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keras_spark3_rossmann.py
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keras_spark3_rossmann.py
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# Copyright 2017 onwards, fast.ai, Inc.
# Modifications copyright (C) 2018 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import argparse
import datetime
import h5py
import io
import os
import sys
import pyarrow as pa
from pyspark import SparkConf, Row
from pyspark.sql import SparkSession
import pyspark.sql.types as T
import pyspark.sql.functions as F
parser = argparse.ArgumentParser(description='Keras Spark3 Rossmann Run Example',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--processing-master',
help='spark cluster to use for light processing (data preparation & prediction).'
'If set to None, uses current default cluster. Cluster should be set up to provide'
'one task per CPU core. Example: spark://hostname:7077')
parser.add_argument('--training-master', default='local-cluster[2,1,1024]',
help='spark cluster to use for training. If set to None, uses current default cluster. Cluster'
'should be set up to provide a Spark task per multiple CPU cores, or per GPU, e.g. by'
'supplying `-c <NUM_GPUS>` in Spark Standalone mode. Example: spark://hostname:7077')
parser.add_argument('--num-proc', type=int, default=4,
help='number of worker processes for training, default: `spark.default.parallelism`')
parser.add_argument('--learning-rate', type=float, default=0.0001,
help='initial learning rate')
parser.add_argument('--batch-size', type=int, default=100,
help='batch size')
parser.add_argument('--epochs', type=int, default=100,
help='number of epochs to train')
parser.add_argument('--sample-rate', type=float,
help='desired sampling rate. Useful to set to low number (e.g. 0.01) to make sure that '
'end-to-end process works')
parser.add_argument('--data-dir', default='file://' + os.getcwd(),
help='location of data on local filesystem (prefixed with file://) or on HDFS')
parser.add_argument('--local-submission-csv', default='submission.csv',
help='output submission predictions CSV on local filesystem (without file:// prefix)')
parser.add_argument('--local-checkpoint-file', default='checkpoint.h5',
help='model checkpoint on local filesystem (without file:// prefix)')
if __name__ == '__main__':
args = parser.parse_args()
# Location of discovery script on local filesystem.
DISCOVERY_SCRIPT = 'get_gpu_resources.sh'
# HDFS driver to use with Petastorm.
PETASTORM_HDFS_DRIVER = 'libhdfs'
# Whether to infer on GPU.
GPU_INFERENCE_ENABLED = False
# Cluster for GPU inference.
GPU_INFERENCE_CLUSTER = 'local-cluster[2,1,1024]' # or 'spark://hostname:7077'
# ================ #
# DATA PREPARATION #
# ================ #
print('================')
print('Data preparation')
print('================')
# Create Spark session for data preparation.
conf = SparkConf().setAppName('data_prep').set('spark.sql.shuffle.partitions', '16')
if args.processing_master:
conf.setMaster(args.processing_master)
spark = SparkSession.builder.config(conf=conf).getOrCreate()
train_csv = spark.read.csv('%s/train.csv' % args.data_dir, header=True)
test_csv = spark.read.csv('%s/test.csv' % args.data_dir, header=True)
store_csv = spark.read.csv('%s/store.csv' % args.data_dir, header=True)
store_states_csv = spark.read.csv('%s/store_states.csv' % args.data_dir, header=True)
state_names_csv = spark.read.csv('%s/state_names.csv' % args.data_dir, header=True)
google_trend_csv = spark.read.csv('%s/googletrend.csv' % args.data_dir, header=True)
weather_csv = spark.read.csv('%s/weather.csv' % args.data_dir, header=True)
def expand_date(df):
df = df.withColumn('Date', df.Date.cast(T.DateType()))
return df \
.withColumn('Year', F.year(df.Date)) \
.withColumn('Month', F.month(df.Date)) \
.withColumn('Week', F.weekofyear(df.Date)) \
.withColumn('Day', F.dayofmonth(df.Date))
def prepare_google_trend():
# Extract week start date and state.
google_trend_all = google_trend_csv \
.withColumn('Date', F.regexp_extract(google_trend_csv.week, '(.*?) -', 1)) \
.withColumn('State', F.regexp_extract(google_trend_csv.file, 'Rossmann_DE_(.*)', 1))
