從 TPUEstimator 遷移至 TPUStrategy

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本指南示範如何將在 TPU 上執行的工作流程從 TensorFlow 1 的 TPUEstimator API 遷移至 TensorFlow 2 的 TPUStrategy API。

  • 在 TensorFlow 1 中，您可以使用 tf.compat.v1.estimator.tpu.TPUEstimator API 在 (雲端) TPU 上訓練及評估模型，以及執行推論並儲存模型 (以供服務使用)。
  • 在 TensorFlow 2 中，若要在 TPU 和 TPU Pod (透過專用高速網路介面連線的 TPU 裝置集合) 上執行同步訓練，您需要使用 TPU 分散式策略，也就是 tf.distribute.TPUStrategy。此策略可搭配 Keras API 使用，包括模型建構 (tf.keras.Model)、最佳化工具 (tf.keras.optimizers.Optimizer) 和訓練 (Model.fit)，以及自訂訓練迴圈 (搭配 tf.function 和 tf.GradientTape)。

如需端對端 TensorFlow 2 範例，請查看使用 TPU 指南 (特別是「TPU 分類」一節) 和使用 TPU 上的 BERT 解決 GLUE 任務教學課程。您可能也會覺得分散式訓練指南很有用，其中涵蓋所有 TensorFlow 分散式策略，包括 TPUStrategy。

設定

從匯入項目和用於示範用途的簡單資料集開始

import tensorflow as tf
import tensorflow.compat.v1 as tf1

2023-06-09 12:22:02.963028: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

features = [[1., 1.5]]
labels = [[0.3]]
eval_features = [[4., 4.5]]
eval_labels = [[0.8]]

TensorFlow 1：使用 TPUEstimator 在 TPU 上驅動模型

本節指南示範如何在 TensorFlow 1 中使用 tf.compat.v1.estimator.tpu.TPUEstimator 執行訓練和評估。

若要使用 TPUEstimator，請先定義幾個函式：用於訓練資料的輸入函式、用於評估資料的評估輸入函式，以及模型函式 (告知 TPUEstimator 如何使用特徵和標籤定義訓練作業)

def _input_fn(params):
  dataset = tf1.data.Dataset.from_tensor_slices((features, labels))
  dataset = dataset.repeat()
  return dataset.batch(params['batch_size'], drop_remainder=True)

def _eval_input_fn(params):
  dataset = tf1.data.Dataset.from_tensor_slices((eval_features, eval_labels))
  dataset = dataset.repeat()
  return dataset.batch(params['batch_size'], drop_remainder=True)

def _model_fn(features, labels, mode, params):
  logits = tf1.layers.Dense(1)(features)
  loss = tf1.losses.mean_squared_error(labels=labels, predictions=logits)
  optimizer = tf1.train.AdagradOptimizer(0.05)
  train_op = optimizer.minimize(loss, global_step=tf1.train.get_global_step())
  return tf1.estimator.tpu.TPUEstimatorSpec(mode, loss=loss, train_op=train_op)

定義這些函式後，請建立 tf.distribute.cluster_resolver.TPUClusterResolver (提供叢集資訊) 和 tf.compat.v1.estimator.tpu.RunConfig 物件。搭配您已定義的模型函式，現在您可以建立 TPUEstimator。在此範例中，您將省略檢查點儲存以簡化流程。然後，您將為 TPUEstimator 指定訓練和評估的批次大小。

cluster_resolver = tf1.distribute.cluster_resolver.TPUClusterResolver(tpu='')
print("All devices: ", tf1.config.list_logical_devices('TPU'))

All devices:  []
2023-06-09 12:22:04.200684: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected

tpu_config = tf1.estimator.tpu.TPUConfig(iterations_per_loop=10)
config = tf1.estimator.tpu.RunConfig(
    cluster=cluster_resolver,
    save_checkpoints_steps=None,
    tpu_config=tpu_config)
estimator = tf1.estimator.tpu.TPUEstimator(
    model_fn=_model_fn,
    config=config,
    train_batch_size=8,
    eval_batch_size=8)

WARNING:tensorflow:From /tmpfs/tmp/ipykernel_15327/4277674569.py:1: The name tf.estimator.tpu.TPUConfig is deprecated. Please use tf.compat.v1.estimator.tpu.TPUConfig instead.

