내결함성 메커니즘 마이그레이션하기

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내결함성은 매개변수 및 모델과 같은 추적 가능한 객체의 상태를 주기적으로 저장하는 메커니즘을 말합니다. 훈련하는 동안 프로그램/머신 오류가 발생한 경우 이를 사용하여 복구할 수 있습니다.

이 가이드에서는 먼저 tf.estimator.RunConfig를 사용하여 메트릭 저장 설정을 지정하고 TensorFlow 1에서 tf.estimator.Estimator를 사용하여 훈련에 내결함성을 추가하는 방법을 보여줍니다. 그런 다음 Tensorflow 2에서 훈련에 내결함성을 구현하는 방법 2가지를 배우게 됩니다.

이 두 가지 메서드 모두 체크포인트 파일의 훈련 상태를 백업하고 복원합니다.

설치하기

tf.keras.callbacks.BackupAndRestore의 save_freq 인수를 사용하여 특정 단계에서 체크포인트의 빈도를 저장하는 기능이 TensorFlow 2.10부터 도입되었으므로 tf-nightly를 설치합니다.

pip install tf-nightly

import tensorflow.compat.v1 as tf1
import tensorflow as tf
import numpy as np
import tempfile
import time

2022-12-14 20:52:01.602161: E tensorflow/tsl/lib/monitoring/collection_registry.cc:81] Cannot register 2 metrics with the same name: /tensorflow/core/bfc_allocator_delay

mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

TensorFlow 1: tf.estimator.RunConfig를 사용하여 체크포인트 저장하기

TensorFlow 1에서는 tf.estimator.RunConfig를 구성하여 모든 단계마다 체크포인트를 저장하도록 tf.estimator를 구성할 수 있습니다.

이 예제에서는 다섯 번째 체크포인트를 진행하는 동안 인위적으로 오류를 발생시키는 후크를 먼저 작성합니다.

class InterruptHook(tf1.train.SessionRunHook):
  # A hook for artificially interrupting training.
  def begin(self):
    self._step = -1

  def before_run(self, run_context):
    self._step += 1

  def after_run(self, run_context, run_values):
    if self._step == 5:
      raise RuntimeError('Interruption')

다음으로 모든 체크포인트를 저장하고 MNIST 데이터세트를 사용하도록 tf.estimator.Estimator를 구성합니다.

feature_columns = [tf1.feature_column.numeric_column("x", shape=[28, 28])]
config = tf1.estimator.RunConfig(save_summary_steps=1,
                                 save_checkpoints_steps=1)

path = tempfile.mkdtemp()

classifier = tf1.estimator.DNNClassifier(
    feature_columns=feature_columns,
    hidden_units=[256, 32],
    optimizer=tf1.train.AdamOptimizer(0.001),
    n_classes=10,
    dropout=0.2,
    model_dir=path,
    config = config
)

train_input_fn = tf1.estimator.inputs.numpy_input_fn(
    x={"x": x_train},
    y=y_train.astype(np.int32),
    num_epochs=10,
    batch_size=50,
    shuffle=True,
)

WARNING:tensorflow:From /tmpfs/tmp/ipykernel_63763/314197976.py:1: numeric_column (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version.
Instructions for updating:
Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model.
WARNING:tensorflow:From /tmpfs/tmp/ipykernel_63763/314197976.py:2: RunConfig.__init__ (from tensorflow_estimator.python.estimator.run_config) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/tmp/ipykernel_63763/314197976.py:7: DNNClassifier.__init__ (from tensorflow_estimator.python.estimator.canned.dnn) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/canned/dnn.py:807: Estimator.__init__ (from tensorflow_estimator.python.estimator.estimator) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpi091tdq3', '_tf_random_seed': None, '_save_summary_steps': 1, '_save_checkpoints_steps': 1, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
WARNING:tensorflow:From /tmpfs/tmp/ipykernel_63763/314197976.py:17: The name tf.estimator.inputs is deprecated. Please use tf.compat.v1.estimator.inputs instead.

WARNING:tensorflow:From /tmpfs/tmp/ipykernel_63763/314197976.py:17: numpy_input_fn (from tensorflow_estimator.python.estimator.inputs.numpy_io) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.

