2d. Distributed training and monitoring

In this notebook, we refactor to call train_and_evaluate instead of hand-coding our ML pipeline. This allows us to carry out evaluation as part of our training loop instead of as a separate step. It also adds in failure-handling that is necessary for distributed training capabilities.



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



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# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.1



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from google.cloud import bigquery
import tensorflow as tf
import numpy as np
import shutil
print(tf.__version__)

Input

Read data created in Lab1a, but this time make it more general, so that we are reading in batches. Instead of using Pandas, we will use add a filename queue to the TensorFlow graph.



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CSV_COLUMNS = ['fare_amount', 'pickuplon','pickuplat','dropofflon','dropofflat','passengers', 'key']
LABEL_COLUMN = 'fare_amount'
DEFAULTS = [[0.0], [-74.0], [40.0], [-74.0], [40.7], [1.0], ['nokey']]

def read_dataset(filename, mode, batch_size = 512):
  def decode_csv(value_column):
    columns = tf.compat.v1.decode_csv(value_column, record_defaults = DEFAULTS)
    features = dict(zip(CSV_COLUMNS, columns))
    label = features.pop(LABEL_COLUMN)
    return features, label

  # Create list of file names that match "glob" pattern (i.e. data_file_*.csv)
  filenames_dataset = tf.data.Dataset.list_files(filename)
  # Read lines from text files
  textlines_dataset = filenames_dataset.flat_map(tf.data.TextLineDataset)
  # Parse text lines as comma-separated values (CSV)
  dataset = textlines_dataset.map(decode_csv)

  # Note:
  # use tf.data.Dataset.flat_map to apply one to many transformations (here: filename -> text lines)
  # use tf.data.Dataset.map      to apply one to one  transformations (here: text line -> feature list)

  if mode == tf.estimator.ModeKeys.TRAIN:
      num_epochs = None # indefinitely
      dataset = dataset.shuffle(buffer_size = 10 * batch_size)
  else:
      num_epochs = 1 # end-of-input after this

  dataset = dataset.repeat(num_epochs).batch(batch_size)

  return dataset

Create features out of input data

For now, pass these through. (same as previous lab)



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INPUT_COLUMNS = [
    tf.feature_column.numeric_column('pickuplon'),
    tf.feature_column.numeric_column('pickuplat'),
    tf.feature_column.numeric_column('dropofflat'),
    tf.feature_column.numeric_column('dropofflon'),
    tf.feature_column.numeric_column('passengers'),
]

def add_more_features(feats):
  # Nothing to add (yet!)
  return feats

feature_cols = add_more_features(INPUT_COLUMNS)

Serving input function

Defines the expected shape of the JSON feed that the modelwill receive once deployed behind a REST API in production.



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## TODO: Create serving input function
def serving_input_fn():
  #ADD CODE HERE
  return tf.estimator.export.ServingInputReceiver(features, json_feature_placeholders)

tf.estimator.train_and_evaluate



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## TODO: Create train and evaluate function using tf.estimator
def train_and_evaluate(output_dir, num_train_steps):
  #ADD CODE HERE
  tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)

Run training



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OUTDIR = './taxi_trained'
shutil.rmtree(OUTDIR, ignore_errors = True) # start fresh each time
tf.compat.v1.summary.FileWriterCache.clear()
train_and_evaluate(OUTDIR, num_train_steps = 2000)

Challenge Exercise

Modify your solution to the challenge exercise in c_dataset.ipynb appropriately.

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