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ncf_estimator_main.py
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ncf_estimator_main.py
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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""NCF framework to train and evaluate the NeuMF model.
The NeuMF model assembles both MF and MLP models under the NCF framework. Check
`neumf_model.py` for more details about the models.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import contextlib
import heapq
import json
import math
import multiprocessing
import os
import signal
import typing
# pylint: disable=g-bad-import-order
import numpy as np
from absl import app as absl_app
from absl import flags
from absl import logging
import tensorflow as tf
# pylint: enable=g-bad-import-order
from official.recommendation import constants as rconst
from official.recommendation import data_pipeline
from official.recommendation import data_preprocessing
from official.recommendation import movielens
from official.recommendation import ncf_common
from official.recommendation import neumf_model
from official.utils.flags import core as flags_core
from official.utils.logs import hooks_helper
from official.utils.logs import logger
from official.utils.logs import mlperf_helper
from official.utils.misc import distribution_utils
from official.utils.misc import model_helpers
FLAGS = flags.FLAGS
def construct_estimator(model_dir, params):
"""Construct either an Estimator or TPUEstimator for NCF.
Args:
model_dir: The model directory for the estimator
params: The params dict for the estimator
Returns:
An Estimator or TPUEstimator.
"""
distribution = ncf_common.get_v1_distribution_strategy(params)
run_config = tf.estimator.RunConfig(train_distribute=distribution,
eval_distribute=distribution)
model_fn = neumf_model.neumf_model_fn
if params["use_xla_for_gpu"]:
# TODO(seemuch): remove the contrib imput
from tensorflow.contrib.compiler import xla
logging.info("Using XLA for GPU for training and evaluation.")
model_fn = xla.estimator_model_fn(model_fn)
estimator = tf.estimator.Estimator(model_fn=model_fn, model_dir=model_dir,
config=run_config, params=params)
return estimator
def log_and_get_hooks(eval_batch_size):
"""Convenience function for hook and logger creation."""
# Create hooks that log information about the training and metric values
train_hooks = hooks_helper.get_train_hooks(
FLAGS.hooks,
model_dir=FLAGS.model_dir,
batch_size=FLAGS.batch_size, # for ExamplesPerSecondHook
tensors_to_log={"cross_entropy": "cross_entropy"}
)
run_params = {
"batch_size": FLAGS.batch_size,
"eval_batch_size": eval_batch_size,
"number_factors": FLAGS.num_factors,
"hr_threshold": FLAGS.hr_threshold,
"train_epochs": FLAGS.train_epochs,
}
benchmark_logger = logger.get_benchmark_logger()
benchmark_logger.log_run_info(
model_name="recommendation",
dataset_name=FLAGS.dataset,
run_params=run_params,
test_id=FLAGS.benchmark_test_id)
return benchmark_logger, train_hooks
def main(_):
with logger.benchmark_context(FLAGS), \
mlperf_helper.LOGGER(FLAGS.output_ml_perf_compliance_logging):
mlperf_helper.set_ncf_root(os.path.split(os.path.abspath(__file__))[0])
run_ncf(FLAGS)
def run_ncf(_):
"""Run NCF training and eval loop."""
params = ncf_common.parse_flags(FLAGS)
num_users, num_items, num_train_steps, num_eval_steps, producer = (
ncf_common.get_inputs(params))
params["num_users"], params["num_items"] = num_users, num_items
producer.start()
model_helpers.apply_clean(flags.FLAGS)
estimator = construct_estimator(model_dir=FLAGS.model_dir, params=params)
benchmark_logger, train_hooks = log_and_get_hooks(params["eval_batch_size"])
total_training_cycle = FLAGS.train_epochs // FLAGS.epochs_between_evals
target_reached = False
mlperf_helper.ncf_print(key=mlperf_helper.TAGS.TRAIN_LOOP)
for cycle_index in range(total_training_cycle):
assert FLAGS.epochs_between_evals == 1 or not mlperf_helper.LOGGER.enabled
logging.info("Starting a training cycle: {}/{}".format(
cycle_index + 1, total_training_cycle))
mlperf_helper.ncf_print(key=mlperf_helper.TAGS.TRAIN_EPOCH,
value=cycle_index)
train_input_fn = producer.make_input_fn(is_training=True)
estimator.train(input_fn=train_input_fn, hooks=train_hooks,
steps=num_train_steps)
logging.info("Beginning evaluation.")
eval_input_fn = producer.make_input_fn(is_training=False)
mlperf_helper.ncf_print(key=mlperf_helper.TAGS.EVAL_START,
value=cycle_index)
eval_results = estimator.evaluate(eval_input_fn, steps=num_eval_steps)
logging.info("Evaluation complete.")
hr = float(eval_results[rconst.HR_KEY])
ndcg = float(eval_results[rconst.NDCG_KEY])
loss = float(eval_results["loss"])
mlperf_helper.ncf_print(
key=mlperf_helper.TAGS.EVAL_TARGET,
value={"epoch": cycle_index, "value": FLAGS.hr_threshold})
mlperf_helper.ncf_print(key=mlperf_helper.TAGS.EVAL_ACCURACY,
value={"epoch": cycle_index, "value": hr})
mlperf_helper.ncf_print(
key=mlperf_helper.TAGS.EVAL_HP_NUM_NEG,
value={"epoch": cycle_index, "value": rconst.NUM_EVAL_NEGATIVES})
mlperf_helper.ncf_print(key=mlperf_helper.TAGS.EVAL_STOP, value=cycle_index)
# Benchmark the evaluation results
benchmark_logger.log_evaluation_result(eval_results)
# Log the HR and NDCG results.
logging.info(
"Iteration {}: HR = {:.4f}, NDCG = {:.4f}, Loss = {:.4f}".format(
cycle_index + 1, hr, ndcg, loss))
# If some evaluation threshold is met
if model_helpers.past_stop_threshold(FLAGS.hr_threshold, hr):
target_reached = True
break
mlperf_helper.ncf_print(key=mlperf_helper.TAGS.RUN_STOP,
value={"success": target_reached})
producer.stop_loop()
producer.join()
# Clear the session explicitly to avoid session delete error
tf.keras.backend.clear_session()
mlperf_helper.ncf_print(key=mlperf_helper.TAGS.RUN_FINAL)
if __name__ == "__main__":
logging.set_verbosity(logging.INFO)
ncf_common.define_ncf_flags()
absl_app.run(main)