모델 훈련을 시작합니다. 앞에서 정의한 후크로 의해 인위적인 예외가 발생합니다.

try:
  classifier.train(input_fn=train_input_fn,
                   hooks=[InterruptHook()],
                   max_steps=10)
except Exception as e:
  print(f'{type(e).__name__}:{e}')

WARNING:tensorflow:From /tmpfs/tmp/ipykernel_63763/2587623597.py:3: object.__init__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:385: StopAtStepHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/training_util.py:396: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py:60: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py:491: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/canned/dnn.py:446: dnn_logit_fn_builder (from tensorflow_estimator.python.estimator.canned.dnn) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/model_fn.py:250: EstimatorSpec.__new__ (from tensorflow_estimator.python.estimator.model_fn) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Done calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1414: NanTensorHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1417: LoggingTensorHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/basic_session_run_hooks.py:232: SecondOrStepTimer.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1454: CheckpointSaverHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Create CheckpointSaverHook.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:579: StepCounterHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:586: SummarySaverHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:910: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
2022-12-14 20:52:08.500848: W tensorflow/core/common_runtime/type_inference.cc:339] Type inference failed. This indicates an invalid graph that escaped type checking. Error message: INVALID_ARGUMENT: expected compatible input types, but input 1:
type_id: TFT_OPTIONAL
args {
  type_id: TFT_PRODUCT
  args {
    type_id: TFT_TENSOR
    args {
      type_id: TFT_INT64
    }
  }
}
 is neither a subtype nor a supertype of the combined inputs preceding it:
type_id: TFT_OPTIONAL
args {
  type_id: TFT_PRODUCT
  args {
    type_id: TFT_TENSOR
    args {
      type_id: TFT_INT32
    }
  }
}

    while inferring type of node 'dnn/zero_fraction/cond/output/_18'
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpi091tdq3/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1455: SessionRunArgs.__new__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1454: SessionRunContext.__init__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1474: SessionRunValues.__new__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1...
INFO:tensorflow:Saving checkpoints for 1 into /tmpfs/tmp/tmpi091tdq3/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1...
INFO:tensorflow:loss = 116.79694, step = 0
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2...
INFO:tensorflow:Saving checkpoints for 2 into /tmpfs/tmp/tmpi091tdq3/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3...
INFO:tensorflow:Saving checkpoints for 3 into /tmpfs/tmp/tmpi091tdq3/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4...
INFO:tensorflow:Saving checkpoints for 4 into /tmpfs/tmp/tmpi091tdq3/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5...
INFO:tensorflow:Saving checkpoints for 5 into /tmpfs/tmp/tmpi091tdq3/model.ckpt.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/saver.py:1067: remove_checkpoint (from tensorflow.python.checkpoint.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to delete files with this prefix.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6...
INFO:tensorflow:Saving checkpoints for 6 into /tmpfs/tmp/tmpi091tdq3/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6...
RuntimeError:Interruption

마지막으로 저장한 체크포인트를 사용하여 tf.estimator.Estimator를 다시 빌드하고 훈련을 계속 진행합니다.

classifier = tf1.estimator.DNNClassifier(
    feature_columns=feature_columns,
    hidden_units=[256, 32],
    optimizer=tf1.train.AdamOptimizer(0.001),
    n_classes=10,
    dropout=0.2,
    model_dir=path,
    config = config
)
classifier.train(input_fn=train_input_fn,
                   max_steps = 10)

INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpi091tdq3', '_tf_random_seed': None, '_save_summary_steps': 1, '_save_checkpoints_steps': 1, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpi091tdq3/model.ckpt-6
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/saver.py:1176: get_checkpoint_mtimes (from tensorflow.python.checkpoint.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file utilities to get mtimes.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6...
INFO:tensorflow:Saving checkpoints for 6 into /tmpfs/tmp/tmpi091tdq3/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7...
INFO:tensorflow:Saving checkpoints for 7 into /tmpfs/tmp/tmpi091tdq3/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7...
INFO:tensorflow:loss = 100.25842, step = 6
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8...
INFO:tensorflow:Saving checkpoints for 8 into /tmpfs/tmp/tmpi091tdq3/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9...
INFO:tensorflow:Saving checkpoints for 9 into /tmpfs/tmp/tmpi091tdq3/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10...
INFO:tensorflow:Saving checkpoints for 10 into /tmpfs/tmp/tmpi091tdq3/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10...
INFO:tensorflow:Loss for final step: 96.2075.
<tensorflow_estimator.python.estimator.canned.dnn.DNNClassifier at 0x7f225bc54d30>

TensorFlow 2: 콜백 및 Model.fit으로 백업 및 복원하기

TensorFlow 2에서는 훈련에 Keras Model.fit API를 사용하는 경우 tf.keras.callbacks.BackupAndRestore 콜백을 제공하여 내결함성 기능을 추가할 수 있습니다.