# Map state NI -> HB,NI to align with other data sources.
google_trend_all = google_trend_all \
.withColumn('State', F.when(google_trend_all.State == 'NI', 'HB,NI').otherwise(google_trend_all.State))
# Expand dates.
return expand_date(google_trend_all)
def add_elapsed(df, cols):
def add_elapsed_column(col, asc):
def fn(rows):
last_store, last_date = None, None
for r in rows:
if last_store != r.Store:
last_store = r.Store
last_date = r.Date
if r[col]:
last_date = r.Date
fields = r.asDict().copy()
fields[('After' if asc else 'Before') + col] = (r.Date - last_date).days
yield Row(**fields)
return fn
df = df.repartition(df.Store)
for asc in [False, True]:
sort_col = df.Date.asc() if asc else df.Date.desc()
rdd = df.sortWithinPartitions(df.Store.asc(), sort_col).rdd
for col in cols:
rdd = rdd.mapPartitions(add_elapsed_column(col, asc))
df = rdd.toDF()
return df
def prepare_df(df):
num_rows = df.count()
# Expand dates.
df = expand_date(df)
df = df \
.withColumn('Open', df.Open != '0') \
.withColumn('Promo', df.Promo != '0') \
.withColumn('StateHoliday', df.StateHoliday != '0') \
.withColumn('SchoolHoliday', df.SchoolHoliday != '0')
# Merge in store information.
store = store_csv.join(store_states_csv, 'Store')
df = df.join(store, 'Store')
# Merge in Google Trend information.
google_trend_all = prepare_google_trend()
df = df.join(google_trend_all, ['State', 'Year', 'Week']).select(df['*'], google_trend_all.trend)
# Merge in Google Trend for whole Germany.
google_trend_de = google_trend_all[google_trend_all.file == 'Rossmann_DE'].withColumnRenamed('trend', 'trend_de')
df = df.join(google_trend_de, ['Year', 'Week']).select(df['*'], google_trend_de.trend_de)
# Merge in weather.
weather = weather_csv.join(state_names_csv, weather_csv.file == state_names_csv.StateName)
df = df.join(weather, ['State', 'Date'])
# Fix null values.
df = df \
.withColumn('CompetitionOpenSinceYear', F.coalesce(df.CompetitionOpenSinceYear, F.lit(1900))) \
.withColumn('CompetitionOpenSinceMonth', F.coalesce(df.CompetitionOpenSinceMonth, F.lit(1))) \
.withColumn('Promo2SinceYear', F.coalesce(df.Promo2SinceYear, F.lit(1900))) \
.withColumn('Promo2SinceWeek', F.coalesce(df.Promo2SinceWeek, F.lit(1)))
# Days & months competition was open, cap to 2 years.
df = df.withColumn('CompetitionOpenSince',
F.to_date(F.format_string('%s-%s-15', df.CompetitionOpenSinceYear,
df.CompetitionOpenSinceMonth)))
df = df.withColumn('CompetitionDaysOpen',
F.when(df.CompetitionOpenSinceYear > 1900,
F.greatest(F.lit(0), F.least(F.lit(360 * 2), F.datediff(df.Date, df.CompetitionOpenSince))))
.otherwise(0))
df = df.withColumn('CompetitionMonthsOpen', (df.CompetitionDaysOpen / 30).cast(T.IntegerType()))
# Days & weeks of promotion, cap to 25 weeks.
df = df.withColumn('Promo2Since',
F.expr('date_add(format_string("%s-01-01", Promo2SinceYear), (cast(Promo2SinceWeek as int) - 1) * 7)'))
df = df.withColumn('Promo2Days',
F.when(df.Promo2SinceYear > 1900,
F.greatest(F.lit(0), F.least(F.lit(25 * 7), F.datediff(df.Date, df.Promo2Since))))
.otherwise(0))
df = df.withColumn('Promo2Weeks', (df.Promo2Days / 7).cast(T.IntegerType()))
