Оценки TensorFlow депрессированы — вот как (и почему) мигрировать

Обзор контента Setup Advantages Estimators Capabilities Using pre-made Estimators Write an input functions Define the feature columns. Instantiate the relevant pre-made Estimator. Call a training, evaluation, or inference method. Benefits of pre-made Estimators Custom Estimators Create an Estimator from a Keras model Saving object-based checkpoints with Estimator SavedModels from Estimators Using tf.distribute.Strategy with Estimator (Limited support) What’s supported now? Examples and tutorial Предупреждение: TensorFlow 2.15 включил окончательное издание пакета tf-estimator. Estimators не будут доступны в TensorFlow 2.16 или позже. TensorFlow 2.15 включает в себя окончательное издание Оценки не будут доступны в TensorFlow 2.16 или позже. для получения дополнительной информации о том, как отключить Estimators. Warning: tf-estimator Руководство по миграции Руководство по миграции Данный документ вводит — высокоуровневый API TensorFlow. Оценители включают в себя следующие действия: tf.estimator Тренировки Оценка Предсказание Экспорт для обслуживания TensorFlow реализует несколько предварительно созданных оценок. Настраиваемые оценок по-прежнему поддерживаются, но в основном как мера обратной совместимости. Все оценщики — предварительно сделанные или индивидуальные — представляют собой классы, основанные на класса . Custom estimators should not be used for new code tf.estimator.Estimator Как быстрый пример, попробуйте Для обзора дизайна API проверьте . Оценка Tutorials Белая книга Настройка pip install -U tensorflow_datasets import tempfile import os import tensorflow as tf import tensorflow_datasets as tfds 2024-01-24 02:20:44.732508: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-01-24 02:20:44.732557: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-01-24 02:20:44.734001: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered Преимущества Похожие на a , а Это абстракция на уровне модели. предоставляет некоторые возможности, которые в настоящее время находятся в разработке для Это и есть: tf.keras.Model estimator tf.estimator tf.keras Обучение на основе параметров сервера Полная интеграция TFX Способности оценителей Оценщики предоставляют следующие преимущества: Вы можете запускать модели на основе Estimator на локальном хосте или в распределенной среде с несколькими серверами, не меняя свою модель. Estimators provide a safe distributed training loop that controls how and when to: Load data Handle exceptions Create checkpoint files and recover from failures Save summaries for TensorBoard При написании приложения с помощью Estimators необходимо отделить трубопровод ввода данных от модели.Это отделение упрощает эксперименты с различными наборами данных. Использование предварительно сделанных оценок Предварительно сделанные оценщики позволяют вам работать на гораздо более высоком концептуальном уровне, чем базовые API TensorFlow. Вам больше не нужно беспокоиться о создании вычислительной графики или сеансов, так как оценщики справляются со всеми «проводами» за вас. , например, это предварительно созданный класс Estimator, который тренирует модели классификации на основе плотных, питательных нейронных сетей. tf.estimator.DNNClassifier Программа TensorFlow, опирающаяся на предварительно созданный Estimator, обычно состоит из следующих четырех шагов: 1 Напишите вводную функцию Например, вы можете создать одну функцию для импорта набора тренировок и другую функцию для импорта набора тестов. Словарь, в котором ключи являются именами функций, а значения — Tensors (или SparseTensors), содержащие соответствующие данные функций Тензор, содержащий одну или несколько этикеток ТЭ Следует вернуться к Это приводит к парам в этом формате. input_fn tf.data.Dataset Например, следующий код создает По данным Титаника Файлы : tf.data.Dataset train.csv def train_input_fn(): titanic_file = tf.keras.utils.get_file("train.csv", "https://storage.googleapis.com/tf-datasets/titanic/train.csv") titanic = tf.data.experimental.make_csv_dataset( titanic_file, batch_size=32, label_name="survived") titanic_batches = ( titanic.cache().repeat().shuffle(500) .prefetch(tf.data.AUTOTUNE)) return titanic_batches ТЭ Он был казнен в Кроме того, можно непосредственно вернуть а пары, содержащие графические тензоры, но это склонность к ошибке вне простых случаев, таких как возвращаемые константы. input_fn tf.Graph (features_dics, labels) Определите характеристики колонн. Каждый определяет название функции, ее тип и любую предварительную обработку ввода. tf.feature_column Например, следующий фрагмент создает три колонки функций. Первый использует функцию возраста непосредственно в качестве ввода плавающей точки. Второй использует характеристику класса в качестве категорического ввода. Третий использует embark_town в качестве категорического ввода, но использует хитрость хеширования, чтобы избежать необходимости перечислять опции и задавать количество опций. Для получения дополнительной информации, проверьте . Настройка колонн Tutorial age = tf.feature_column.numeric_column('age') cls = tf.feature_column.categorical_column_with_vocabulary_list('class', ['First', 'Second', 'Third']) embark = tf.feature_column.categorical_column_with_hash_bucket('embark_town', 32) WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/4100412726.