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)
          # No need to features.pop('key') since it is not specified in the INPUT_COLUMNS.
          # The key passes through the graph unused.
          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



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# Defines the expected shape of the JSON feed that the model
# will receive once deployed behind a REST API in production.
def serving_input_fn():
    json_feature_placeholders = {
        'pickuplon' : tf.compat.v1.placeholder(tf.float32, [None]),
        'pickuplat' : tf.compat.v1.placeholder(tf.float32, [None]),
        'dropofflat' : tf.compat.v1.placeholder(tf.float32, [None]),
        'dropofflon' : tf.compat.v1.placeholder(tf.float32, [None]),
        'passengers' : tf.compat.v1.placeholder(tf.float32, [None]),
    }
    # You can transforma data here from the input format to the format expected by your model.
    features = json_feature_placeholders # no transformation needed
    return tf.estimator.export.ServingInputReceiver(features, json_feature_placeholders)

tf.estimator.train_and_evaluate



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def train_and_evaluate(output_dir, num_train_steps):
    estimator = tf.estimator.LinearRegressor(
                       model_dir = output_dir,
                       feature_columns = feature_cols)
    
    train_spec=tf.estimator.TrainSpec(
                       input_fn = lambda: read_dataset('./taxi-train.csv', mode = tf.estimator.ModeKeys.TRAIN),
                       max_steps = num_train_steps)

    exporter = tf.estimator.LatestExporter('exporter', serving_input_fn)

    eval_spec=tf.estimator.EvalSpec(
                       input_fn = lambda: read_dataset('./taxi-valid.csv', mode = tf.estimator.ModeKeys.EVAL),
                       steps = None,
                       start_delay_secs = 1, # start evaluating after N seconds
                       throttle_secs = 10,  # evaluate every N seconds
                       exporters = exporter)
    
    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 = 500)

Challenge Exercise

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

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