# Check that we did not lose any rows through inner joins.
assert num_rows == df.count(), 'lost rows in joins'
return df
def build_vocabulary(df, cols):
vocab = {}
for col in cols:
values = [r[0] for r in df.select(col).distinct().collect()]
col_type = type([x for x in values if x is not None][0])
default_value = col_type()
vocab[col] = sorted(values, key=lambda x: x or default_value)
return vocab
def cast_columns(df, cols):
for col in cols:
df = df.withColumn(col, F.coalesce(df[col].cast(T.FloatType()), F.lit(0.0)))
return df
def lookup_columns(df, vocab):
def lookup(mapping):
def fn(v):
return mapping.index(v)
return F.udf(fn, returnType=T.IntegerType())
for col, mapping in vocab.items():
df = df.withColumn(col, lookup(mapping)(df[col]))
return df
if args.sample_rate:
train_csv = train_csv.sample(withReplacement=False, fraction=args.sample_rate)
test_csv = test_csv.sample(withReplacement=False, fraction=args.sample_rate)
# Prepare data frames from CSV files.
train_df = prepare_df(train_csv).cache()
test_df = prepare_df(test_csv).cache()
# Add elapsed times from holidays & promos, the data spanning training & test datasets.
elapsed_cols = ['Promo', 'StateHoliday', 'SchoolHoliday']
elapsed = add_elapsed(train_df.select('Date', 'Store', *elapsed_cols)
.unionAll(test_df.select('Date', 'Store', *elapsed_cols)),
elapsed_cols)
# Join with elapsed times.
train_df = train_df \
.join(elapsed, ['Date', 'Store']) \
.select(train_df['*'], *[prefix + col for prefix in ['Before', 'After'] for col in elapsed_cols])
test_df = test_df \
.join(elapsed, ['Date', 'Store']) \
.select(test_df['*'], *[prefix + col for prefix in ['Before', 'After'] for col in elapsed_cols])
# Filter out zero sales.
train_df = train_df.filter(train_df.Sales > 0)
print('===================')
print('Prepared data frame')
print('===================')
train_df.show()
categorical_cols = [
'Store', 'State', 'DayOfWeek', 'Year', 'Month', 'Day', 'Week', 'CompetitionMonthsOpen', 'Promo2Weeks', 'StoreType',
'Assortment', 'PromoInterval', 'CompetitionOpenSinceYear', 'Promo2SinceYear', 'Events', 'Promo',
'StateHoliday', 'SchoolHoliday'
]
continuous_cols = [
'CompetitionDistance', 'Max_TemperatureC', 'Mean_TemperatureC', 'Min_TemperatureC', 'Max_Humidity',
'Mean_Humidity', 'Min_Humidity', 'Max_Wind_SpeedKm_h', 'Mean_Wind_SpeedKm_h', 'CloudCover', 'trend', 'trend_de',
'BeforePromo', 'AfterPromo', 'AfterStateHoliday', 'BeforeStateHoliday', 'BeforeSchoolHoliday', 'AfterSchoolHoliday'
]
all_cols = categorical_cols + continuous_cols
# Select features.
train_df = train_df.select(*(all_cols + ['Sales', 'Date'])).cache()
test_df = test_df.select(*(all_cols + ['Id', 'Date'])).cache()
# Build vocabulary of categorical columns.
vocab = build_vocabulary(train_df.select(*categorical_cols)
.unionAll(test_df.select(*categorical_cols)).cache(),
categorical_cols)
# Cast continuous columns to float & lookup categorical columns.
train_df = cast_columns(train_df, continuous_cols + ['Sales'])
train_df = lookup_columns(train_df, vocab)
test_df = cast_columns(test_df, continuous_cols)
test_df = lookup_columns(test_df, vocab)
# Split into training & validation.
# Test set is in 2015, use the same period in 2014 from the training set as a validation set.
test_min_date = test_df.agg(F.min(test_df.Date)).collect()[0][0]
test_max_date = test_df.agg(F.max(test_df.Date)).collect()[0][0]
a_year = datetime.timedelta(365)
val_df = train_df.filter((test_min_date - a_year <= train_df.Date) & (train_df.Date < test_max_date - a_year))
train_df = train_df.filter((train_df.Date < test_min_date - a_year) | (train_df.Date >= test_max_date - a_year))
