Training Keras model on Cloud AI Platform

In [1]:

    

# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'


    

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import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
os.environ['TFVERSION'] = '2.0'  # not used in this notebook


    

In [3]:

    

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


    

    




Updated property [core/project].
Updated property [compute/region].

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%%bash
if ! gsutil ls | grep -q gs://${BUCKET}/babyweight/preproc; then
  gsutil mb -l ${REGION} gs://${BUCKET}
  # copy canonical set of preprocessed files if you didn't do previous notebook
  gsutil -m cp -R gs://cloud-training-demos/babyweight gs://${BUCKET}
fi


    

In [4]:

    

%%bash
gsutil ls gs://${BUCKET}/babyweight/preproc/*-00000*


    

    




gs://cloud-training-demos-ml/babyweight/preproc/eval.csv-00000-of-00012
gs://cloud-training-demos-ml/babyweight/preproc/eval_2000.csv-00000-of-00030
gs://cloud-training-demos-ml/babyweight/preproc/train.csv-00000-of-00043
gs://cloud-training-demos-ml/babyweight/preproc/train_2000.csv-00000-of-00255

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!mkdir -p babyweight_tf2/trainer


    

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%%writefile babyweight_tf2/trainer/task.py
import argparse
import json
import os

from . import model

import tensorflow as tf

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--bucket',
        help = 'GCS path to data. We assume that data is in gs://BUCKET/babyweight/preproc/',
        required = True
    )
    parser.add_argument(
        '--output_dir',
        help = 'GCS location to write checkpoints and export models',
        required = True
    )
    parser.add_argument(
        '--batch_size',
        help = 'Number of examples to compute gradient over.',
        type = int,
        default = 512
    )
    parser.add_argument(
        '--job-dir',
        help = 'this model ignores this field, but it is required by gcloud',
        default = 'junk'
    )
    parser.add_argument(
        '--nnsize',
        help = 'Hidden layer sizes to use for DNN feature columns -- provide space-separated layers',
        nargs = '+',
        type = int,
        default=[128, 32, 4]
    )
    parser.add_argument(
        '--nembeds',
        help = 'Embedding size of a cross of n key real-valued parameters',
        type = int,
        default = 3
    )

    ## TODO 1: add the new arguments here 
    parser.add_argument(
        '--train_examples',
        # TODO specify 5000 training examples as default
    )    
    parser.add_argument(
        '--pattern',
        # TODO
    )
    parser.add_argument(
        '--eval_steps',
        # specify eval steps, default is none
    )
        
    ## parse all arguments
    args = parser.parse_args()
    arguments = args.__dict__

    # unused args provided by service
    arguments.pop('job_dir', None)
    arguments.pop('job-dir', None)

    ## assign the arguments to the model variables
    output_dir = arguments.pop('output_dir')
    model.BUCKET     = arguments.pop('bucket')
    model.BATCH_SIZE = arguments.pop('batch_size')
    model.TRAIN_EXAMPLES = arguments.pop('train_examples') * 1000
    model.EVAL_STEPS = arguments.pop('eval_steps')    
    print ("Will train on {} examples using batch_size={}".format(model.TRAIN_EXAMPLES, model.BATCH_SIZE))
    model.PATTERN = arguments.pop('pattern')
    model.NEMBEDS= arguments.pop('nembeds')
    model.NNSIZE = arguments.pop('nnsize')
    print ("Will use DNN size of {}".format(model.NNSIZE))

    # Append trial_id to path if we are doing hptuning
    # This code can be removed if you are not using hyperparameter tuning
    output_dir = os.path.join(
        output_dir,
        json.loads(
            os.environ.get('TF_CONFIG', '{}')
        ).get('task', {}).get('trial', '')
    )

    # Run the training job
    model.train_and_evaluate(output_dir)


    

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%%writefile babyweight_tf2/trainer/model.py
import shutil, os, datetime
import numpy as np
import tensorflow as tf

BUCKET = None  # set from task.py
PATTERN = 'of' # gets all files

# Determine CSV, label, and key columns
CSV_COLUMNS = 'weight_pounds,is_male,mother_age,plurality,gestation_weeks,key'.split(',')
LABEL_COLUMN = 'weight_pounds'
KEY_COLUMN = 'key'

# Set default values for each CSV column
DEFAULTS = [[0.0], ['null'], [0.0], ['null'], [0.0], ['nokey']]

# Define some hyperparameters
TRAIN_EXAMPLES = 1000 * 1000
EVAL_STEPS = None
NUM_EVALS = 10
BATCH_SIZE = 512
NEMBEDS = 3
NNSIZE = [64, 16, 4]

# Create an input function reading a file using the Dataset API
def features_and_labels(row_data):
    for unwanted_col in ['key']:
        row_data.pop(unwanted_col)
    label = row_data.pop(LABEL_COLUMN)
    return row_data, label  # features, label

