diff --git a/tools/ml/trainer/task.py b/tools/ml/trainer/task.py
new file mode 100644
index 0000000..7416c68
--- /dev/null
+++ b/tools/ml/trainer/task.py
@@ -0,0 +1,284 @@
+# Copyright 2018 The Chromium Authors. All rights reserved.
+# Use of this source code is governed by a BSD-style
+# license that can be found in the LICENSE file or at
+# https://developers.google.com/open-source/licenses/bsd
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import json
+import os
+import re
+
+import numpy as np
+import tensorflow as tf
+from googleapiclient import discovery
+from googleapiclient import errors
+from oauth2client.client import GoogleCredentials
+from sklearn.model_selection import train_test_split
+from tensorflow.contrib.learn.python.learn import learn_runner
+from tensorflow.contrib.learn.python.learn.estimators import run_config
+from tensorflow.contrib.learn.python.learn.utils import saved_model_export_utils
+from tensorflow.contrib.training.python.training import hparam
+
+from google.cloud.storage import blob, bucket, client
+
+import trainer.dataset
+import trainer.model
+import trainer.ml_helpers
+import trainer.top_words
+
+def generate_experiment_fn(**experiment_args):
+  """Create an experiment function.
+
+  Args:
+    experiment_args: keyword arguments to be passed through to experiment
+      See `tf.contrib.learn.Experiment` for full args.
+  Returns:
+    A function:
+      (tf.contrib.learn.RunConfig, tf.contrib.training.HParams) -> Experiment
+
+    This function is used by learn_runner to create an Experiment which
+    executes model code provided in the form of an Estimator and
+    input functions.
+  """
+  def _experiment_fn(config, hparams):
+    index_to_component = {}
+
+    if hparams.train_file:
+      with open(hparams.train_file) as f:
+        if hparams.trainer_type == 'spam':
+          training_data = trainer.ml_helpers.spam_from_file(f)
+        else:
+          training_data = trainer.ml_helpers.component_from_file(f)
+    else:
+      training_data = trainer.dataset.fetch_training_data(hparams.gcs_bucket,
+        hparams.gcs_prefix, hparams.trainer_type)
+
+    tf.logging.info('Training data received. Len: %d' % len(training_data))
+
+    if hparams.trainer_type == 'spam':
+      X, y = trainer.ml_helpers.transform_spam_csv_to_features(
+          training_data)
+    else:
+      top_list = trainer.top_words.make_top_words_list(hparams.job_dir)
+      X, y, index_to_component = trainer.ml_helpers \
+          .transform_component_csv_to_features(training_data, top_list)
+
+    tf.logging.info('Features generated')
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
+      random_state=42)
+
+    train_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x=trainer.model.feature_list_to_dict(X_train, hparams.trainer_type),
+      y=np.array(y_train),
+      num_epochs=hparams.num_epochs,
+      batch_size=hparams.train_batch_size,
+      shuffle=True
+    )
+    eval_input_fn = tf.estimator.inputs.numpy_input_fn(
+      x=trainer.model.feature_list_to_dict(X_test, hparams.trainer_type),
+      y=np.array(y_test),
+      num_epochs=None,
+      batch_size=hparams.eval_batch_size,
+      shuffle=False # Don't shuffle evaluation data
+    )
+
+    tf.logging.info('Numpy fns created')
+    if hparams.trainer_type == 'component':
+      store_component_conversion(hparams.job_dir, index_to_component)
+
+    return tf.contrib.learn.Experiment(
+      trainer.model.build_estimator(config=config,
+                                    trainer_type=hparams.trainer_type,
+                                    class_count=len(set(y))),
+      train_input_fn=train_input_fn,
+      eval_input_fn=eval_input_fn,
+      **experiment_args
+    )
+  return _experiment_fn
+
+
+def store_component_conversion(job_dir, data):
+
+  tf.logging.info('job_dir: %s' % job_dir)
+  job_info = re.search('gs://(monorail-.+)-mlengine/(component_trainer_\d+)',
+                       job_dir)
+
+  # Check if training is being done on GAE or locally.
+  if job_info:
+    project = job_info.group(1)
+    job_name = job_info.group(2)
+
+    client_obj = client.Client(project=project)
+    bucket_name = '%s-mlengine' % project
+    bucket_obj = bucket.Bucket(client_obj, bucket_name)
+
+    bucket_obj.blob = blob.Blob(job_name + '/component_index.json', bucket_obj)
+
+    bucket_obj.blob.upload_from_string(json.dumps(data),
+                                       content_type='application/json')
+
+  else:
+    paths = job_dir.split('/')
+    for y, _ in enumerate(list(range(1, len(paths))), 1):
+      if not os.path.exists("/".join(paths[:y+1])):
+        os.makedirs('/'.join(paths[:y+1]))
+    with open(job_dir + '/component_index.json', 'w') as f:
+      f.write(json.dumps(data))
+
+
+def store_eval(job_dir, results):
+
+  tf.logging.info('job_dir: %s' % job_dir)
+  job_info = re.search('gs://(monorail-.+)-mlengine/(spam_trainer_\d+)',
+                       job_dir)
+
+  # Only upload eval data if this is not being run locally.