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_9073/4100412726.py:2: categorical_column_with_vocabulary_list (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_9073/4100412726.py:3: categorical_column_with_hash_bucket (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. 3. Instantiate соответствующий предварительно сделанный Estimator. Например, вот примерная инстанция предварительно сделанного Estimator под названием : LinearClassifier model_dir = tempfile.mkdtemp() model = tf.estimator.LinearClassifier( model_dir=model_dir, feature_columns=[embark, cls, age], n_classes=2 ) WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/1904628810.py:2: LinearClassifierV2.__init__ (from tensorflow_estimator.python.estimator.canned.linear) 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/head/head_utils.py:54: BinaryClassHead.__init__ (from tensorflow_estimator.python.estimator.head.binary_class_head) 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/linear.py:944: 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. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1844: 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. INFO:tensorflow:Using default config. INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpbt9n791j', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_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} Для получения дополнительной информации вы можете обратиться к . Линейный классификатор Tutorial Назовите метод обучения, оценки или вывода. Все оценщики предоставляют , , и Методы train evaluate predict model = model.train(input_fn=train_input_fn, steps=100) 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. Downloading data from https://storage.googleapis.com/tf-datasets/titanic/train.csv 30874/30874 [==============================] - 0s 0us/step INFO:tensorflow:Calling model_fn. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/src/optimizers/legacy/ftrl.py:173: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. Instructions for updating: Call initializer instance with the dtype argument instead of passing it to the constructor 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:1416: 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:1419: 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:1456: 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. INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpbt9n791j/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:loss = 0.6931472, step = 0 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 100... INFO:tensorflow:Saving checkpoints for 100 into /tmpfs/tmp/tmpbt9n791j/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 100... INFO:tensorflow:Loss for final step: 0.59910536. 2024-01-24 02:20:52.016569: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead. result = model.evaluate(train_input_fn, steps=10) for key, value in result.items(): print(key, ":", value) INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2024-01-24T02:20:52 WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/evaluation.py:260: FinalOpsHook.__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:Restoring parameters from /tmpfs/tmp/tmpbt9n791j/model.ckpt-100 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Inference Time : 3.02373s INFO:tensorflow:Finished evaluation at 2024-01-24-02:20:55 INFO:tensorflow:Saving dict for global step 100: accuracy = 0.715625, accuracy_baseline = 0.615625, auc = 0.7548389, auc_precision_recall = 0.66771877, average_loss = 0.56671846, global_step = 100, label/mean = 0.384375, loss = 0.56671846, precision = 0.7, prediction/mean = 0.37144834, recall = 0.45528457 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 100: /tmpfs/tmp/tmpbt9n791j/model.ckpt-100 accuracy : 0.715625 accuracy_baseline : 0.615625 auc : 0.7548389 auc_precision_recall : 0.66771877 average_loss : 0.56671846 label/mean : 0.384375 loss : 0.56671846 precision : 0.7 prediction/mean : 0.37144834 recall : 0.45528457 global_step : 100 2024-01-24 02:20:55.868009: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead. for pred in model.predict(train_input_fn): for key, value in pred.items(): print(key, ":", value) break INFO:tensorflow:Calling model_fn. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/head/base_head.py:786: ClassificationOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) 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/head/binary_class_head.py:561: RegressionOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) 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/head/binary_class_head.py:563: PredictOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpbt9n791j/model.ckpt-100 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. logits : [-0.6829183] logistic : [0.33561027] probabilities : [0.6643897 0.33561027] class_ids : [0] classes : [b'0'] all_class_ids : [0 1] all_classes : [b'0' b'1'] 2024-01-24 02:20:56.898963: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead. Преимущества предварительно сделанных оценок Pre-made Estimators encode best practices, providing the following benefits: Лучшие практики для определения того, где должны работать различные части вычислительной графики, реализация стратегий на одной машине или на кластере. Лучшие практики для написания событий (резюме) и универсально полезных резюме. Если вы не используете предварительно созданные Estimators, вы должны сами реализовать предыдущие функции. Оценки Custom Сердце каждого оценщика — будь то предварительно сделанный или обычный — является его , , который является методом, который создает графики для обучения, оценки и прогнозирования. Когда вы используете предварительно созданный оценитель, кто-то еще уже реализовал функцию модели. Функциональная модель model_fn Примечание: Настраиваемая модель_fn по-прежнему будет работать в графическом режиме в стиле 1.x. Это означает, что нет желательной реализации и нет автоматических зависимостей управления. Вы должны планировать миграцию от tf.estimator с помощью настраиваемой модели_fn. Альтернативные API — tf.keras и tf.distribute. Если вам все еще нужен Estimator для какой-то части вашего обучения, вы можете использовать конвертер tf.keras.estimator.model_to_estimator для создания Estimator из keras.Model. Примечание: Настраиваемая модель_fn по-прежнему будет работать в графическом режиме в стиле 1.x. Это означает, что нет желательной реализации и нет автоматических зависимостей управления. Вы должны планировать миграцию от tf.estimator с помощью настраиваемой модели_fn. Альтернативные API — tf.keras и tf.distribute. Если вам все еще нужен Estimator для какой-то части вашего обучения, вы можете использовать конвертер tf.keras.estimator.model_to_estimator для создания Estimator из keras.Model. Создание Estimator из модели Keras Вы можете конвертировать существующие модели Keras в Estimators с помощью Это полезно, если вы хотите модернизировать свой код модели, но ваш учебный трубопровод все равно требует Estimators. tf.keras.estimator.model_to_estimator Инстинктируйте модель Keras MobileNet V2 и компилируйте модель с оптимизатором, потерей и метриками, чтобы тренироваться с: keras_mobilenet_v2 = tf.keras.applications.MobileNetV2( input_shape=(160, 160, 3), include_top=False) keras_mobilenet_v2.trainable = False estimator_model = tf.keras.Sequential([ keras_mobilenet_v2, tf.keras.layers.GlobalAveragePooling2D(), tf.keras.layers.Dense(1) ]) # Compile the model estimator_model.compile( optimizer='adam', loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics=['accuracy']) Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_160_no_top.h5 9406464/9406464 [==============================] - 0s 0us/step Создать один из составленной модели Кераса. Первоначальное состояние модели Кераса сохраняется в созданной : Estimator Estimator est_mobilenet_v2 = tf.keras.estimator.model_to_estimator(keras_model=estimator_model) INFO:tensorflow:Using default config. WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmpjcucleiz INFO:tensorflow:Using the Keras model provided. WARNING:absl:You are using `tf.keras.optimizers.experimental.Optimizer` in TF estimator, which only supports `tf.keras.optimizers.legacy.Optimizer`. Automatically converting your optimizer to `tf.keras.optimizers.legacy.Optimizer`. /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/src/backend.py:452: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model. warnings.warn( 2024-01-24 02:21:01.520653: W tensorflow/c/c_api.cc:305] Operation '{name:'block_16_project_BN/gamma/Assign' id:2164 op device:{requested: '', assigned: ''} def:{ { {node block_16_project_BN/gamma/Assign} } = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](block_16_project_BN/gamma, block_16_project_BN/gamma/Initializer/ones)} }' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session. INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpjcucleiz', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_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:Using config: {'_model_dir': '/tmpfs/tmp/tmpjcucleiz', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_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/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:2404: WarmStartSettings.