1'  # Tensorflow version



In [ ]:

    
%%bash
gcloud config set project $PROJECT
gcloud config set compute/region $REGION

Enable the Cloud Machine Learning Engine API

The next command works with Cloud AI Platform API. In order for the command to work, you must enable the API using the Cloud Console UI. Use this link. Then search the API list for Cloud Machine Learning and enable the API before executing the next cell.

Allow the Cloud AI Platform service account to read/write to the bucket containing training data.



In [ ]:

    
%%bash
# This command will fail if the Cloud Machine Learning Engine API is not enabled using the link above.
echo "Getting the service account email associated with the Cloud AI Platform API"

AUTH_TOKEN=$(gcloud auth print-access-token)
SVC_ACCOUNT=$(curl -X GET -H "Content-Type: application/json" \
    -H "Authorization: Bearer $AUTH_TOKEN" \
    https://ml.googleapis.com/v1/projects/${PROJECT}:getConfig \
    | python -c "import json; import sys; response = json.load(sys.stdin); \
    print (response['serviceAccount'])")  # If this command fails, the Cloud Machine Learning Engine API has not been enabled above.

echo "Authorizing the Cloud AI Platform account $SVC_ACCOUNT to access files in $BUCKET"
gsutil -m defacl ch -u $SVC_ACCOUNT:R gs://$BUCKET   
gsutil -m acl ch -u $SVC_ACCOUNT:R -r gs://$BUCKET   # error message (if bucket is empty) can be ignored.  
gsutil -m acl ch -u $SVC_ACCOUNT:W gs://$BUCKET

Packaging up the code

Take your code and put into a standard Python package structure. model.py and task.py containing the Tensorflow code from earlier (explore the directory structure).



In [ ]:

    
%%bash
find ${MODEL_NAME}



In [ ]:

    
%%bash
cat ${MODEL_NAME}/trainer/model.py

Find absolute paths to your data

Note the absolute paths below.



In [ ]:

    
%%bash
echo "Working Directory: ${PWD}"
echo "Head of taxi-train.csv"
head -1 $PWD/taxi-train.csv
echo "Head of taxi-valid.csv"
head -1 $PWD/taxi-valid.csv

Running the Python module from the command-line

Clean model training dir/output dir



In [ ]:

    
%%bash
# This is so that the trained model is started fresh each time. However, this needs to be done before 
rm -rf $PWD/${TRAINING_DIR}



In [ ]:

    
%%bash
# Setup python so it sees the task module which controls the model.py
export PYTHONPATH=${PYTHONPATH}:${PWD}/${MODEL_NAME}
# Currently set for python 2.  To run with python 3 
#    1.  Replace 'python' with 'python3' in the following command
#    2.  Edit trainer/task.py to reflect proper module import method 
python -m trainer.task \
   --train_data_paths="${PWD}/taxi-train*" \
   --eval_data_paths=${PWD}/taxi-valid.csv  \
   --output_dir=${PWD}/${TRAINING_DIR} \
   --train_steps=1000 --job-dir=./tmp



In [ ]:

    
%%bash
ls $PWD/${TRAINING_DIR}/export/exporter/



In [ ]:

    
%%writefile ./test.json
{"pickuplon": -73.885262,"pickuplat": 40.773008,"dropofflon": -73.987232,"dropofflat": 40.732403,"passengers": 2}



In [ ]:

    
%%bash
sudo find "/usr/lib/google-cloud-sdk/lib/googlecloudsdk/command_lib/ml_engine" -name '*.pyc' -delete



In [ ]:

    
%%bash
# This model dir is the model exported after training and is used for prediction
#
model_dir=$(ls ${PWD}/${TRAINING_DIR}/export/exporter | tail -1)
# predict using the trained model
gcloud ai-platform local predict  \
    --model-dir=${PWD}/${TRAINING_DIR}/export/exporter/${model_dir} \
    --json-instances=./test.json

Clean model training dir/output dir



In [ ]:

    
%%bash
# This is so that the trained model is started fresh each time. However, this needs to be done before 
rm -rf $PWD/${TRAINING_DIR}

Running locally using gcloud



In [ ]:

    
%%bash
# Use Cloud Machine Learning Engine to train the model in local file system
gcloud ai-platform local train \
   --module-name=trainer.task \
   --package-path=${PWD}/${MODEL_NAME}/trainer \
   -- \
   --train_data_paths=${PWD}/taxi-train.csv \
   --eval_data_paths=${PWD}/taxi-valid.csv  \
   --train_steps=1000 \
   --output_dir=${PWD}/${TRAINING_DIR}



In [ ]:

    
%%bash
ls $PWD/${TRAINING_DIR}

Submit training job using gcloud

First copy the training data to the cloud. Then, launch a training job.

After you submit the job, go to the cloud console (http://console.cloud.google.com) and select AI Platform | Jobs to monitor progress.

Note: Don't be concerned if the notebook stalls (with a blue progress bar) or returns with an error about being unable to refresh auth tokens. This is a long-lived Cloud job and work is going on in the cloud. Use the Cloud Console link (above) to monitor the job.