WARNING:tensorflow:From /tmpfs/tmp/ipykernel_15327/4277674569.py:1: TPUConfig.__new__ (from tensorflow_estimator.python.estimator.tpu.tpu_config) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:From /tmpfs/tmp/ipykernel_15327/4277674569.py:2: RunConfig.__init__ (from tensorflow_estimator.python.estimator.tpu.tpu_config) 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/tpu/tpu_config.py:268: 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_15327/4277674569.py:6: TPUEstimator.__init__ (from tensorflow_estimator.python.estimator.tpu.tpu_estimator) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
WARNING:tensorflow:Estimator's model_fn (<function _model_fn at 0x7f66f9c41670>) includes params argument, but params are not passed to Estimator.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py:2811: 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:Using temporary folder as model directory: /tmpfs/tmp/tmpfi5eso9h
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpfi5eso9h', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
cluster_def {
  job {
    name: "worker"
    tasks {
      key: 0
      value: "10.25.167.66:8470"
    }
  }
}
isolate_session_state: true
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_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({'worker': ['10.25.167.66:8470']}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': 'grpc://10.25.167.66:8470', '_evaluation_master': 'grpc://10.25.167.66:8470', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_tpu_config': TPUConfig(iterations_per_loop=10, num_shards=None, num_cores_per_replica=None, per_host_input_for_training=2, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1, experimental_allow_per_host_v2_parallel_get_next=False, experimental_feed_hook=None), '_cluster': <tensorflow.python.distribute.cluster_resolver.tpu.tpu_cluster_resolver.TPUClusterResolver object at 0x7f66f9c49e50>}
INFO:tensorflow:_TPUContext: eval_on_tpu True

呼叫 TPUEstimator.train 以開始訓練模型

estimator.train(_input_fn, steps=1)

INFO:tensorflow:Querying Tensorflow master (grpc://10.25.167.66:8470) for TPU system metadata.
INFO:tensorflow:Found TPU system:
INFO:tensorflow:*** Num TPU Cores: 8
INFO:tensorflow:*** Num TPU Workers: 1
INFO:tensorflow:*** Num TPU Cores Per Worker: 8
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, -1, 1291425829812295795)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 17179869184, -3325997117977499465)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 17179869184, 8477538334583946656)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 17179869184, 7772414489121114884)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 17179869184, 3690613627338882953)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 17179869184, 7506155478907565168)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 17179869184, 2236267778124756088)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 17179869184, -3746359292134531998)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 17179869184, -7944284987887513631)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 17179869184, -5726915818709471474)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, -2156150057367007347)
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.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py:2371: 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/basic_session_run_hooks.py:686: 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/python/training/adagrad.py:138: 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/tmp/ipykernel_15327/3404938034.py:16: TPUEstimatorSpec.__new__ (from tensorflow_estimator.python.estimator.tpu.tpu_estimator) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.keras instead.
2023-06-09 12:22:04.585870: W tensorflow/core/distributed_runtime/rpc/grpc_session.cc:374] GrpcSession::ListDevices will initialize the session with an empty graph and other defaults because the session has not yet been created.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py:3328: 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.
INFO:tensorflow:Bypassing TPUEstimator hook
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py:3369: 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/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:TPU job name worker
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_estimator/python/estimator/tpu/tpu_estimator.py:760: Variable.load (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Prefer Variable.assign which has equivalent behavior in 2.X.
INFO:tensorflow:Initialized dataset iterators in 0 seconds
INFO:tensorflow:Installing graceful shutdown hook.
INFO:tensorflow:Creating heartbeat manager for ['/job:worker/replica:0/task:0/device:CPU:0']
INFO:tensorflow:Configuring worker heartbeat: shutdown_mode: WAIT_FOR_COORDINATOR

