Merge pull request #1453 from saurabhclusterone/patch-1

import deepchem as dc

import tensorflow as tf

import numpy as np

import os

import json

import time

def input_fn(dataset, epochs):

    x, y, weights = dataset.make_iterator(batch_size=100, epochs=epochs).get_next()

    return {'x': x, 'weights': weights}, y

def mean_auc(labels, predictions, weights):

    metric_ops = []

    update_ops = []

    for i in range(n_tasks):

        metric, update = tf.metrics.auc(labels[:,i], predictions[:,i], weights[:,i])

        metric_ops.append(metric)

        update_ops.append(update)

    mean_metric = tf.reduce_mean(tf.stack(metric_ops))

    update_all = tf.group(*update_ops)

    return mean_metric, update_all

def run():

   os.environ['GRPC_POLL_STRATEGY'] = 'poll'

   tf.logging.set_verbosity(tf.logging.DEBUG)

   try:

       task_type = os.environ['JOB_NAME']

       task_index = int(os.environ['TASK_INDEX'])

       ps_hosts = os.environ['PS_HOSTS'].split(',')

       worker_hosts = os.environ['WORKER_HOSTS'].split(',')

       TF_CONFIG = {

          'task': {'type': task_type, 'index': task_index},

          'cluster': {

              'chief': [worker_hosts[0]],

              'worker': worker_hosts,

              'ps': ps_hosts

          },

          'environment': 'cloud'

       }

       local_ip = 'localhost:' + TF_CONFIG['cluster'][task_type][task_index].split(':')[1]

       TF_CONFIG['cluster'][task_type][task_index] = local_ip

       if (task_type in ('chief', 'master')) or (task_type == 'worker' and task_index == 0):

           TF_CONFIG['cluster']['worker'][task_index] = local_ip

           TF_CONFIG['task']['type'] = 'chief'

       os.environ['TF_CONFIG'] = json.dumps(TF_CONFIG)

   except KeyError as ex:

       print(ex)

       job_name = None

       task_index = 0

       ps_hosts = None

       worker_hosts = None

   tasks, datasets, transformers = dc.molnet.load_tox21()

   train_dataset, valid_dataset, test_dataset = datasets

   n_tasks = len(tasks)

   n_features = train_dataset.X.shape[1]

   model = dc.models.MultitaskClassifier(n_tasks, n_features, layer_sizes=[1000], dropout=0.25)

   print "featurizing columns"

   x_col = tf.feature_column.numeric_column('x', shape=(n_features,))

   weight_col = tf.feature_column.numeric_column('weights', shape=(n_tasks,))

   print "entering estimator"

   estimator = model.make_estimator(feature_columns=[x_col], weight_column=weight_col, metrics={'mean_auc': mean_auc},

                                 model_dir='/logs')

   # following lines added to run train_and_evaluate function of deepchem which is compatible for distributed training

   train_spec = tf.estimator.TrainSpec(input_fn=lambda: input_fn(train_dataset, 100), max_steps=100000)

   eval_spec = tf.estimator.EvalSpec(input_fn=lambda: input_fn(test_dataset, 1), steps=None, start_delay_secs=0,

                                  throttle_secs=30)

   tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)

if __name__ == "__main__":

  run()