__new__ (from tensorflow_estimator.python.estimator.estimator) 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:2404: WarmStartSettings.__new__ (from tensorflow_estimator.python.estimator.estimator) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. Обращайте внимание на производные Как бы ты с любым другим . Estimator Estimator IMG_SIZE = 160 # All images will be resized to 160x160 def preprocess(image, label): image = tf.cast(image, tf.float32) image = (image/127.5) - 1 image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE)) return image, label def train_input_fn(batch_size): data = tfds.load('cats_vs_dogs', as_supervised=True) train_data = data['train'] train_data = train_data.map(preprocess).shuffle(500).batch(batch_size) return train_data Для поездов выберите функцию поезда Estimator: est_mobilenet_v2.train(input_fn=lambda: train_input_fn(32), steps=50) INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmpfs/tmp/tmpjcucleiz/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={}) INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmpfs/tmp/tmpjcucleiz/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={}) INFO:tensorflow:Warm-starting from: /tmpfs/tmp/tmpjcucleiz/keras/keras_model.ckpt INFO:tensorflow:Warm-starting from: /tmpfs/tmp/tmpjcucleiz/keras/keras_model.ckpt INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES. INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES. INFO:tensorflow:Warm-started 158 variables. INFO:tensorflow:Warm-started 158 variables. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpjcucleiz/model.ckpt. INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpjcucleiz/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:loss = 0.6796611, step = 0 INFO:tensorflow:loss = 0.6796611, step = 0 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50... INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50... INFO:tensorflow:Saving checkpoints for 50 into /tmpfs/tmp/tmpjcucleiz/model.ckpt. INFO:tensorflow:Saving checkpoints for 50 into /tmpfs/tmp/tmpjcucleiz/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50... INFO:tensorflow:Loss for final step: 0.69532585. INFO:tensorflow:Loss for final step: 0.69532585. Аналогичным образом, чтобы оценить, назовите функцию оценки Estimator: est_mobilenet_v2.evaluate(input_fn=lambda: train_input_fn(32), steps=10) INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/src/engine/training_v1.py:2335: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically. updates = self.state_updates INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2024-01-24T02:21:19 INFO:tensorflow:Starting evaluation at 2024-01-24T02:21:19 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpjcucleiz/model.ckpt-50 INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpjcucleiz/model.ckpt-50 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Inference Time : 2.81526s INFO:tensorflow:Inference Time : 2.81526s INFO:tensorflow:Finished evaluation at 2024-01-24-02:21:22 INFO:tensorflow:Finished evaluation at 2024-01-24-02:21:22 INFO:tensorflow:Saving dict for global step 50: accuracy = 0.54375, global_step = 50, loss = 0.6558426 INFO:tensorflow:Saving dict for global step 50: accuracy = 0.54375, global_step = 50, loss = 0.6558426 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 50: /tmpfs/tmp/tmpjcucleiz/model.ckpt-50 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 50: /tmpfs/tmp/tmpjcucleiz/model.ckpt-50 {'accuracy': 0.54375, 'loss': 0.6558426, 'global_step': 50} Для более подробной информации, пожалуйста, ознакомьтесь с документацией для . tf.keras.estimator.model_to_estimator Сохранение контрольных пунктов на основе объекта с помощью Estimator По умолчанию оценщики сохраняют контрольные точки с переменными именами, а не с графиком объекта, описанным в . . будет читать контрольные точки, основанные на названиях, но имена переменных могут меняться при перемещении частей модели за пределы Для предварительной совместимости сохранение контрольных точек на основе объекта облегчает обучение модели внутри Estimator, а затем использование ее вне. Руководство Checkpoint tf.train.Checkpoint model_fn import tensorflow.compat.v1 as tf_compat def toy_dataset(): inputs = tf.range(10.)[:, None] labels = inputs * 5. + tf.range(5.)[None, :] return tf.data.Dataset.from_tensor_slices( dict(x=inputs, y=labels)).repeat().batch(2) class Net(tf.keras.Model): """A simple linear model.""" def __init__(self): super(Net, self).