INFO:tensorflow:Init TPU system
2023-06-09 12:22:04.975589: W tensorflow/core/distributed_runtime/rpc/grpc_session.cc:374] GrpcSession::ListDevices will initialize the session with an empty graph and other defaults because the session has not yet been created.
INFO:tensorflow:Initialized TPU in 4 seconds
INFO:tensorflow:Starting infeed thread controller.
INFO:tensorflow:Starting outfeed thread controller.
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.
INFO:tensorflow:Enqueue next (1) batch(es) of data to infeed.
INFO:tensorflow:Dequeue next (1) batch(es) of data from outfeed.
INFO:tensorflow:Outfeed finished for iteration (0, 0)
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 = 2.3149996, step = 1
INFO:tensorflow:Stop infeed thread controller
INFO:tensorflow:Shutting down InfeedController thread.
INFO:tensorflow:InfeedController received shutdown signal, stopping.
INFO:tensorflow:Infeed thread finished, shutting down.
INFO:tensorflow:infeed marked as finished
INFO:tensorflow:Stop output thread controller
INFO:tensorflow:Shutting down OutfeedController thread.
INFO:tensorflow:OutfeedController received shutdown signal, stopping.
INFO:tensorflow:Outfeed thread finished, shutting down.
INFO:tensorflow:outfeed marked as finished
INFO:tensorflow:Shutdown TPU system.
INFO:tensorflow:Loss for final step: 2.3149996.
INFO:tensorflow:training_loop marked as finished
<tensorflow_estimator.python.estimator.tpu.tpu_estimator.TPUEstimator at 0x7f6750bd6d30>

然後，呼叫 TPUEstimator.evaluate 以使用評估資料評估模型

estimator.evaluate(_eval_input_fn, steps=1)

INFO:tensorflow:Could not find trained model in model_dir: /tmpfs/tmp/tmpfi5eso9h, running initialization to evaluate.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/util/dispatch.py:1176: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2023-06-09T12:22:09
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:TPU job name worker
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Init TPU system
INFO:tensorflow:Initialized TPU in 4 seconds
INFO:tensorflow:Starting infeed thread controller.
INFO:tensorflow:Starting outfeed thread controller.
INFO:tensorflow:Initialized dataset iterators in 0 seconds
INFO:tensorflow:Enqueue next (1) batch(es) of data to infeed.
INFO:tensorflow:Dequeue next (1) batch(es) of data from outfeed.
INFO:tensorflow:Outfeed finished for iteration (0, 0)
INFO:tensorflow:Evaluation [1/1]
INFO:tensorflow:Stop infeed thread controller
INFO:tensorflow:Shutting down InfeedController thread.
INFO:tensorflow:InfeedController received shutdown signal, stopping.
INFO:tensorflow:Infeed thread finished, shutting down.
INFO:tensorflow:infeed marked as finished
INFO:tensorflow:Stop output thread controller
INFO:tensorflow:Shutting down OutfeedController thread.
INFO:tensorflow:OutfeedController received shutdown signal, stopping.
INFO:tensorflow:Outfeed thread finished, shutting down.
INFO:tensorflow:outfeed marked as finished
INFO:tensorflow:Shutdown TPU system.
INFO:tensorflow:Inference Time : 5.10077s
INFO:tensorflow:Finished evaluation at 2023-06-09-12:22:14
INFO:tensorflow:Saving dict for global step 1: global_step = 1, loss = 5.8631864
INFO:tensorflow:evaluation_loop marked as finished
{'loss': 5.8631864, 'global_step': 1}

TensorFlow 2：使用 Keras Model.fit 和 TPUStrategy 在 TPU 上驅動模型

在 TensorFlow 2 中，若要在 TPU 工作站上進行訓練，請將 tf.distribute.TPUStrategy 與 Keras API 搭配使用，以進行模型定義和訓練/評估。(如需搭配 Keras Model.fit 和自訂訓練迴圈 (搭配 tf.function 和 tf.GradientTape) 進行訓練的更多範例，請參閱使用 TPU 指南。)

由於您需要執行一些初始化工作，才能連線至遠端叢集並初始化 TPU 工作站，因此請先建立 TPUClusterResolver，以提供叢集資訊並連線至叢集。(如要瞭解詳情，請參閱使用 TPU 指南的「TPU 初始化」一節。)

cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
tf.config.experimental_connect_to_cluster(cluster_resolver)
tf.tpu.experimental.initialize_tpu_system(cluster_resolver)
print("All devices: ", tf.config.list_logical_devices('TPU'))