이를 보여주기 위해 우선적으로 네 번째 epoch 체크포인트를 진행하는 동안 인위적으로 오류를 발생시키는 Keras Callback 클래스를 정의합니다.

class InterruptAtEpoch(tf.keras.callbacks.Callback):
  # A callback for artificially interrupting training.
  def __init__(self, interrupting_epoch=3):
    self.interrupting_epoch = interrupting_epoch

  def on_epoch_end(self, epoch, log=None):
    if epoch == self.interrupting_epoch:
      raise RuntimeError('Interruption')

그런 다음 간단한 Keras 모델을 정의 및 인스턴스화하고, 손실 함수를 정의하고, Model.compile을 호출하고, epoch 경계에서 임시 디렉터리에 체크포인트를 저장하는 tf.keras.callbacks.BackupAndRestore 콜백을 설정합니다.

def create_model():
  return tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10)
  ])
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model = create_model()
model.compile(optimizer='adam',
              loss=loss,
              metrics=['accuracy'])
log_dir = tempfile.mkdtemp()
backup_restore_callback = tf.keras.callbacks.BackupAndRestore(
    backup_dir = log_dir)

Model.fit을 사용하여 모델 훈련을 시작합니다. 훈련을 진행하는 동안 위에서 인스턴스화한 tf.keras.callbacks.BackupAndRestore 덕분에 체크포인트가 저장되지만 InterruptAtEpoch 클래스는 인위적으로 예외를 발생시켜 네 번째 epoch 이후에 실패를 시뮬레이션합니다.

try:
  model.fit(x=x_train,
            y=y_train,
            epochs=10,
            steps_per_epoch=100,
            validation_data=(x_test, y_test),
            callbacks=[backup_restore_callback, InterruptAtEpoch()])
except Exception as e:
  print(f'{type(e).__name__}:{e}')

Epoch 1/10
100/100 [==============================] - 2s 11ms/step - loss: 0.4660 - accuracy: 0.8693 - val_loss: 0.2196 - val_accuracy: 0.9391
Epoch 2/10
100/100 [==============================] - 1s 8ms/step - loss: 0.2022 - accuracy: 0.9430 - val_loss: 0.1582 - val_accuracy: 0.9549
Epoch 3/10
100/100 [==============================] - 1s 8ms/step - loss: 0.1475 - accuracy: 0.9580 - val_loss: 0.1253 - val_accuracy: 0.9629
Epoch 4/10
 90/100 [==========================>...] - ETA: 0s - loss: 0.1174 - accuracy: 0.9661RuntimeError:Interruption

그런 다음 Keras 모델을 인스턴스화하고 Model.compile을 호출한 다음 이전에 저장한 체크포인트의 Model.fit을 사용하여 모델을 계속 훈련합니다.

model = create_model()
model.compile(optimizer='adam',
              loss=loss,
              metrics=['accuracy'],
              steps_per_execution=10)
model.fit(x=x_train,
            y=y_train,
            epochs=10,
            steps_per_epoch=100,
            validation_data=(x_test, y_test),
            callbacks=[backup_restore_callback])

Epoch 5/10
100/100 [==============================] - 2s 19ms/step - loss: 0.0947 - accuracy: 0.9733 - val_loss: 0.0896 - val_accuracy: 0.9731
Epoch 6/10
100/100 [==============================] - 1s 5ms/step - loss: 0.0812 - accuracy: 0.9769 - val_loss: 0.0824 - val_accuracy: 0.9761
Epoch 7/10
100/100 [==============================] - 0s 5ms/step - loss: 0.0671 - accuracy: 0.9814 - val_loss: 0.0781 - val_accuracy: 0.9769
Epoch 8/10
100/100 [==============================] - 1s 5ms/step - loss: 0.0595 - accuracy: 0.9829 - val_loss: 0.0709 - val_accuracy: 0.9795
Epoch 9/10
100/100 [==============================] - 0s 5ms/step - loss: 0.0516 - accuracy: 0.9850 - val_loss: 0.0734 - val_accuracy: 0.9779
Epoch 10/10
100/100 [==============================] - 1s 5ms/step - loss: 0.0469 - accuracy: 0.9866 - val_loss: 0.0683 - val_accuracy: 0.9792
<keras.callbacks.History at 0x7f217c299e20>