__init__() self.l1 = tf.keras.layers.Dense(5) def call(self, x): return self.l1(x) def model_fn(features, labels, mode): net = Net() opt = tf.keras.optimizers.Adam(0.1) ckpt = tf.train.Checkpoint(step=tf_compat.train.get_global_step(), optimizer=opt, net=net) with tf.GradientTape() as tape: output = net(features['x']) loss = tf.reduce_mean(tf.abs(output - features['y'])) variables = net.trainable_variables gradients = tape.gradient(loss, variables) return tf.estimator.EstimatorSpec( mode, loss=loss, train_op=tf.group(opt.apply_gradients(zip(gradients, variables)), ckpt.step.assign_add(1)), # Tell the Estimator to save "ckpt" in an object-based format. scaffold=tf_compat.train.Scaffold(saver=ckpt)) tf.keras.backend.clear_session() est = tf.estimator.Estimator(model_fn, './tf_estimator_example/') est.train(toy_dataset, steps=10) INFO:tensorflow:Using default config. INFO:tensorflow:Using default config. INFO:tensorflow:Using config: {'_model_dir': './tf_estimator_example/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_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:Using config: {'_model_dir': './tf_estimator_example/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_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:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... INFO:tensorflow:Saving checkpoints for 0 into ./tf_estimator_example/model.ckpt. INFO:tensorflow:Saving checkpoints for 0 into ./tf_estimator_example/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:loss = 4.4765882, step = 1 INFO:tensorflow:loss = 4.4765882, step = 1 WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 4 vs previous value: 4. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize. WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 4 vs previous value: 4. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize. INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10... INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10... INFO:tensorflow:Saving checkpoints for 10 into ./tf_estimator_example/model.ckpt. INFO:tensorflow:Saving checkpoints for 10 into ./tf_estimator_example/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10... INFO:tensorflow:Loss for final step: 44.10201. INFO:tensorflow:Loss for final step: 44.10201. Затем можно загрузить контрольные пункты Estimator с его . tf.train.Checkpoint model_dir opt = tf.keras.optimizers.Adam(0.1) net = Net() ckpt = tf.train.Checkpoint( step=tf.Variable(1, dtype=tf.int64), optimizer=opt, net=net) ckpt.restore(tf.train.latest_checkpoint('./tf_estimator_example/')) ckpt.step.numpy() # From est.train(..., steps=10) 10 Сберегательные модели от оценителей Оценщики экспортируют SavedModels через . tf.Estimator.export_saved_model input_column = tf.feature_column.numeric_column("x") estimator = tf.estimator.LinearClassifier(feature_columns=[input_column]) def input_fn(): return tf.data.Dataset.from_tensor_slices( ({"x": [1., 2., 3., 4.]}, [1, 1, 0, 0])).repeat(200).shuffle(64).batch(16) estimator.train(input_fn) INFO:tensorflow:Using default config. INFO:tensorflow:Using default config. WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmps_vspwiv WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmps_vspwiv INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmps_vspwiv', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_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:Using config: {'_model_dir': '/tmpfs/tmp/tmps_vspwiv', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_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:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmps_vspwiv/model.ckpt. INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmps_vspwiv/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:loss = 0.6931472, step = 0 INFO:tensorflow:loss = 0.6931472, step = 0 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50... INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50... INFO:tensorflow:Saving checkpoints for 50 into /tmpfs/tmp/tmps_vspwiv/model.ckpt. INFO:tensorflow:Saving checkpoints for 50 into /tmpfs/tmp/tmps_vspwiv/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50... INFO:tensorflow:Loss for final step: 0.42848104. INFO:tensorflow:Loss for final step: 0.42848104. Для спасения одного Вам необходимо создать а Эта функция формирует часть а Это анализирует сырые данные, полученные SavedModel. Estimator serving_input_receiver tf.Graph ТЭ модуль содержит функции, которые помогают создавать эти . tf.estimator.export receivers Следующий код создает приемник, основанный на , что принимает сериализированный протоколы буфера, которые часто используются с . feature_columns tf.Example TF-сервисы tmpdir = tempfile.mkdtemp() serving_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn( tf.feature_column.make_parse_example_spec([input_column])) estimator_base_path = os.path.join(tmpdir, 'from_estimator') estimator_path = estimator.export_saved_model(estimator_base_path, serving_input_fn) WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/121230551.py:4: make_parse_example_spec_v2 (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_9073/121230551.py:4: make_parse_example_spec_v2 (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_9073/121230551.py:3: build_parsing_serving_input_receiver_fn (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/121230551.py:3: build_parsing_serving_input_receiver_fn (from tensorflow_estimator.python.estimator.export.export) 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/export/export.py:312: ServingInputReceiver.