+  if job_info:
+    project = job_info.group(1)
+    job_name = job_info.group(2)
+
+    tf.logging.info('project: %s' % project)
+    tf.logging.info('job_name: %s' % job_name)
+
+    client_obj = client.Client(project=project)
+    bucket_name = '%s-mlengine' % project
+    bucket_obj = bucket.Bucket(client_obj, bucket_name)
+
+    bucket_obj.blob = blob.Blob(job_name + '/eval_data.json', bucket_obj)
+    for key, value in results[0].items():
+      if isinstance(value, np.float32):
+        results[0][key] = value.item()
+
+    bucket_obj.blob.upload_from_string(json.dumps(results[0]),
+                                       content_type='application/json')
+
+  else:
+    tf.logging.error('Could not find bucket "%s" to output evalution to.'
+                     % job_dir)
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+
+  # Input Arguments
+  parser.add_argument(
+    '--train-file',
+    help='GCS or local path to training data',
+  )
+  parser.add_argument(
+    '--gcs-bucket',
+    help='GCS bucket for training data.',
+  )
+  parser.add_argument(
+    '--gcs-prefix',
+    help='Training data path prefix inside GCS bucket.',
+  )
+  parser.add_argument(
+    '--num-epochs',
+    help="""\
+    Maximum number of training data epochs on which to train.
+    If both --max-steps and --num-epochs are specified,
+    the training job will run for --max-steps or --num-epochs,
+    whichever occurs first. If unspecified will run for --max-steps.\
+    """,
+    type=int,
+  )
+  parser.add_argument(
+    '--train-batch-size',
+    help='Batch size for training steps',
+    type=int,
+    default=128
+  )
+  parser.add_argument(
+    '--eval-batch-size',
+    help='Batch size for evaluation steps',
+    type=int,
+    default=128
+  )
+
+  # Training arguments
+  parser.add_argument(
+    '--job-dir',
+    help='GCS location to write checkpoints and export models',
+    required=True
+  )
+
+  # Logging arguments
+  parser.add_argument(
+    '--verbosity',
+    choices=[
+        'DEBUG',
+        'ERROR',
+        'FATAL',
+        'INFO',
+        'WARN'
+    ],
+    default='INFO',
+  )
+
+  # Experiment arguments
+  parser.add_argument(
+    '--eval-delay-secs',
+    help='How long to wait before running first evaluation',
+    default=10,
+    type=int
+  )
+  parser.add_argument(
+    '--min-eval-frequency',
+    help='Minimum number of training steps between evaluations',
+    default=None,  # Use TensorFlow's default (currently, 1000)
+    type=int
+  )
+  parser.add_argument(
+    '--train-steps',
+    help="""\
+    Steps to run the training job for. If --num-epochs is not specified,
+    this must be. Otherwise the training job will run indefinitely.\
+    """,
+    type=int
+  )
+  parser.add_argument(
+    '--eval-steps',
+    help='Number of steps to run evalution for at each checkpoint',
+    default=100,
+    type=int
+  )
+  parser.add_argument(
+    '--trainer-type',
+    help='Which trainer to use (spam or component)',
+    choices=['spam', 'component'],
+    required=True
+  )
+
+  args = parser.parse_args()
+
+  tf.logging.set_verbosity(args.verbosity)
+
+  # Run the training job
+  # learn_runner pulls configuration information from environment
+  # variables using tf.learn.RunConfig and uses this configuration
+  # to conditionally execute Experiment, or param server code.
+  eval_results = learn_runner.run(
+    generate_experiment_fn(
+      min_eval_frequency=args.min_eval_frequency,
+      eval_delay_secs=args.eval_delay_secs,
+      train_steps=args.train_steps,
+      eval_steps=args.eval_steps,
+      export_strategies=[saved_model_export_utils.make_export_strategy(
+        trainer.model.SERVING_FUNCTIONS['JSON-' + args.trainer_type],
+        exports_to_keep=1,
+        default_output_alternative_key=None,
+      )],
+    ),
+    run_config=run_config.RunConfig(model_dir=args.job_dir),
+    hparams=hparam.HParams(**args.__dict__)
+  )
+
+  # Store a json blob in GCS with the results of training job (AUC of
+  # precision/recall, etc).
+  if args.trainer_type == 'spam':
+    store_eval(args.job_dir, eval_results)