INFO:tensorflow:Deallocate tpu buffers before initializing tpu system.
INFO:tensorflow:Deallocate tpu buffers before initializing tpu system.
INFO:tensorflow:Initializing the TPU system: grpc://10.25.167.66:8470
INFO:tensorflow:Initializing the TPU system: grpc://10.25.167.66:8470
INFO:tensorflow:Finished initializing TPU system.
INFO:tensorflow:Finished initializing TPU system.
All devices:  [LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:0', device_type='TPU'), LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:1', device_type='TPU'), LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:2', device_type='TPU'), LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:3', device_type='TPU'), LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:4', device_type='TPU'), LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:5', device_type='TPU'), LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:6', device_type='TPU'), LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:7', device_type='TPU')]

接著，在您的資料準備就緒後，您將建立 TPUStrategy、定義模型、指標和最佳化工具，並將其納入此策略的範圍。

為了達到與 TPUStrategy 相當的訓練速度，您應確保在 Model.compile 中選取 steps_per_execution 的數字，因為它會指定每次 tf.function 呼叫期間要執行的批次數量，這對效能至關重要。此引數與 TPUEstimator 中使用的 iterations_per_loop 類似。如果您使用的是自訂訓練迴圈，則應確保在 tf.function 化訓練函式中執行多個步驟。如需詳細資訊，請前往使用 TPU 指南的「透過 tf.function 內的多個步驟改善效能」一節。

tf.distribute.TPUStrategy 可以支援有界動態形狀，也就是可以推斷動態形狀計算的上限。但是，與靜態形狀相比，動態形狀可能會產生一些效能負擔。因此，一般建議盡可能讓輸入形狀保持靜態，尤其是在訓練時。傳回動態形狀的一個常見作業是 tf.data.Dataset.batch(batch_size)，因為串流中剩餘的樣本數可能少於批次大小。因此，在 TPU 上訓練時，您應使用 tf.data.Dataset.batch(..., drop_remainder=True) 以獲得最佳訓練效能。

dataset = tf.data.Dataset.from_tensor_slices(
    (features, labels)).shuffle(10).repeat().batch(
        8, drop_remainder=True).prefetch(2)
eval_dataset = tf.data.Dataset.from_tensor_slices(
    (eval_features, eval_labels)).batch(1, drop_remainder=True)

strategy = tf.distribute.TPUStrategy(cluster_resolver)
with strategy.scope():
  model = tf.keras.models.Sequential([tf.keras.layers.Dense(1)])
  optimizer = tf.keras.optimizers.Adagrad(learning_rate=0.05)
  model.compile(optimizer, "mse", steps_per_execution=10)

INFO:tensorflow:Found TPU system:
INFO:tensorflow:Found TPU system:
INFO:tensorflow:*** Num TPU Cores: 8
INFO:tensorflow:*** Num TPU Cores: 8
INFO:tensorflow:*** Num TPU Workers: 1
INFO:tensorflow:*** Num TPU Workers: 1
INFO:tensorflow:*** Num TPU Cores Per Worker: 8
INFO:tensorflow:*** Num TPU Cores Per Worker: 8
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)

如此一來，您就可以使用訓練資料集來訓練模型了

model.fit(dataset, epochs=5, steps_per_epoch=10)

Epoch 1/5
10/10 [==============================] - 1s 117ms/step - loss: 1.6286
Epoch 2/5
10/10 [==============================] - 0s 4ms/step - loss: 0.6109
Epoch 3/5
10/10 [==============================] - 0s 4ms/step - loss: 0.2844
Epoch 4/5
10/10 [==============================] - 0s 4ms/step - loss: 0.1395
Epoch 5/5
10/10 [==============================] - 0s 4ms/step - loss: 0.0699
<keras.callbacks.History at 0x7f66d86adf10>

最後，使用評估資料集評估模型

model.evaluate(eval_dataset, return_dict=True)

1/1 [==============================] - 2s 2s/step - loss: 1.2904
{'loss': 1.2903766632080078}

後續步驟

若要進一步瞭解 TensorFlow 2 中的 TPUStrategy，請參閱下列資源

若要進一步瞭解如何自訂訓練，請參閱

TPU 是 Google 專為機器學習設計的 ASIC，可透過 Google Colab、TPU Research Cloud 和 Cloud TPU 取得。