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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 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
Step1:  Packaging up the code
Take your code and put into a standard Python package structure.  <a href="taxifare/trainer/model.py">model.py</a> and <a href="taxifare/trainer/task.py">task.py</a> containing the Tensorflow code from earlier (explore the <a href="taxifare/trainer/">directory structure</a>).
Step2:  Find absolute paths to your data
Note the absolute paths below. /content is mapped in Datalab to where the home icon takes you
Step3:  Running the Python module from the command-line
Clean model training dir/output dir
Step4:  Clean model training dir/output dir
Step5:  Running locally using gcloud
Step6:  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
Step7:  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. 
<b>Use the Cloud Console link to monitor the job and do NOT proceed until the job is done.</b>
Step8:  Train on larger dataset
I have already followed the steps below and the files are already available. <b> You don't need to do the steps in this comment. </b> In the next chapter (on feature engineering), we will avoid all this manual processing by using Cloud Dataflow.
Go to http
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Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
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
# For Python Code
# Model Info
MODEL_NAME = 'taxifare'
# Model Version
MODEL_VERSION = 'v1'
# Training Directory name
TRAINING_DIR = 'taxi_trained'
# for bash
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'] = '2.5'  # Tensorflow version
%%bash
gcloud config set project $PROJECT
gcloud config set compute/region $REGION
Explanation: 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:
<ol>
<li> 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:  <b>Project ID:</b> cloud-training-demos </li>
<li> 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. </li>
</ol>
<b>Change the cell below</b> to reflect your Project ID and bucket name.
End of explanation
%%bash
find ${MODEL_NAME}
%%bash
cat ${MODEL_NAME}/trainer/model.py
Explanation: Packaging up the code
Take your code and put into a standard Python package structure.  <a href="taxifare/trainer/model.py">model.py</a> and <a href="taxifare/trainer/task.py">task.py</a> containing the Tensorflow code from earlier (explore the <a href="taxifare/trainer/">directory structure</a>).
End of explanation
%%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
Explanation: Find absolute paths to your data
Note the absolute paths below. /content is mapped in Datalab to where the home icon takes you
End of explanation
%%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}
%%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
%%bash
ls $PWD/${TRAINING_DIR}/export/exporter/
%%writefile ./test.json
{"pickuplon": -73.885262,"pickuplat": 40.773008,"dropofflon": -73.987232,"dropofflat": 40.732403,"passengers": 2}
%%bash
sudo find "/usr/lib/google-cloud-sdk/lib/googlecloudsdk/command_lib/ml_engine" -name '*.pyc' -delete
%%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
Explanation: Running the Python module from the command-line
Clean model training dir/output dir
End of explanation
%%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}
Explanation: Clean model training dir/output dir
End of explanation
%%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} 
%%bash
ls $PWD/${TRAINING_DIR}
Explanation: Running locally using gcloud
End of explanation
%%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}/${MODEL_NAME}/smallinput/
%%bash
OUTDIR=gs://${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 2.3 \
   --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=$OUTDIR \
   --train_steps=10000
Explanation: Submit training job using gcloud
First copy the training data to the cloud.  Then, launch a training job.
After you submit the job, go to the cloud console (http://console.cloud.google.com) and select <b>AI Platform | Jobs</b> to monitor progress.  
<b>Note:</b> Don't be concerned if the notebook stalls (with a blue progress bar) or returns with an error about being unable to refresh auth tokens. This is a long-lived Cloud job and work is going on in the cloud.  Use the Cloud Console link (above) to monitor the job.
End of explanation
%%bash
gsutil ls gs://${BUCKET}/${MODEL_NAME}/smallinput
Explanation: Don't be concerned if the notebook appears stalled (with a blue progress bar) or returns with an error about being unable to refresh auth tokens. This is a long-lived Cloud job and work is going on in the cloud. 
<b>Use the Cloud Console link to monitor the job and do NOT proceed until the job is done.</b>
End of explanation
%%bash
OUTDIR=gs://${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 2.3 \
   --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
Explanation: Train on larger dataset
I have already followed the steps below and the files are already available. <b> You don't need to do the steps in this comment. </b> 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:
<pre>
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
</pre>
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 <b>I have already done this and made the resulting GCS data publicly available</b>, so you don't need to do it.):
<ol>
<li> Click on the "Save As Table" button and note down the name of the dataset and table.
<li> On the BigQuery console, find the newly exported table in the left-hand-side menu, and click on the name.
<li> Click on "Export Table"
<li> 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)
<li> 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)
<li> Download the two files, remove the header line and upload it back to GCS.
</ol>
<p/>
<p/>
<h2> Run Cloud training on 1-million row dataset </h2>
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.
End of explanation
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