140번째 단계에서 인위적으로 오류를 발생시키는 다른 Callback 클래스를 정의합니다.

class InterruptAtStep(tf.keras.callbacks.Callback):
  # A callback for artificially interrupting training.
  def __init__(self, interrupting_step=140):
    self.total_step_count = 0
    self.interrupting_step = interrupting_step

  def on_batch_begin(self, batch, logs=None):
    self.total_step_count += 1

  def on_batch_end(self, batch, logs=None):
    if self.total_step_count == self.interrupting_step:
      print("\nInterrupting at step count", self.total_step_count)
      raise RuntimeError('Interruption')

참고: 이 섹션에서는 Tensorflow 2.10이 릴리스될 때까지 tf-nightly에서만 사용할 수 있는 특성을 사용합니다.

체크포인트가 30단계마다 저장되도록 하려면 BackupAndRestore 콜백의 save_freq를 30으로 설정합니다. InterruptAtStep이 epoch 1 및 40단계(총 단계 수 140)에서 실패를 시뮬레이션하기 위해 인위적으로 예외를 발생시킵니다. 체크포인트는 epoch 1과 20단계에서 마지막으로 저장될 것입니다.

log_dir_2 = tempfile.mkdtemp()

backup_restore_callback = tf.keras.callbacks.BackupAndRestore(
    backup_dir = log_dir_2, save_freq=30
)
model = create_model()
model.compile(optimizer='adam',
              loss=loss,
              metrics=['accuracy'])
try:
  model.fit(x=x_train,
            y=y_train,
            epochs=10,
            steps_per_epoch=100,
            validation_data=(x_test, y_test),
            callbacks=[backup_restore_callback, InterruptAtStep()])
except Exception as e:
  print(f'{type(e).__name__}:{e}')

Epoch 1/10
100/100 [==============================] - 2s 11ms/step - loss: 0.4730 - accuracy: 0.8676 - val_loss: 0.2210 - val_accuracy: 0.9369
Epoch 2/10
 37/100 [==========>...................] - ETA: 0s - loss: 0.2252 - accuracy: 0.9364
Interrupting at step count 140
RuntimeError:Interruption

그런 다음 Keras 모델을 인스턴스화하고 Model.compile을 호출한 다음 이전에 저장한 체크포인트의 Model.fit을 사용하여 모델을 계속 훈련합니다. 훈련은 epoch 2와 21단계부터 시작합니다.

model = create_model()
model.compile(optimizer='adam',
              loss=loss,
              metrics=['accuracy'],
              steps_per_execution=10)
model.fit(x=x_train,
            y=y_train,
            epochs=10,
            steps_per_epoch=100,
            validation_data=(x_test, y_test),
            callbacks=[backup_restore_callback])

Epoch 2/10
100/100 [==============================] - 2s 18ms/step - loss: 0.1896 - accuracy: 0.9465 - val_loss: 0.1512 - val_accuracy: 0.9555
Epoch 3/10
100/100 [==============================] - 1s 5ms/step - loss: 0.1452 - accuracy: 0.9584 - val_loss: 0.1201 - val_accuracy: 0.9642
Epoch 4/10
100/100 [==============================] - 0s 5ms/step - loss: 0.1135 - accuracy: 0.9674 - val_loss: 0.0995 - val_accuracy: 0.9707
Epoch 5/10
100/100 [==============================] - 0s 5ms/step - loss: 0.0899 - accuracy: 0.9737 - val_loss: 0.0900 - val_accuracy: 0.9722
Epoch 6/10
100/100 [==============================] - 0s 5ms/step - loss: 0.0779 - accuracy: 0.9781 - val_loss: 0.0812 - val_accuracy: 0.9760
Epoch 7/10
100/100 [==============================] - 0s 5ms/step - loss: 0.0654 - accuracy: 0.9813 - val_loss: 0.0718 - val_accuracy: 0.9774
Epoch 8/10
100/100 [==============================] - 0s 5ms/step - loss: 0.0593 - accuracy: 0.9828 - val_loss: 0.0707 - val_accuracy: 0.9789
Epoch 9/10
100/100 [==============================] - 1s 5ms/step - loss: 0.0487 - accuracy: 0.9864 - val_loss: 0.0659 - val_accuracy: 0.9799
Epoch 10/10
100/100 [==============================] - 0s 5ms/step - loss: 0.0444 - accuracy: 0.9872 - val_loss: 0.0654 - val_accuracy: 0.9799
<keras.callbacks.History at 0x7f2230038430>