# Determine max Sales number.
max_sales = train_df.agg(F.max(train_df.Sales)).collect()[0][0]
print('===================================')
print('Data frame with transformed columns')
print('===================================')
train_df.show()
print('================')
print('Data frame sizes')
print('================')
train_rows, val_rows, test_rows = train_df.count(), val_df.count(), test_df.count()
print('Training: %d' % train_rows)
print('Validation: %d' % val_rows)
print('Test: %d' % test_rows)
# Save data frames as Parquet files.
train_df.write.parquet('%s/train_df.parquet' % args.data_dir, mode='overwrite')
val_df.write.parquet('%s/val_df.parquet' % args.data_dir, mode='overwrite')
test_df.write.parquet('%s/test_df.parquet' % args.data_dir, mode='overwrite')
spark.stop()
# ============== #
# MODEL TRAINING #
# ============== #
print('==============')
print('Model training')
print('==============')
import tensorflow as tf
from tensorflow.keras.layers import Input, Embedding, Concatenate, Dense, Flatten, Reshape, BatchNormalization, Dropout
import tensorflow.keras.backend as K
import horovod.spark
import horovod.tensorflow.keras as hvd
def exp_rmspe(y_true, y_pred):
"""Competition evaluation metric, expects logarithic inputs."""
pct = tf.square((tf.exp(y_true) - tf.exp(y_pred)) / tf.exp(y_true))
# Compute mean excluding stores with zero denominator.
x = tf.reduce_sum(tf.where(y_true > 0.001, pct, tf.zeros_like(pct)))
y = tf.reduce_sum(tf.where(y_true > 0.001, tf.ones_like(pct), tf.zeros_like(pct)))
return tf.sqrt(x / y)
def act_sigmoid_scaled(x):
"""Sigmoid scaled to logarithm of maximum sales scaled by 20%."""
return tf.nn.sigmoid(x) * tf.log(max_sales) * 1.2
CUSTOM_OBJECTS = {'exp_rmspe': exp_rmspe,
'act_sigmoid_scaled': act_sigmoid_scaled}
def serialize_model(model):
"""Serialize model into byte array."""
bio = io.BytesIO()
with h5py.File(bio) as f:
model.save(f)
return bio.getvalue()
def deserialize_model(model_bytes, load_model_fn):
"""Deserialize model from byte array."""
bio = io.BytesIO(model_bytes)
with h5py.File(bio) as f:
return load_model_fn(f, custom_objects=CUSTOM_OBJECTS)
# Do not use GPU for the session creation.
config = tf.ConfigProto(device_count={'GPU': 0})
K.set_session(tf.Session(config=config))
# Build the model.
inputs = {col: Input(shape=(1,), name=col) for col in all_cols}
embeddings = [Embedding(len(vocab[col]), 10, input_length=1, name='emb_' + col)(inputs[col])
for col in categorical_cols]
continuous_bn = Concatenate()([Reshape((1, 1), name='reshape_' + col)(inputs[col])
for col in continuous_cols])
continuous_bn = BatchNormalization()(continuous_bn)
x = Concatenate()(embeddings + [continuous_bn])
x = Flatten()(x)
x = Dense(1000, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x)
x = Dense(1000, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x)
x = Dense(1000, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x)
x = Dense(500, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x)
x = Dropout(0.5)(x)
output = Dense(1, activation=act_sigmoid_scaled)(x)
model = tf.keras.Model([inputs[f] for f in all_cols], output)
model.summary()
# Horovod: add Distributed Optimizer.
opt = tf.keras.optimizers.Adam(lr=args.learning_rate, epsilon=1e-3)
opt = hvd.DistributedOptimizer(opt)
model.compile(opt, 'mae', metrics=[exp_rmspe])
model_bytes = serialize_model(model)
def train_fn(model_bytes):