__new__ (from tensorflow_estimator.python.estimator.export.export) 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/export/export.py:312: ServingInputReceiver.__new__ (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:168: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:168: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:83: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:83: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2. INFO:tensorflow:Signatures INCLUDED in export for Classify: ['serving_default', 'classification'] INFO:tensorflow:Signatures INCLUDED in export for Classify: ['serving_default', 'classification'] INFO:tensorflow:Signatures INCLUDED in export for Regress: ['regression'] INFO:tensorflow:Signatures INCLUDED in export for Regress: ['regression'] INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict'] INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict'] INFO:tensorflow:Signatures INCLUDED in export for Train: None INFO:tensorflow:Signatures INCLUDED in export for Train: None INFO:tensorflow:Signatures INCLUDED in export for Eval: None INFO:tensorflow:Signatures INCLUDED in export for Eval: None INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmps_vspwiv/model.ckpt-50 INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmps_vspwiv/model.ckpt-50 INFO:tensorflow:Assets added to graph. INFO:tensorflow:Assets added to graph. INFO:tensorflow:No assets to write. INFO:tensorflow:No assets to write. INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tmpvsrxtilj/from_estimator/temp-1706062885/saved_model.pb INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tmpvsrxtilj/from_estimator/temp-1706062885/saved_model.pb Вы также можете загрузить и запустить эту модель из Python: imported = tf.saved_model.load(estimator_path) def predict(x): example = tf.train.Example() example.features.feature["x"].float_list.value.extend([x]) return imported.signatures["predict"]( examples=tf.constant([example.SerializeToString()])) print(predict(1.5)) print(predict(3.5)) {'logistic': , 'probabilities': , 'class_ids': , 'logits': , 'classes': , 'all_class_ids': , 'all_classes': } {'logistic': , 'probabilities': , 'class_ids': , 'logits': , 'classes': , 'all_class_ids': , 'all_classes': } позволяет создавать входные функции, которые принимают сырые тензоры вместо с с . tf.estimator.export.build_raw_serving_input_receiver_fn tf.train.Example Использование с Estimator (ограниченная поддержка) tf.distribute.Стратегия tf.distribute.Стратегия это распределенный учебный API TensorFlow, который первоначально поддерживал подход сервера параметров async. Сейчас поддерживает Если вы используете , вы можете перейти на распределенное обучение с очень немногими изменениями в вашем коде. С этим пользователи Estimator теперь могут проводить синхронное распределенное обучение на нескольких GPU и нескольких рабочих, а также использовать TPU. Эта поддержка в Estimator, однако, ограничена. Раздел ниже для более подробной информации. tf.estimator tf.estimator tf.distribute.Strategy tf.estimator Что сейчас поддерживается Использование с Estimator немного отличается от случая Keras. , теперь вы передаете объект стратегии в Для того чтобы оценить. tf.distribute.Strategy strategy.scope RunConfig Вы можете ссылаться на Для большей информации. Распределенное руководство по обучению Вот фрагмент кода, который показывает это с помощью предварительного Estimator и : LinearRegressor MirroredStrategy mirrored_strategy = tf.distribute.MirroredStrategy() config = tf.estimator.RunConfig( train_distribute=mirrored_strategy, eval_distribute=mirrored_strategy) regressor = tf.estimator.LinearRegressor( feature_columns=[tf.feature_column.numeric_column('feats')], optimizer='SGD', config=config) INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1', '/job:localhost/replica:0/task:0/device:GPU:2', '/job:localhost/replica:0/task:0/device:GPU:3') INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1', '/job:localhost/replica:0/task:0/device:GPU:2', '/job:localhost/replica:0/task:0/device:GPU:3') INFO:tensorflow:Initializing RunConfig with distribution strategies. INFO:tensorflow:Initializing RunConfig with distribution strategies. INFO:tensorflow:Not using Distribute Coordinator. INFO:tensorflow:Not using Distribute Coordinator. WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/4277059788.py:4: LinearRegressorV2.__init__ (from tensorflow_estimator.python.estimator.canned.linear) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/4277059788.py:4: LinearRegressorV2.