TensorFlow 2: 사용자 정의 훈련 루프를 사용하여 수동 체크포인트 작성하기

TensorFlow 2에서 사용자 정의 훈련 루프를 사용하는 경우 tf.train.Checkpoint 및 tf.train.CheckpointManager API로 내결함성 메커니즘을 구현할 수 있습니다.

이 예제는 다음을 수행하는 방법을 보여줍니다.

  • 저장하려는 추적 가능한 객체를 속성으로 설정한 체크포인트를 수동으로 생성하려면 tf.train.Checkpoint 객체를 사용합니다.
  • 여러 체크포인트를 관리하려면 tf.train.CheckpointManager를 사용합니다.

먼저 Keras 모델, 옵티마이저, 손실 함수를 정의하고 인스턴스화합니다. 그런 다음 추적 가능한 상태가 있는 두 객체(모델 및 옵티마이저)를 관리하는 Checkpoint와 임시 디렉터리에서 여러 체크포인트를 기록하고 유지하는 CheckpointManager를 생성합니다.

model = create_model()
optimizer = tf.keras.optimizers.SGD(learning_rate=0.001)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
log_dir = tempfile.mkdtemp()
epochs = 5
steps_per_epoch = 5

checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
checkpoint_manager = tf.train.CheckpointManager(
            checkpoint, log_dir, max_to_keep=2)

이제 새 epoch가 시작될 때마다 첫 번째 epoch 이후 마지막 체크포인트를 로드하는 사용자 정의 훈련 루프를 구현합니다.

for epoch in range(epochs):
  if epoch > 0:
      tf.train.load_checkpoint(save_path)
  print(f"\nStart of epoch {epoch}")

  for step in range(steps_per_epoch):
    with tf.GradientTape() as tape:

      logits = model(x_train, training=True)
      loss_value = loss_fn(y_train, logits)

      grads = tape.gradient(loss_value, model.trainable_weights)
      optimizer.apply_gradients(zip(grads, model.trainable_weights))

    save_path = checkpoint_manager.save()
    print(f"Checkpoint saved to {save_path}")
    print(f"Training loss at step {step}: {loss_value}")

Start of epoch 0
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-1
Training loss at step 0: 2.4224987030029297
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-2
Training loss at step 1: 2.422628402709961
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-3
Training loss at step 2: 2.4189400672912598
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-4
Training loss at step 3: 2.4165825843811035
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-5
Training loss at step 4: 2.4144229888916016

Start of epoch 1
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-6
Training loss at step 0: 2.4147567749023438
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-7
Training loss at step 1: 2.4123194217681885
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-8
Training loss at step 2: 2.410810708999634
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-9
Training loss at step 3: 2.4087791442871094
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-10
Training loss at step 4: 2.407498359680176

Start of epoch 2
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-11
Training loss at step 0: 2.4056396484375
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-12
Training loss at step 1: 2.4038097858428955
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-13
Training loss at step 2: 2.401495933532715
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-14
Training loss at step 3: 2.3997390270233154
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-15
Training loss at step 4: 2.397336959838867

Start of epoch 3
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-16
Training loss at step 0: 2.3974244594573975
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-17
Training loss at step 1: 2.394087076187134
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-18
Training loss at step 2: 2.393651008605957
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-19
Training loss at step 3: 2.3912947177886963
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-20
Training loss at step 4: 2.389580726623535

Start of epoch 4
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-21
Training loss at step 0: 2.388636350631714
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-22
Training loss at step 1: 2.386532783508301
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-23
Training loss at step 2: 2.3842995166778564
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-24
Training loss at step 3: 2.3836612701416016
Checkpoint saved to /tmpfs/tmp/tmpz95vb9ch/ckpt-25
Training loss at step 4: 2.3818771839141846

다음 단계

TensorFlow 2의 내결함성 및 체크포인트에 대해 자세히 알아보려면 다음 문서를 고려합니다.

분산 훈련과 관련된 다음 자료도 유용할 수 있습니다.