# Make sure pyarrow is referenced before anything else to avoid segfault due to conflict
# with TensorFlow libraries. Use `pa` package reference to ensure it's loaded before
# functions like `deserialize_model` which are implemented at the top level.
# See https://jira.apache.org/jira/browse/ARROW-3346
pa
import atexit
import horovod.tensorflow.keras as hvd
from horovod.spark.task import get_available_devices
import os
from petastorm import make_batch_reader
from petastorm.tf_utils import make_petastorm_dataset
import tempfile
import tensorflow as tf
import tensorflow.keras.backend as K
import shutil
# Horovod: initialize Horovod inside the trainer.
hvd.init()
# Horovod: pin GPU to be used to process local rank (one GPU per process), if GPUs are available.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.visible_device_list = get_available_devices()[0]
K.set_session(tf.Session(config=config))
# Horovod: restore from checkpoint, use hvd.load_model under the hood.
model = deserialize_model(model_bytes, hvd.load_model)
# Horovod: adjust learning rate based on number of processes.
scaled_lr = K.get_value(model.optimizer.lr) * hvd.size()
K.set_value(model.optimizer.lr, scaled_lr)
# Horovod: print summary logs on the first worker.
verbose = 2 if hvd.rank() == 0 else 0
callbacks = [
# Horovod: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
hvd.callbacks.BroadcastGlobalVariablesCallback(root_rank=0),
# Horovod: average metrics among workers at the end of every epoch.
#
# Note: This callback must be in the list before the ReduceLROnPlateau,
# TensorBoard, or other metrics-based callbacks.
hvd.callbacks.MetricAverageCallback(),
# Horovod: using `lr = 1.0 * hvd.size()` from the very beginning leads to worse final
# accuracy. Scale the learning rate `lr = 1.0` ---> `lr = 1.0 * hvd.size()` during
# the first five epochs. See https://arxiv.org/abs/1706.02677 for details.
hvd.callbacks.LearningRateWarmupCallback(initial_lr=scaled_lr, warmup_epochs=5, verbose=verbose),
# Reduce LR if the metric is not improved for 10 epochs, and stop training
# if it has not improved for 20 epochs.
tf.keras.callbacks.ReduceLROnPlateau(monitor='val_exp_rmspe', patience=10, verbose=verbose),
tf.keras.callbacks.EarlyStopping(monitor='val_exp_rmspe', mode='min', patience=20, verbose=verbose),
tf.keras.callbacks.TerminateOnNaN()
]
# Model checkpoint location.
ckpt_dir = tempfile.mkdtemp()
ckpt_file = os.path.join(ckpt_dir, 'checkpoint.h5')
atexit.register(lambda: shutil.rmtree(ckpt_dir))
# Horovod: save checkpoints only on the first worker to prevent other workers from corrupting them.
if hvd.rank() == 0:
callbacks.append(tf.keras.callbacks.ModelCheckpoint(ckpt_file, monitor='val_exp_rmspe', mode='min',
save_best_only=True))
# Make Petastorm readers.
with make_batch_reader('%s/train_df.parquet' % args.data_dir, num_epochs=None,
cur_shard=hvd.rank(), shard_count=hvd.size(),
hdfs_driver=PETASTORM_HDFS_DRIVER) as train_reader:
with make_batch_reader('%s/val_df.parquet' % args.data_dir, num_epochs=None,
cur_shard=hvd.rank(), shard_count=hvd.size(),
hdfs_driver=PETASTORM_HDFS_DRIVER) as val_reader:
# Convert readers to tf.data.Dataset.
train_ds = make_petastorm_dataset(train_reader) \
.apply(tf.data.experimental.unbatch()) \
.shuffle(int(train_rows / hvd.size())) \
.batch(args.batch_size) \
.map(lambda x: (tuple(getattr(x, col) for col in all_cols), tf.log(x.Sales)))
val_ds = make_petastorm_dataset(val_reader) \
.apply(tf.data.experimental.unbatch()) \
.batch(args.batch_size) \
.map(lambda x: (tuple(getattr(x, col) for col in all_cols), tf.log(x.Sales)))
history = model.fit(train_ds,
validation_data=val_ds,
steps_per_epoch=int(train_rows / args.batch_size / hvd.size()),
validation_steps=int(val_rows / args.batch_size / hvd.size()),
callbacks=callbacks,
verbose=verbose,
epochs=args.epochs)
# Dataset API usage currently displays a wall of errors upon termination.
# This global model registration ensures clean termination.
# Tracked in https://github.com/tensorflow/tensorflow/issues/24570
globals()['_DATASET_FINALIZATION_HACK'] = model
if hvd.rank() == 0:
with open(ckpt_file, 'rb') as f:
return history.history, f.read()
def set_gpu_conf(conf):