__init__ (from tensorflow_estimator.python.estimator.canned.linear) 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/linear.py:1344: RegressionHead.__init__ (from tensorflow_estimator.python.estimator.head.regression_head) 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/linear.py:1344: RegressionHead.__init__ (from tensorflow_estimator.python.estimator.head.regression_head) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmpjagdxixr WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmpjagdxixr INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpjagdxixr', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_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': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_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, '_distribute_coordinator_mode': None} INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpjagdxixr', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_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': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_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, '_distribute_coordinator_mode': None} Здесь вы используете предварительный Estimator, но тот же код работает и с настраиваемым Estimator. определяет, как будет распределено обучение, и Это еще одно отличие от Keras, где вы используете одну и ту же стратегию как для обучения, так и для eval. train_distribute eval_distribute Теперь вы можете обучить и оценить этот Estimator с помощью вводной функции: def input_fn(): dataset = tf.data.Dataset.from_tensors(({"feats":[1.]}, [1.])) return dataset.repeat(1000).batch(10) regressor.train(input_fn=input_fn, steps=10) regressor.evaluate(input_fn=input_fn, steps=10) WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1246: StrategyBase.configure (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version. Instructions for updating: use `update_config_proto` instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1246: StrategyBase.configure (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version. Instructions for updating: use `update_config_proto` instead. /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/data/ops/dataset_ops.py:462: UserWarning: To make it possible to preserve tf.data options across serialization boundaries, their implementation has moved to be part of the TensorFlow graph. As a consequence, the options value is in general no longer known at graph construction time. Invoking this method in graph mode retains the legacy behavior of the original implementation, but note that the returned value might not reflect the actual value of the options. warnings.warn("To make it possible to preserve tf.data options across " INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Collective all_reduce tensors: 2 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1 INFO:tensorflow:Collective all_reduce tensors: 2 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1 INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Create CheckpointSaverHook. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/util.py:95: DistributedIteratorV1.initialize (from tensorflow.python.distribute.v1.input_lib) is deprecated and will be removed in a future version. Instructions for updating: Use the iterator's `initializer` property instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/util.py:95: DistributedIteratorV1.initialize (from tensorflow.python.distribute.v1.input_lib) is deprecated and will be removed in a future version. Instructions for updating: Use the iterator's `initializer` property instead. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpjagdxixr/model.ckpt. INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpjagdxixr/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... 2024-01-24 02:21:31.119820: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:31.121097: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' 2024-01-24 02:21:31.125859: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:31.126357: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' 2024-01-24 02:21:31.137091: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:31.137578: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' 2024-01-24 02:21:31.143860: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:31.144344: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' INFO:tensorflow:loss = 4.0, step = 0 INFO:tensorflow:loss = 4.0, step = 0 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10... INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10... INFO:tensorflow:Saving checkpoints for 10 into /tmpfs/tmp/tmpjagdxixr/model.ckpt. INFO:tensorflow:Saving checkpoints for 10 into /tmpfs/tmp/tmpjagdxixr/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10... INFO:tensorflow:Loss for final step: 1.1510792e-12. INFO:tensorflow:Loss for final step: 1.1510792e-12. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2024-01-24T02:21:35 INFO:tensorflow:Starting evaluation at 2024-01-24T02:21:35 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpjagdxixr/model.ckpt-10 INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpjagdxixr/model.ckpt-10 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Done running local_init_op. 2024-01-24 02:21:36.170934: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:36.172154: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' 2024-01-24 02:21:36.176484: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:36.176957: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' 2024-01-24 02:21:36.182511: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:36.182980: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' 2024-01-24 02:21:36.196737: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:36.197227: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Inference Time : 1.83647s INFO:tensorflow:Inference Time : 1.83647s INFO:tensorflow:Finished evaluation at 2024-01-24-02:21:37 INFO:tensorflow:Finished evaluation at 2024-01-24-02:21:37 INFO:tensorflow:Saving dict for global step 10: average_loss = 1.4210855e-14, global_step = 10, label/mean = 1.0, loss = 1.4210855e-14, prediction/mean = 0.99999994 INFO:tensorflow:Saving dict for global step 10: average_loss = 1.4210855e-14, global_step = 10, label/mean = 1.0, loss = 1.4210855e-14, prediction/mean = 0.99999994 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10: /tmpfs/tmp/tmpjagdxixr/model.ckpt-10 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10: /tmpfs/tmp/tmpjagdxixr/model.ckpt-10 {'average_loss': 1.4210855e-14, 'label/mean': 1.0, 'loss': 1.4210855e-14, 'prediction/mean': 0.99999994, 'global_step': 10} Другая разница, которую следует подчеркнуть здесь между Estimator и Keras, заключается в обработке входов. В Keras каждая партия набора данных автоматически разделяется на несколько реплик. Однако в Estimator вы не выполняете автоматическое разделение партий, а также автоматически не разделяете данные между различными работниками. У вас есть полный контроль над тем, как вы хотите, чтобы ваши данные распределялись между работниками и устройствами, и вы должны предоставить Определить, как распределять ваши данные. input_fn Твой называется один раз на работника, таким образом, давая один набор данных на работника. Затем одна партия из этого набора данных подается одной реплике на этого работника, тем самым потребляя N партий для N реплик на 1 работника. Должны быть упаковки размером И глобальный размер партии на один шаг можно получить как . input_fn input_fn PER_REPLICA_BATCH_SIZE PER_REPLICA_BATCH_SIZE * strategy.num_replicas_in_sync При выполнении многотрудового обучения вы должны либо разделить свои данные между работниками, либо пошевелиться случайным семенем на каждом из них. Это Tutorial. Многопрофильное обучение с Estimator Точно так же вы можете использовать стратегии мультирабочих и параметров-серверов.Код остается прежним, но вам нужно использовать и сет переменные окружающей среды для каждого бегущего бинарного в вашем кластере. tf.estimator.train_and_evaluate TF_CONFIG Что сейчас поддерживается? Есть ограниченная поддержка для обучения с Estimator, используя все стратегии, кроме Базовая подготовка и оценка должны работать, но ряд передовых функций, таких как Также может быть ряд ошибок в этой интеграции и нет планов активно улучшать эту поддержку (фокус делается на поддержку Keras и персонализированной тренировочной цепи). Вместо этого используйте эти АПИ. TPUStrategy v1.train.Scaffold tf.distribute Training API MirroredStrategy TPUStrategy MultiWorkerMirroredStrategy CentralStorageStrategy ParameterServerStrategy Estimator API Limited support Not supported Limited support Limited support Limited support Оценка API ограниченная поддержка Не поддерживается ограниченная поддержка ограниченная поддержка ограниченная поддержка Примеры и tutorials Вот некоторые примеры, которые показывают, как использовать различные стратегии с Estimator: Учебное пособие Multi-worker Training with Estimator показывает, как можно тренироваться с несколькими работниками, используя MultiWorkerMirroredStrategy на наборе данных MNIST. Конечный пример выполнения многорабочего обучения с стратегиями распределения в tensorflow/экосистеме с использованием шаблонов Kubernetes. Он начинается с модели Keras и преобразует ее в Estimator с помощью API tf.keras.estimator.model_to_estimator. Официальная модель ResNet50, которую можно тренировать с помощью MirroredStrategy или MultiWorkerMirroredStrategy. Первоначально опубликованная на веб-сайте TensorFlow, эта статья появляется здесь под новым заголовком и лицензирована под CC BY 4.0. Первоначально опубликованная на веб-сайте TensorFlow, эта статья появляется здесь под новым заголовком и лицензирована под CC BY 4.0. TensorFlow TensorFlow