# load the training data
def load_dataset(pattern, batch_size=1, mode=tf.estimator.ModeKeys.EVAL):
  dataset = (tf.data.experimental.make_csv_dataset(pattern, batch_size, CSV_COLUMNS, DEFAULTS)
             .map(features_and_labels) # features, label
             )
  if mode == tf.estimator.ModeKeys.TRAIN:
        dataset = dataset.shuffle(1000).repeat()
  dataset = dataset.prefetch(1) # take advantage of multi-threading; 1=AUTOTUNE
  return dataset

## Build a Keras wide-and-deep model using its Functional API
def rmse(y_true, y_pred):
    return tf.sqrt(tf.reduce_mean(tf.square(y_pred - y_true))) 

# Helper function to handle categorical columns
def categorical_fc(name, values):
    orig = tf.feature_column.categorical_column_with_vocabulary_list(name, values)
    wrapped = tf.feature_column.indicator_column(orig)
    return orig, wrapped

def build_wd_model(dnn_hidden_units = [64, 32], nembeds = 3):
    # input layer
    deep_inputs = {
        colname : tf.keras.layers.Input(name=colname, shape=(), dtype='float32')
           for colname in ['mother_age', 'gestation_weeks']
    }
    wide_inputs = {
        colname : tf.keras.layers.Input(name=colname, shape=(), dtype='string')
           for colname in ['is_male', 'plurality']        
    }
    inputs = {**wide_inputs, **deep_inputs}
    
    # feature columns from inputs
    deep_fc = {
        colname : tf.feature_column.numeric_column(colname)
           for colname in ['mother_age', 'gestation_weeks']
    }
    wide_fc = {}
    is_male, wide_fc['is_male'] = categorical_fc('is_male', ['True', 'False', 'Unknown'])
    plurality, wide_fc['plurality'] = categorical_fc('plurality',
                      ['Single(1)', 'Twins(2)', 'Triplets(3)',
                       'Quadruplets(4)', 'Quintuplets(5)','Multiple(2+)'])
    
    # bucketize the float fields. This makes them wide
    age_buckets = tf.feature_column.bucketized_column(deep_fc['mother_age'],
                                                     boundaries=np.arange(15,45,1).tolist())
    wide_fc['age_buckets'] = tf.feature_column.indicator_column(age_buckets)
    gestation_buckets = tf.feature_column.bucketized_column(deep_fc['gestation_weeks'],
                                                     boundaries=np.arange(17,47,1).tolist())
    wide_fc['gestation_buckets'] = tf.feature_column.indicator_column(gestation_buckets)
    
    # cross all the wide columns. We have to do the crossing before we one-hot encode
    crossed = tf.feature_column.crossed_column(
        [is_male, plurality, age_buckets, gestation_buckets], hash_bucket_size=20000)
    deep_fc['crossed_embeds'] = tf.feature_column.embedding_column(crossed, nembeds)

    # the constructor for DenseFeatures takes a list of numeric columns
    # The Functional API in Keras requires that you specify: LayerConstructor()(inputs)
    wide_inputs = tf.keras.layers.DenseFeatures(wide_fc.values(), name='wide_inputs')(inputs)
    deep_inputs = tf.keras.layers.DenseFeatures(deep_fc.values(), name='deep_inputs')(inputs)
        
    # hidden layers for the deep side
    layers = [int(x) for x in dnn_hidden_units]
    deep = deep_inputs
    for layerno, numnodes in enumerate(layers):
        deep = tf.keras.layers.Dense(numnodes, activation='relu', name='dnn_{}'.format(layerno+1))(deep)        
    deep_out = deep
    
    # linear model for the wide side
    wide_out = tf.keras.layers.Dense(10, activation='relu', name='linear')(wide_inputs)
   
    # concatenate the two sides
    both = tf.keras.layers.concatenate([deep_out, wide_out], name='both')

    # final output is a linear activation because this is regression
    output = tf.keras.layers.Dense(1, activation='linear', name='weight')(both)
    model = tf.keras.models.Model(inputs, output)
    model.compile(optimizer='adam', loss='mse', metrics=[rmse, 'mse'])
    return model



# The main function
def train_and_evaluate(output_dir):
    model = build_wd_model(NNSIZE, NEMBEDS)
    print("Here is our Wide-and-Deep architecture so far:\n")
    print(model.summary())

    train_file_path = 'gs://{}/babyweight/preproc/{}*{}*'.format(BUCKET, 'train', PATTERN)
    eval_file_path = 'gs://{}/babyweight/preproc/{}*{}*'.format(BUCKET, 'eval', PATTERN)
    trainds = load_dataset('train*', BATCH_SIZE, tf.estimator.ModeKeys.TRAIN)
    evalds = load_dataset('eval*', 1000, tf.estimator.ModeKeys.EVAL)
    if EVAL_STEPS:
        evalds = evalds.take(EVAL_STEPS)
    steps_per_epoch = TRAIN_EXAMPLES // (BATCH_SIZE * NUM_EVALS)
    
    checkpoint_path = os.path.join(output_dir, 'checkpoints/babyweight')
    
    # TODO Create a checkpoint to save the model after every epoch
    # https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint
    cp_callback = # TODO
    
    # TODO Complete the model.fit statement
    # https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit
    history = model.fit(
                )
    
