DeepLearningExamples/TensorFlow2/Recommendation/WideAndDeep/trainer/run.py

# Copyright (c) 2021, NVIDIA CORPORATION. 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
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import horovod.tensorflow as hvd
import tensorflow as tf
from trainer.utils.benchmark import ThroughputCalculator
from trainer.utils.evaluator import Evaluator
from trainer.utils.schedulers import LearningRateScheduler
from trainer.utils.trainer import Trainer


def run(args, model, config):
    train_dataset = config["train_dataset"]
    eval_dataset = config["eval_dataset"]
    steps_per_epoch = len(train_dataset)

    steps = int(steps_per_epoch * args.num_epochs)
    deep_optimizer = tf.keras.optimizers.RMSprop(
        learning_rate=args.deep_learning_rate, rho=0.5
    )

    wide_optimizer = tf.keras.optimizers.Ftrl(learning_rate=args.linear_learning_rate)

    if not args.cpu:
        deep_optimizer = hvd.DistributedOptimizer(deep_optimizer)
        wide_optimizer = hvd.DistributedOptimizer(wide_optimizer)

    if args.amp:
        deep_optimizer = tf.keras.mixed_precision.LossScaleOptimizer(
            deep_optimizer, dynamic=True
        )
        wide_optimizer = tf.keras.mixed_precision.LossScaleOptimizer(
            wide_optimizer, dynamic=True
        )

    scheduler = LearningRateScheduler(
        args=args, steps_per_epoch=steps_per_epoch, optimizer=deep_optimizer
    )

    throughput_calculator = ThroughputCalculator(args)
    compiled_loss = tf.keras.losses.BinaryCrossentropy()

    evaluator = Evaluator(
        model=model,
        throughput_calculator=throughput_calculator,
        eval_dataset=eval_dataset,
        compiled_loss=compiled_loss,
        steps=steps,
        args=args,
    )

    trainer = Trainer(
        model=model,
        scheduler=scheduler,
        deep_optimizer=deep_optimizer,
        wide_optimizer=wide_optimizer,
        throughput_calculator=throughput_calculator,
        compiled_loss=compiled_loss,
        steps=steps,
        args=args,
        train_dataset=train_dataset,
        evaluator=evaluator,
    )

    trainer.maybe_restore_checkpoint()

    if args.evaluate:
        evaluator.eval(trainer.current_step_var)
    else:
        trainer.run_loop()