# This config will change depending on your cluster setup.
#
# 1. Standalone Cluster
# - Must configure spark.worker.* configs as below.
#
# 2. YARN
# - Requires YARN 3.1 or higher to support GPUs
# - Cluster should be configured to have isolation on so that
# multiple executors don’t see the same GPU on the same host.
# - If you don’t have isolation then you would require a different discovery script
# or other way to make sure that 2 executors don’t try to use same GPU.
#
# 3. Kubernetes
# - Requires GPU support and isolation.
# - Add conf.set(“spark.executor.resource.gpu.discoveryScript”, DISCOVERY_SCRIPT)
# - Add conf.set(“spark.executor.resource.gpu.vendor”, “nvidia.com”)
conf = conf.set("spark.test.home", os.environ.get('SPARK_HOME'))
conf = conf.set("spark.worker.resource.gpu.discoveryScript", DISCOVERY_SCRIPT)
conf = conf.set("spark.worker.resource.gpu.amount", 1)
conf = conf.set("spark.task.resource.gpu.amount", "1")
conf = conf.set("spark.executor.resource.gpu.amount", "1")
return conf
# Create Spark session for training.
conf = SparkConf().setAppName('training')
if args.training_master:
conf.setMaster(args.training_master)
conf = set_gpu_conf(conf)
spark = SparkSession.builder.config(conf=conf).getOrCreate()
# Horovod: run training.
history, best_model_bytes = \
horovod.spark.run(train_fn, args=(model_bytes,), num_proc=args.num_proc,
stdout=sys.stdout, stderr=sys.stderr, verbose=2,
prefix_output_with_timestamp=True)[0]
best_val_rmspe = min(history['val_exp_rmspe'])
print('Best RMSPE: %f' % best_val_rmspe)
# Write checkpoint.
with open(args.local_checkpoint_file, 'wb') as f:
f.write(best_model_bytes)
print('Written checkpoint to %s' % args.local_checkpoint_file)
spark.stop()
# ================ #
# FINAL PREDICTION #
# ================ #
print('================')
print('Final prediction')
print('================')
# Create Spark session for prediction.
conf = SparkConf().setAppName('prediction') \
.setExecutorEnv('LD_LIBRARY_PATH', os.environ.get('LD_LIBRARY_PATH')) \
.setExecutorEnv('PATH', os.environ.get('PATH'))
if GPU_INFERENCE_ENABLED:
if GPU_INFERENCE_CLUSTER:
conf.setMaster(GPU_INFERENCE_CLUSTER)
conf = set_gpu_conf(conf)
else:
if args.processing_master:
conf.setMaster(args.processing_master)
spark = SparkSession.builder.config(conf=conf).getOrCreate()
def predict_fn(model_bytes):
def fn(rows):
import math
import tensorflow as tf
import tensorflow.keras.backend as K
if GPU_INFERENCE_ENABLED:
from pyspark import TaskContext
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.visible_device_list = TaskContext.get().resources()['gpu'].addresses[0]
K.set_session(tf.Session(config=config))
else:
# Do not use GPUs for prediction, use single CPU core per task.
config = tf.ConfigProto(device_count={'GPU': 0})
config.inter_op_parallelism_threads = 1
config.intra_op_parallelism_threads = 1
K.set_session(tf.Session(config=config))
# Restore from checkpoint.
model = deserialize_model(model_bytes, tf.keras.models.load_model)
# Perform predictions.
for row in rows:
fields = row.asDict().copy()
# Convert from log domain to real Sales numbers.
log_sales = model.predict_on_batch([[row[col]] for col in all_cols])[0]
# Add 'Sales' column with prediction results.
fields['Sales'] = math.exp(log_sales)
yield Row(**fields)
return fn
# Submit a Spark job to do inference. Horovod framework is not involved here.
pred_df = spark.read.parquet('%s/test_df.parquet' % args.data_dir) \
.rdd.mapPartitions(predict_fn(best_model_bytes)).toDF()
submission_df = pred_df.select(pred_df.Id.cast(T.IntegerType()), pred_df.Sales).toPandas()
submission_df.sort_values(by=['Id']).to_csv(args.local_submission_csv, index=False)
print('Saved predictions to %s' % args.local_submission_csv)
spark.stop()