    EXPORT_PATH = os.path.join(output_dir, datetime.datetime.now().strftime('%Y%m%d%H%M%S'))
    tf.saved_model.save(model, EXPORT_PATH) # with default serving function
    print("Exported trained model to {}".format(EXPORT_PATH))


    

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%%bash
echo "bucket=${BUCKET}"
rm -rf babyweight_trained
export PYTHONPATH=${PYTHONPATH}:${PWD}/babyweight_tf2
python3 -m trainer.task \
  --bucket=${BUCKET} \
  --output_dir=babyweight_trained \
  --job-dir=./tmp \
  --pattern="00000-of-" --train_examples=1 --eval_steps=1 --batch_size=10


    

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%%writefile babyweight_tf2/Dockerfile
FROM gcr.io/deeplearning-platform-release/tf2-cpu
COPY trainer /babyweight_tf2/trainer
RUN apt update && \
    apt install --yes python3-pip && \
    pip3 install --upgrade --quiet tf-nightly-2.0-preview

ENV PYTHONPATH ${PYTHONPATH}:/babyweight_tf2
CMD ["python3", "-m", "trainer.task"]


    

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%%writefile babyweight_tf2/push_docker.sh
export PROJECT_ID=$(gcloud config list project --format "value(core.project)")
export IMAGE_REPO_NAME=babyweight_training_container
#export IMAGE_TAG=$(date +%Y%m%d_%H%M%S)
#export IMAGE_URI=gcr.io/$PROJECT_ID/$IMAGE_REPO_NAME:$IMAGE_TAG
export IMAGE_URI=gcr.io/$PROJECT_ID/$IMAGE_REPO_NAME

echo "Building  $IMAGE_URI"
docker build -f Dockerfile -t $IMAGE_URI ./
echo "Pushing $IMAGE_URI"
docker push $IMAGE_URI


    

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%%bash
cd babyweight_tf2
bash push_docker.sh


    

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%%bash
OUTDIR=gs://${BUCKET}/babyweight/trained_model
JOBID=babyweight_$(date -u +%y%m%d_%H%M%S)
echo $OUTDIR $REGION $JOBNAME
gsutil -m rm -rf $OUTDIR

#IMAGE=gcr.io/deeplearning-platform-release/tf2-cpu
IMAGE=gcr.io/$PROJECT/$IMAGE_REPO_NAME

gcloud beta ai-platform jobs submit training $JOBID \
   --staging-bucket=gs://$BUCKET  --region=$REGION \
   --master-image-uri=$IMAGE \
   --master-machine-type=n1-standard-4 --scale-tier=CUSTOM \
   -- \
   --bucket=${BUCKET} \
   --output_dir=${OUTDIR} \
   --train_examples=200000


    

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%%writefile hyperparam.yaml
trainingInput:
  scaleTier: STANDARD_1
  hyperparameters:
    hyperparameterMetricTag: rmse
    goal: MINIMIZE
    maxTrials: 20
    maxParallelTrials: 5
    enableTrialEarlyStopping: True
    params:
    - parameterName: batch_size
      type: INTEGER
      minValue: 8
      maxValue: 512
      scaleType: UNIT_LOG_SCALE
    - parameterName: nembeds
      type: INTEGER
      minValue: 3
      maxValue: 30
      scaleType: UNIT_LINEAR_SCALE
    - parameterName: nnsize
      type: INTEGER
      minValue: 64
      maxValue: 512
      scaleType: UNIT_LOG_SCALE


    

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%%bash
OUTDIR=gs://${BUCKET}/babyweight/hyperparam
JOBNAME=babyweight_$(date -u +%y%m%d_%H%M%S)
echo $OUTDIR $REGION $JOBNAME
gsutil -m rm -rf $OUTDIR
gcloud ai-platform jobs submit training $JOBNAME \
  --region=$REGION \
  --module-name=trainer.task \
  --package-path=$(pwd)/babyweight/trainer \
  --job-dir=$OUTDIR \
  --staging-bucket=gs://$BUCKET \
  --scale-tier=STANDARD_1 \
  --config=hyperparam.yaml \
  --runtime-version=$TFVERSION \
  -- \
  --bucket=${BUCKET} \
  --output_dir=${OUTDIR} \
  --eval_steps=10 \
  --train_examples=20000


    

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%%bash
OUTDIR=gs://${BUCKET}/babyweight/trained_model_tuned
JOBNAME=babyweight_$(date -u +%y%m%d_%H%M%S)
echo $OUTDIR $REGION $JOBNAME
gsutil -m rm -rf $OUTDIR
gcloud ai-platform jobs submit training $JOBNAME \
  --region=$REGION \
  --module-name=trainer.task \
  --package-path=$(pwd)/babyweight/trainer \
  --job-dir=$OUTDIR \
  --staging-bucket=gs://$BUCKET \
  --scale-tier=STANDARD_1 \
  --runtime-version=$TFVERSION \
  -- \
  --bucket=${BUCKET} \
  --output_dir=${OUTDIR} \
  --train_examples=20000 --batch_size=35 --nembeds=16 --nnsize=281