In [ ]:

    
%%bash
# Clear Cloud Storage bucket and copy the CSV files to Cloud Storage bucket
echo $BUCKET
gsutil -m rm -rf gs://${BUCKET}/${MODEL_NAME}/smallinput/
gsutil -m cp ${PWD}/*.csv gs://${BUCKET}/${MODEL_NAME}/smallinput/



In [ ]:

    
%%bash
OUTDIR=gs://${BUCKET}/${MODEL_NAME}/smallinput/${TRAINING_DIR}
JOBNAME=${MODEL_NAME}_$(date -u +%y%m%d_%H%M%S)
echo $OUTDIR $REGION $JOBNAME
# Clear the Cloud Storage Bucket used for the training job
gsutil -m rm -rf $OUTDIR
gcloud ai-platform jobs submit training $JOBNAME \
   --region=$REGION \
   --module-name=trainer.task \
   --package-path=${PWD}/${MODEL_NAME}/trainer \
   --job-dir=$OUTDIR \
   --staging-bucket=gs://$BUCKET \
   --scale-tier=BASIC \
   --runtime-version 2.1 \
   --python-version 3.5 \
   -- \
   --train_data_paths="gs://${BUCKET}/${MODEL_NAME}/smallinput/taxi-train*" \
   --eval_data_paths="gs://${BUCKET}/${MODEL_NAME}/smallinput/taxi-valid*"  \
   --output_dir=$OUTDIR \
   --train_steps=10000

Don't be concerned if the notebook appears stalled (with a blue progress bar) or returns with an error about being unable to refresh auth tokens. This is a long-lived Cloud job and work is going on in the cloud.

Use the Cloud Console link to monitor the job and do NOT proceed until the job is done.



In [ ]:

    
%%bash
gsutil ls gs://${BUCKET}/${MODEL_NAME}/smallinput

Train on larger dataset

I have already followed the steps below and the files are already available. You don't need to do the steps in this comment. In the next chapter (on feature engineering), we will avoid all this manual processing by using Cloud Dataflow.

Go to http://bigquery.cloud.google.com/ and type the query:

SELECT
  (tolls_amount + fare_amount) AS fare_amount,
  pickup_longitude AS pickuplon,
  pickup_latitude AS pickuplat,
  dropoff_longitude AS dropofflon,
  dropoff_latitude AS dropofflat,
  passenger_count*1.0 AS passengers,
  'nokeyindata' AS key
FROM
  [nyc-tlc:yellow.trips]
WHERE
  trip_distance > 0
  AND fare_amount >= 2.5
  AND pickup_longitude > -78
  AND pickup_longitude < -70
  AND dropoff_longitude > -78
  AND dropoff_longitude < -70
  AND pickup_latitude > 37
  AND pickup_latitude < 45
  AND dropoff_latitude > 37
  AND dropoff_latitude < 45
  AND passenger_count > 0
  AND ABS(HASH(pickup_datetime)) % 1000 == 1

Note that this is now 1,000,000 rows (i.e. 100x the original dataset). Export this to CSV using the following steps (Note that I have already done this and made the resulting GCS data publicly available, so you don't need to do it.):

Click on the "Save As Table" button and note down the name of the dataset and table.
On the BigQuery console, find the newly exported table in the left-hand-side menu, and click on the name.
Click on "Export Table"
Supply your bucket name and give it the name train.csv (for example: gs://cloud-training-demos-ml/taxifare/ch3/train.csv). Note down what this is. Wait for the job to finish (look at the "Job History" on the left-hand-side menu)
In the query above, change the final "== 1" to "== 2" and export this to Cloud Storage as valid.csv (e.g. gs://cloud-training-demos-ml/taxifare/ch3/valid.csv)
Download the two files, remove the header line and upload it back to GCS.

Run Cloud training on 1-million row dataset

This took 60 minutes and uses as input 1-million rows. The model is exactly the same as above. The only changes are to the input (to use the larger dataset) and to the Cloud MLE tier (to use STANDARD_1 instead of BASIC -- STANDARD_1 is approximately 10x more powerful than BASIC). At the end of the training the loss was 32, but the RMSE (calculated on the validation dataset) was stubbornly at 9.03. So, simply adding more data doesn't help.



In [ ]:

    
%%bash

OUTDIR=gs://${BUCKET}/${MODEL_NAME}/${TRAINING_DIR}
JOBNAME=${MODEL_NAME}_$(date -u +%y%m%d_%H%M%S)
CRS_BUCKET=cloud-training-demos # use the already exported data
echo $OUTDIR $REGION $JOBNAME
gsutil -m rm -rf $OUTDIR
gcloud ai-platform jobs submit training $JOBNAME \
   --region=$REGION \
   --module-name=trainer.task \
   --package-path=${PWD}/${MODEL_NAME}/trainer \
   --job-dir=$OUTDIR \
   --staging-bucket=gs://$BUCKET \
   --scale-tier=STANDARD_1 \
   --runtime-version 2.1 \
   --python-version 3.5 \
   -- \
   --train_data_paths="gs://${CRS_BUCKET}/${MODEL_NAME}/ch3/train.csv" \
   --eval_data_paths="gs://${CRS_BUCKET}/${MODEL_NAME}/ch3/valid.csv"  \
   --output_dir=$OUTDIR \
   --train_steps=100000

Challenge Exercise

Modify your solution to the challenge exercise in d_trainandevaluate.ipynb appropriately. Make sure that you implement training and deployment. Increase the size of your dataset by 10x since you are running on the cloud. Does your accuracy improve?

Copyright 2020 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