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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:
 <h1> Hyper-parameter tuning </h1>
In this notebook, you will learn how to carry out hyper-parameter tuning.
This notebook takes several hours to run.
<h2> Environment variables for project and bucket </h2>
Change the cell below to reflect your Project ID and bucket name. See Lab 3a for setup instructions.
Step1:  <h1> 1. Command-line parameters to task.py </h1>
Note the command-line parameters to task.py.  These are the things that could be hypertuned if we wanted.
Step2:  <h1> 2. Evaluation metric </h1>
We add a special evaluation metric. It could be any objective function we want.
Step3:  <h1> 3. Make sure outputs do not clobber each other </h1>
We append the trial-number to the output directory.
Step4:  <h1> 4. Create hyper-parameter configuration </h1>
The file specifies the search region in parameter space.  Cloud MLE carries out a smart search algorithm within these constraints (i.e. it does not try out every single value).
Step5:  <h1> 5. Run the training job </h1>
Just --config to the usual training command.
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Python Code:
import os
PROJECT = 'cloud-training-demos'    # CHANGE THIS
REGION = 'us-central1' # Choose an available region for Cloud MLE from https://cloud.google.com/ml-engine/docs/regions.
BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME. Use a regional bucket in the region you selected.
# for bash
os.environ['PROJECT'] = PROJECT
os.environ['BUCKET'] = BUCKET
os.environ['REGION'] = REGION
%%bash
gcloud config set project $PROJECT
gcloud config set compute/region $REGION
Explanation: <h1> Hyper-parameter tuning </h1>
In this notebook, you will learn how to carry out hyper-parameter tuning.
This notebook takes several hours to run.
<h2> Environment variables for project and bucket </h2>
Change the cell below to reflect your Project ID and bucket name. See Lab 3a for setup instructions.
End of explanation
!grep -A 2 add_argument taxifare/trainer/task.py
Explanation: <h1> 1. Command-line parameters to task.py </h1>
Note the command-line parameters to task.py.  These are the things that could be hypertuned if we wanted.
End of explanation
!grep -A 5 add_eval_metrics taxifare/trainer/model.py
Explanation: <h1> 2. Evaluation metric </h1>
We add a special evaluation metric. It could be any objective function we want.
End of explanation
!grep -A 5 "trial" taxifare/trainer/task.py
Explanation: <h1> 3. Make sure outputs do not clobber each other </h1>
We append the trial-number to the output directory.
End of explanation
%%writefile hyperparam.yaml
trainingInput:
  scaleTier: STANDARD_1
  hyperparameters:
    goal: MINIMIZE
    maxTrials: 30
    maxParallelTrials: 3
    hyperparameterMetricTag: rmse
    params:
    - parameterName: train_batch_size
      type: INTEGER
      minValue: 64
      maxValue: 512
      scaleType: UNIT_LOG_SCALE
    - parameterName: nbuckets
      type: INTEGER
      minValue: 10
      maxValue: 20
      scaleType: UNIT_LINEAR_SCALE
    - parameterName: hidden_units
      type: CATEGORICAL
      categoricalValues: ["128 32", "256 128 16", "64 64 64 8"]       
Explanation: <h1> 4. Create hyper-parameter configuration </h1>
The file specifies the search region in parameter space.  Cloud MLE carries out a smart search algorithm within these constraints (i.e. it does not try out every single value).
End of explanation
%%bash
OUTDIR=gs://${BUCKET}/taxifare/ch4/taxi_trained
JOBNAME=lab4a_$(date -u +%y%m%d_%H%M%S)
echo $OUTDIR $REGION $JOBNAME
gsutil -m rm -rf $OUTDIR
gcloud ml-engine jobs submit training $JOBNAME \
   --region=$REGION \
   --module-name=trainer.task \
   --package-path=${PWD}/taxifare/trainer \
   --job-dir=$OUTDIR \
   --staging-bucket=gs://$BUCKET \
   --scale-tier=STANDARD_1 \
   --runtime-version=1.4 \
   --config=hyperparam.yaml \
   -- \
   --train_data_paths="gs://$BUCKET/taxifare/ch4/taxi_preproc/train*" \
   --eval_data_paths="gs://${BUCKET}/taxifare/ch4/taxi_preproc/valid*"  \
   --output_dir=$OUTDIR \
   --train_steps=5000
Explanation: <h1> 5. Run the training job </h1>
Just --config to the usual training command.
End of explanation
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